Abstract
Precision agriculture (PA), also known as “site-specific management (SSM)”, “prescription farming”, or “variable-rate technology”, offers a practical option to address the rising food demand for optimal and sustainable agriculture. Effectively identifying and analysing the spatial and temporal changes in the fields requires the application of agriculture sciences and information technologies. To sustainably conserve natural resources, including water, air, and soil quality, while reducing production costs, various precision agriculture approaches have evolved worldwide. The application of sensor-based technology and remote sensing modifies conventional farming methods. The agricultural industry has grown remarkably due to this modernization of agriculture. This study analyses every technical facet of precision agriculture. It looks at different sensor types and pertinent technology related to precision farming. Precision agriculture and the tools and technologies needed to implement it are also discussed, with an eye on the needs of small-scale farmers in developing countries.
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1 Introduction
About 50% of all households in India rely on agriculture as their primary source of income, making it the country’s most important economic sector. For 2022–23, Agriculture accounts for 15% of the gross domestic product (GDP) of India (Anonymous, 2023). India has made great strides towards achieving food security since independence (Kumar et al., 2018). By 2025, India’s population is projected to increase to 1.5 billion (Kapur, 2018). To meet the consistently increasing food demand for such a large population using conventional farming methods is challenging. Similar to many other industries, agriculture has already adapted to the knowledge-based age to address concerns like declining total production, unethical pesticide and fertilizer usage, depletion of natural resources, and post-harvest losses. A closer examination of the situation reveals that eco-friendly technology must be developed to maximize crop productivity (Mondal & Basu, 2009).
With the advent of information technology in the 1980s, the agricultural revolution officially began, as illustrated in Fig. 1 (Basnet & Bang, 2018). By allowing a sophisticated decision-making process known as precision agriculture, the development of smart wireless sensors and the IoT adds additional feathers to crop production and agricultural hat. The term “precision” began to describe agricultural fields in the 1990s. In principle, precision agriculture (PA) is a promising approach wherein agricultural inputs will be used more efficiently, resulting in increased revenue and production with less harm to the environment. PA is based on the application of cutting-edge information technologies, which involves the characterization and modelling of soil and plant species variety and the integration of agricultural techniques to satisfy site-specific requirements (Dobermann et al., 2002). This system monitors, quantifies, and reacts to crop variability within and between fields. In addition, this system attempts to increase economic returns while decreasing conventional agriculture's energy input and environmental effects (Banu, 2015).
Revolution in agriculture technologies
Serrano et al. (2020) assessed the influence of the tree canopy on the geographical and temporal variability of the soil as well as the productivity, quality, and floristic composition of the pasture using precision agriculture (PA) technology. The findings revealed the following: the positive impact of trees and animal grazing on soil fertility; the impact of inter-annual variability in precipitation on the pattern of pasture vegetative cycle; the positive effect of trees on pasture quality; the negative effect of trees on pasture productivity; and the significance of pasture floristic composition as a measure of soil constraints or climatic changes; the potential of technology linked with the idea of PA as explicit aids to decision-making assistance and for the optimization of the herbaceous stratum and the dynamic management of pasture in this ecological system in an integrated and stable manner.
The interest in PA is increasing globally with the advancements in information technology. Various methods have been developed for precision agriculture based on global positioning system (GPS), geographic information systems (GIS), variable-rate technology (VRT), profitability, yield mapping, etc. (Mondal et al., 2004). Funding prospects for new hardware, software, and consulting sectors relevant to precision agriculture are gradually expanding (Srinivasan, 2001). Punjab, Haryana, and other relatively more developed states in India and other developing nations can be incubators for cutting-edge ideas and technology based on local circumstances (Lang, 1992). When evaluating the use of precision agriculture technologies, it is essential to consider this diversity. This work aims to give researchers a helpful document on the future of real-time technology with intelligent sensors for precision agriculture. The primary objective of this review is to examine the advancements in precision agricultural technology and its application for field crops. Sensors and sensor networks for precision agriculture have been classified and evaluated based on the necessity of precision agriculture in the existing environment. Subsequently, a review of developments in precision agriculture technologies is conducted, including remote sensing (RS), global positioning system GPS, geographical information system (GIS), variable-rate technology (VRT), yield monitoring systems (YMS), and guidance systems (GS). Lastly, precision agriculture's effect on the environment and small landowners’ access to precision farming tools in India has been examined.
2 Necessity of precision agriculture
Currently, there is an urgent need for the application of precision agriculture technologies. The major complications related to agriculture, which necessitate the use of precision agriculture, are as follows (Fig. 2).
Need of precision agriculture technologies
2.1 Decline in crop productivity
Today, the world's population stands at 7.5 billion, increasing from 2.5 billion in 1950, 3.7 billion in 1970, and 6.9 billion in 2010. According to projections from the United Nations, the world's population might reach 9.15 billion by 2050. As a result, we anticipate an average yearly growth of roughly 50 million, with an overall spike of 1.65 billion over the coming 33 years, less than the yearly rise of 80 million between 1970 and 2010. Food production must significantly grow by 2050 to support this greater population (Saha, 2017). Establish self-sufficiency for India in terms of the food supply is the prime concern, which has been exacerbated by the decline in crop productivity caused by long-term climatic changes (Anonymous, 2019), improper application of fertilizers, real-time monitoring of the field parameters (Mondal & Basu, 2009), and the constriction of agricultural land due to high operating costs (Dhas, 2009). To enhance crop outputs using cost-effective alternatives, developing and applying technologies and principles involving regulation of spatio-temporal variability associated with all elements of agricultural practices is imperative. Instead of managing an entire field based on an inaccurate average state, the precision farming strategy considers site-specific changes within fields, maximizing available resources and ensuring crop quality (Bongiovanni & Lowenberg-Deboer, 2004). PA accommodates automation, simplifies data collection and analysis, and enables management decisions, such as the location of the experiment, the quantity of fertilizer or inputs to be applied, and the method of operation that may be concurrently applied. This approach ultimately leads to adequate crop quality, the best utilization of available land resources, and enhanced productivity.
2.2 Excessive use of chemicals
Excessive application of fertilizers contaminates various natural resources, including soil and groundwater. Additionally, ongoing chemical overdoses run the risk of destroying the inherent fertility of the soil (Auernhammer, 2001). Consequently, farmers are advised to use the recommended amount of chemicals. However, insufficient information about the field's growth patterns and soil properties often leads to over- or under-application of fertilizers. The PA-based techniques allow real-time monitoring of the field and crops, rather than relying on the average value and predicting the precise amount of chemicals required over small sub-sections and areas (Kapur, 2018).
2.3 Depreciation of natural resources
Water used in Irrigation operations is an essential component of agriculture, irrespective of scale. Most farming practices depend on groundwater resources to meet the freshwater requirement of crops, resulting in about 85–90% of freshwater consumption. Over time, this has led to a severe depletion of ground water table, indicating a need to reduce water use by employing efficient methods (Neupane & Guo, 2019). Even a 10% reduction in agricultural water consumption can double the current amount of water accessible to the rest of the world. To achieve this, precision agriculture techniques such as micro-irrigation may be applied; delivering the exact amount of water, a plant requires right at the roots with minimal wastage through run-off, evaporation, or stagnation. Subsequently, laser land levellers, which improve the water distribution or coverage by evenly distributing the water in the field, can save water (Aggarwal et al., 2010).
Each plant needs a certain amount of space to grow, and cultivation in restricted areas may lead to competition for soil nutrients. On the other hand, excess land use would lead to wastage of resources. In this regard, precision planting allows the placement of seeds at an adequate distance, ensuring that the roots of each plant receive sufficient nutrients while their shoots have access to enough sunlight. This approach maximizes productivity while making the best use of the available area (Bharatiya & Kale, 2018).
2.4 Uncertainty in farmers
The farmers uncertainty related to their field conditions or recommended input to be used in the field arises the need of precision farming concept. As there are recommended quantities of various farming inputs such as seed rate and fertilizers for farmers to be applied in the field. But they are uncertain for using the recommended values as they are more oriented to the production enhancement rather than the quantity of inputs to be used. The precision farming concept can provide real-time or need-based data for agriculture inputs, which should be applied. The precision agriculture technologies' data and information enable better decision-making and lessen the risk and uncertainty frequently connected with agriculture (Lowenberg-DeBoer, 2003). With the help of precision farming technologies, farmers can have real-time or need-based information about the fields and apply the inputs accordingly without affecting the outcomes.
3 Sensors and sensor networks for precision agriculture
Reliable and affordable real-time sensor systems are indispensable implementing various PA technologies. Sensors allow the accessible collection of enormous amounts of field data, which otherwise is a time-intensive task and often involves laboratory equipment and analysis (Zhang et al., 2002). To measure parameters such as humidity, vegetation, temperature, texture, structure, nutrient level, vapour, and air, various sensors have been utilized. Sensor data is utilized to identify crop species, detect stress conditions, locate and identify pests and weeds, and track drought, soil, and plant conditions (Hakkim et al., 2016). The different types of sensors (Fig. 3) designed to sense various parameters are discussed below.
Classification of sensors for precision agriculture
3.1 Location sensors
Precision agriculture is conceptualized around precise data-collection positioning and thus commonly employs location-based sensors. These sensors can determine latitude, longitude, and altitude within a few feet using signals from GPS (global positioning system) satellites. In order to triangulate a position, at least three satellites are needed. GPS receivers on handheld devices offer positional accuracy of about 100 m. Using differential GPS (DGPS), this error is reduced to 2 m (Adamchuk et al., 2004). Further, GPS with relative positioning reduces the inaccuracy to a sub-centimetre level, while a real-time kinematic (RTK) GPS can keep this precision for moving vehicles.
Various studies have reported using satellite-generated data for agricultural field studies (Sishodia et al., 2020). Although a few investigations concentrated on a single application area, such as the determination of soil properties (Ge et al., 2011), the estimation of evapotranspiration (ET) (Courault et al., 2005; Maes & Steppe, 2012); and the administration and control of disease and pests (Zhang et al., 2019), some included multiple application regions (Atzberger, 2013; Mulla, 2013; Weiss et al., 2020). Dux et al. (1999) created field maps during field scouting using a geo-referenced audio recorder with speech recognition. This technique assists users in monitoring weed development, disease, and crop growth in the field. Furthermore, a data gathering system was created by Yule et al. (1999) to enable efficient recording of the performance of an agricultural tractor in the field. In order to analyse field variability in topology, soil type, and soil moisture content and to pinpoint locations in the field that require corrective action, performance parameters were mapped during field activities. Most farmers in economically developed countries can now afford commercial sensors that receive and interpret GPS signals, but small-scale farmers in poorer nations have a difficult uphill battle (Triantafyllou et al., 2019). It may be the reason for less adoption of sensors-based technologies in underdeveloped countries.
3.2 Optical sensors
Optical sensors such as GreenSeeker, Yara N-sensor, and SPAD (Soil Plant Analysis Development) meter use light to gauge the properties of soil by monitoring a range of light frequency reflectance in the near-infrared, mid-infrared, and polarized light spectrums. Such sensors are useful for various agricultural management tasks, including weed detection and soil fertility determination, by measuring the amount of organic matter, moisture content, and other factors in the soil (Viana et al., 2019).
Stafford and Bolam (1998) mapped vegetative indices using a near-ground scanning radiometer placed on a tractor. Thai et al. (1999) employed a field spectral-imaging system with an adjustable liquid crystal filter in cotton and peanut fields. At a ground speed of 3.6 km h−1, an online, real-time soil spectrophotometer detects the spectral reflectance of the soil in the visible and NIR wavebands. During field testing, linear correlations were reported between the reflectance at particular wavelengths and other soil characteristics, such as moisture and organic matter levels (Shibusawa et al., 2000). Solie et al. (2000) estimated yield during cropping seasons by using variable-rate technology (VRT) to estimate nitrogen fertilizer requirements through an optical sensor that measured spectral brightness in the red and NIR wavebands. An infrared plant-temperature transducer was developed by Michels et al. (2000) to detect temperature variations in plants caused by water stress. To map water, nutrition, disease, and salinity stresses in plants, Anom et al. (2000) developed an online, real-time spectrophotometer. It was predicted that advancements in crop plant gene editing may make it even easier to distinguish between these different types of stress (Stafford, 2000). In order to estimate the organic matter and moisture contents of surface and subsurface soils, Hummel et al. (2001) used a near-infrared (NIR) soil sensor to measure the spectral reflectance of the soil in the range of 1600–2600 nm.
Sharma et al. (2012) studied the tractor-mounted optical N-sensor remote sensing system that generates sensor biomass data by processing real-time observations with an onboard computer. Using its geometry, the N-sensor, which was mounted on the roof of the tractor at a height of 2.74 m, scanned the 46.72 m2 cropped area. It comprises two diode spectrometers with viewing angles of 58 and 70 degrees, fibre optics, and a CPU enclosed in a hard shell. The N-sensor was used to monitor nitrogen levels in five wheat crop plots with varying nitrogen contents (0, 40, 80, 120, and 160 kgN ha−1), and it was concluded that the N-sensor data showed strong connections with nitrogen uptake at the 35 growth stage (about 90 DS) on wheat crop. From the studies reviewed, it may be concluded that high spatio-temporal resolution and real-time data transfer enable the depiction of in-field fluctuations, making sensor technology highly advantageous and increasing its contribution to crop production.
For a commercial 42-ha orchard in Spain, González-Dugo et al. (2013) assessed the utility of thermal imaging from a thermographic camera for irrigation management by employing it as a water stress indicator. The findings demonstrated the value of thermal imaging as a tool for appropriately scheduling orchard irrigation. By assessing the health of soil and plants, Cao et al. (2013) examined the viability of the GreenSeeker and Minolta SPAD-502 to suggest nitrogen fertilizer. In order to calculate the amount of required nitrogen fertilization for the crop by the desired productivity, GreenSeeker measures the Normalized Difference Vegetation Index (NDVI), and Minolta SPAD-502 uses SPAD analysis of soil plants to calculate the amount of chlorophyll content in plants (optical sensor). Another sensor used for nitrogen fertilizer is the Yara N-Sensor. It is based on spectral reflection in certain bands connected to the culture biomass and chlorophyll content (Kumar et al., 2018).
3.3 Electrical sensors
Electrical sensors are used to assess the propensity of a material to conduct electricity. Electrical circuit behaviour is influenced by variations in the physical and chemical properties of soil, which are quantified in terms of changing electric parameters. Electrical sensors are now the most practical methods for mobile soil mapping owing to their quick response time, low cost, and excellent durability and the soil texture, salinity, organic matter, moisture content, and other soil properties are correlated with the soil maps that were obtained (Adamchuk et al., 2004). There are many types of electrical sensors, such as electro-chemical, electro-magnetic, and electro-mechanical or mechanical. Various electrical sensors are examined in the following sub-sections, and their present status and potential future applications are discussed.
3.3.1 Electro-chemical sensors
The spatial heterogeneity of various soil chemical characteristics can be evaluated either directly or indirectly via electrochemical sensors. Electro-chemical sensors use of ion-selective membranes, which provide a voltage output in response to the activity of particular ions (such as H+, K+, NO3−, and Na+). These ions present in the soil are detected using sensor electrodes. Such sensors provide details about the pH and soil nutrient levels, which is crucial for precision farming. Data regarding soil chemical composition can collected, processed, and mapped using sensors mounted on specially made sleds. Most electro-chemical ion detection systems employ an electrochemical transducer to convert reactions into potentiometric readout signals and an ion-selective membrane (ISM) containing specific ionophores that exclusively react with target ions. This sensor's reference electrode maintains a steady voltage while the active electrode detects a particular target ion (Yin et al., 2020). Ayranci & Ak (2019) created a poly (2, 5-dithienylpyrrole)-detecting electrode-based pyrene-substituted electrochemical sensor with a limit of detection of 1.73 × 10−7 M for Fe2+/Fe3+ ions. The use of a electrochemically produced rhodamine-based conductive polymer created a solid-state sensor platform with a small limit of detection (3.16 × 10−8 M). Ali et al. (2020) developed a deployable electrochemical soil nitrate sensor by coating a printed circuit board with a nanocomposite of poly (3-octyl-thiophene) and molybdenum disulphide (MoS2). This membrane-based, ion-selective sensor is susceptible to 1–1500 ppm and can detect nitrate-nitrogen in soil slurries. Developing low-cost nitrate sensors that can monitor nitrates and other soil nutrients in solution has become the focus of various developers.
Furthermore, a soil electrical conductivity (EC) sensor based on a four-electrode approach has identified several yield-limiting factors associated with non-saline soils (Lund et al., 2000). The subsurface of the soil can be mapped using a soil EC probe and an automated penetrometer (Drummond et al., 2000). In order to monitor the EC of soil at three depths, a sensor with eight rolling electrodes was developed in France (Dabas et al., 2000). Plant-available-water-holding capacity and the EC, as assessed before planting, can be connected (Morgan et al., 2000). Overall, electrochemical sensors have been widely used to detect a range of ions present in the soil and to deduce various crop properties and dynamics.
3.3.2 Electro-magnetic sensors
The magnetic behaviour of various soil properties is investigated by electro-magnetic sensors. Since a single parameter cannot accurately predict multiple soil qualities simultaneously, multiple measurement approaches are used. Fan et al. (2001) attempted to test both the capacitive and conductive characteristics of soil. Measuring capacitance or the dielectric constant is a particularly efficient method to assess the moisture content of soil, as the dielectric constant of water is an order of magnitude greater than that of soil (Starr & Paltineanu, 2002). The dielectric soil moisture sensor is a standard electromagnetic sensor that measures the dielectric constant of the soil to assess water content. Throughout a farm, moisture sensors may be employed in conjunction with rain gauge stations to enable monitoring of the soil moisture levels, even in absence of vegetation.
Air flow sensors are another class of electromagnetic sensors used to gauge many aspects of soil, such as compaction, structure, soil type, and moisture content. Such sensors determine the permeability of the soil and respond with the amount of pressure needed to force a specific volume of air into the ground at a specific depth. Measurements may be recorded statically while stationary or dynamically while moving. Further, an electromagnetic-induction-based Soil EC sensor is a non-contact sensor based on the soil productivity index. This index incorporates the effects of bulk density, water-holding capacity, salt, pH, and EC determined using this sensor with significant correlation (Myers et al., 2000). A contour map is created using a ground-penetrating radar to show the location of clay lenses, which control the amount and direction of groundwater circulation (Dulaney et al., 2000).
3.3.3 Electro-mechanical sensors
Electro-mechanical sensors are applied in various agriculture related applications. It can assess the “mechanical resistance” of the soil or soil compaction. Such sensors use load cells or strain gauges to monitor resistive forces using a probe that extends far into the ground. It can measure the force applied by the roots to absorb water, and this principle is employed in tensiometers (e.g. Honeywell FSG15N1A) which is a useful tool for irrigation interventions (Padhiary & Mishra, 2020). The electromechanical sensor was created and evaluated by Sudduth et al. (2000) to count corn plants as they approached the collecting chains of a combine header. When compared to manual counts during harvest, the sensors marginally underestimated the actual population. When the combine header was operated near to the ground level and at speeds no faster than 2 m s−1, errors were reduced. Sensor assessment in maize sown at different rates demonstrated an increasing underestimate error as population increased. This inaccuracy was proportional to the pace at which maize stalks passed through the sensor. After correction, the felt population was a good predictor of the actual, hand-counted population (r2 = 0.93, zero mean error).
A cotton mass-flow and strength sensor was made using a halogen lamp and a NIR light (Keskin et al., 1999). A capacitance sensor, a sensor measuring the power required at the PTO shaft, a microwave sensor, and an NIR sensor were all examined to ascertain the moisture content of fodder (Marcotte et al., 1999). Ahmad et al. (1999) used a DGPS in conjunction with a chlorophyll metre (SPAD-502) to map nitrogen stress in corn. Rial and Han (1999) studied the performance of a commercial complex permittivity sensor to measure ionic nutrients. Furthermore, soil penetration resistance, moisture content, and organic matter were assessed using a penetrometer with a near-infrared reflectance sensor (Newman et al., 1999). An infrared thermometer was used to measure the canopy's temperature to control irrigation practices (Evans et al., 2000). To gauge soil hardpan, a penetrometer and a soil EC sensor have been utilized (Clark et al., 2000). Different physical principles were used to build soil moisture-content sensors, including time-domain reflectivity (TDR), standing-wave ratio (Sun et al., 1999), and laser light depolarization (Zhang et al., 2000). Micro-electro-mechanical system (MEMS) sensors paired with electrochemical sensing units are gaining popularity for soil quality assessment due to its portability, field deployment capability, and real-time and rapid detection. Mechanical fingers and infrared light beams were used to gauge cotton plant height (Searcy & Beck, 2000).
A technique that evaluates soil resistivity to a depth of 30 cm (three measurements at once) was created by Adamchuk et al. (2001). Soil resistance maps were created by georeferencing these findings using a global positioning system. An enhanced technique that evaluates soil resistivity to a depth of 63 cm (eight measurements at once) was created by Andrade et al. (2001). Hanquet et al. (2004) used an on-the-go mechanical sensor to investigate the fluctuation of soil strength in an agricultural field. Two field zones divided up based on soil strength were validated by the maps of soil strength produced by their analysis. These details can be used in conjunction with mapping, demonstrating the variability of soil nutrients and other significant crop-soil characteristics to identify variables that influence or restrict production.
The benefit of electrical sensors is that they can quickly and non-destructively quantify soil variability, enabling accurate soil nutrient management and monitoring. In order to enable precise soil nutrient management and monitoring, real-time mapping of significant soil chemical and physical characteristics is a prospective application for electrochemical, electromagnetic, and electromechanical technologies. However, it is yet unknown if electrical sensor fusion will be able to simultaneously quantify spatial variability of significant crop-soil parameters under various growth situations. The various electrical sensor types and their uses are listed in Table 1.
4 Technologies for precision agriculture
4.1 Remote sensing (RS)
Breeders frequently utilize RS to evaluate soil characteristics and crop growth. By giving accelerated access to agricultural attributes in comparison with conventional field measurements, this technique has demonstrated considerable potential. In this approach, the amount of electromagnetic energy reflected from an object is measured using the remote sensing technique of spectral reflectance curve. A wide variety of wavelengths in the electromagnetic spectrum, ranging from visible to microwave, enable the application of numerous sophisticated technological interventions, including light detection and ranging (LiDAR), fluorescence spectroscopy, and heat spectroscopy (Mulla, 2013). Earlier, sensors were placed inside the ground to measure soil organic matter. Such ground-based sensors, such as pH, humidity, and temperature sensors, are placed in the field to collect data, which is later sent remotely to bases for analysis. However, agronomists now regularly use various additional remote sensing technologies, including satellite-based sensors, portable sensors, and tractor-mounted sensors. By combining the data from ground sensors, farmers can make better decisions, increase agricultural output, and maximize the use of available resources.
Weeds in crops can be located and mapped using remote sensing technologies that collect data from sensors on satellites or unmanned air vehicles (UAV) (Triantafyllou et al., 2019). With technological improvements, affordable drones are now readily available, which can extract relevant information from the photographs captured by the sensor (Barbedo, 2019; Nex et al., 2022). Reflective indicators have been studied to evaluate vegetation-related biophysical factors (Xue & Su, 2017). Mini and micro-UAVs with inexpensive and lightweight sensors were introduced by Nebiker et al. (2008) for use in remote sensing applications in agronomical research. Later, the Leaf Area Index (LAI) of the canopy was estimated (Mathews & Jensen, 2013) using this UAV technology.
Remote sensing technology is a precious instrument for acquiring a large amount of information simultaneously (Ryu et al., 2011), and it aids in obtaining data from a distance. Data sensors might be handheld devices, aircraft-mounted, or satellite-based. Remote sensing data may be used to assess crop health (Mandal & Maity, 2013). Plant stress caused by moisture, nutrients, compaction, crop diseases, and other plant health issues is frequently visible in overhead photographs. Remote sensing may show in-season variability that influences agricultural productivity and can be used to make management changes that increase present crop profitability. Although remote sensing technology collects data, it is difficult to pinpoint the essential management component since each field has different variables, such as the time and duration of midseason drainage, the timing and quantity of nitrogen fertilizer application, and the timing of harvest.
4.2 Global positioning system (GPS)
Global positioning system (GPS) serves as the principal navigation system, which allows a receiver to record real-time data, along with several parameters, such as speed of machine or vehicle, altitude, the area covered, and soil sampling location. These data can be gathered/obtained, stored, and analysed for productive and efficient farming techniques and operations. Several precision farming applications use a ZigBee-based GPS receiver coupled with the controller area network (CAN) for quick and wireless communication (Unal, 2020). This system uses wireless communication to transmit ZigBee signals to the CAN Bus, avoiding cabling costs while offering dynamic, adaptable, and useful communication. Integrating GPS with Green-seeker aids crop health monitoring and helps determine the required amount of fertilizer to be applied in the field (Sharma, 2018). Furthermore, furrows may be placed with an accuracy of 1 mm in a field using the autonomous steering system connected to the GPS receiver before seed sowing to save time and prevent seed waste. GPS receivers and yield monitors also create yield maps (Shannon et al., 2002). Additionally, portable GPS facilitates quick mapping of insect infestations for efficient pest-management and observations and records the results for later use. GPS is used extensively, including field mapping, fertilizer application, and crop production monitoring. With the expansion of field applications, GPS technology has given a new direction to India's precision farming system (Dziuk, 2021; Gale, 2018).
GPS has been commercially used for surveying of farms having diverse location and size. Optimal positioning information of sensors and other spatial information permits a significant reduction in fertilizer, pesticides, and other environmentally hazardous chemicals. The field area obtained via real-time investigation of field through GPS was 5–7% less/more than the actual value, indicating significant accuracy (Jatin et al., 2012). Swain and Singha (2018) used a handheld GPS receiver to map the Palli-Siksha Bhavana (PSB) Agricultural Farm in Sriniketan, West Bengal, while gathering data on crop practises and secondary study areas. The crop area was concealed from other land uses during the digitizing and mapping processes in the Arc View software environment. Additionally, the different cropping patterns were noted, and a database was created for record-keeping and decision-making in relation to enhanced production. The different areas of agriculture where GPS can be applied and are depicted in Table 2.
4.3 Geographic information system (GIS)
GIS is a software-aided technology that collects feature data and geographic coordinates to create an information map. The GIS hardware features a central processing unit (CPU) for capturing and processing photographs to gain layers of information, including soil moisture, nutrient levels, and yield information. Compared to conventional maps, computer-generated GIS maps offer more data about yield, soil surveys, rainfall, and crops. This technology examines characteristics and geography using statistics and spatial methodologies (Runquist et al., 2001). Application of GIS on the field level provides data on crop yield, irrigation, and soil testing.
To meet the urgent need for PA applications, a variety of GIS programmes, including ArcView, IDRISI, SURFER, and software packages provided by John Deere Precision Farming Group, Agri-Logic, and Rockwell International are available (Ess et al., 1997). Applications such as crop health assessment and yield forecasting utilize images obtained from hyperspectral sensors (Deguise & McNairn, 2000). The wine vigour of a vineyard has been mapped using the Normalized Vegetation Index (NDVI) generated from a multispectral camera image (Matese et al., 2013). Biomass and nitrogen content are estimated using the hyperspectral sensor installed on the unmanned air vehicle (Pölönen et al., 2013). A relatively new technology called hyper-spectral sensing can gather data throughout a nearly continuous spectrum in the visible, NIR, and MIR wavebands (Sahoo et al., 2015).
4.4 Grid soil sampling
To create a representative map that shows changes in soil fertility, soil sampling is a requirement. Grid soil sampling is a method for determining the variability in the soil texture, nutrient level, moisture content, or other soil parameters on a grid basis. In this method, the entire field is divided into grids, and for each grid, more samples are gathered at regular intervals than in a standard sampling method. Representative samples collected from each grid are then analysed in a lab to determine agricultural inputs like fertilizer and water for each grid. Thereafter, an application map of the field under observation (complete with location coordinates) is produced. For the input (water/nutrient) application, this map is loaded on a tractor-mounted computer system connected to a variable rate applicator and a GPS receiver. According to Ferguson and Hergert (2009), maps created by utilizing dense and optimal grid sampling can provide farmers with valuable information on soil fertility for many years.
According to Mishra et al. (2019), the grid technique performs best when big tracts of land available. The most common grid sizes utilized are 2-acre grids, though these shapes and sizes can be changed to fit the requirement and convenience. At the locations where intensive sampling is required, even one-acre grids are used. As a result, the field will be divided into equal square-shaped regions by these fixed-area grids, from which samples will be taken. These square-shaped spaces are also known as cells. Three feasible techniques for gathering samples within a grid have been described by Mishra et al. (2019): (1) starting at the centre of the grid with the GPS unit and moving outwards in multiple directions to collect samples from three to five sites at random and consolidate them (Fig. 4a). This grid-centred method can be applied consistently to any field because it is extremely easy. However, caution should be exercised to prevent the concentration of samples towards the centre point to ensure unbiased sampling. (2) Random selection of samples from all points on the grid, regardless of the grid centre (Fig. 4b). Although the sampling pattern is not uniform throughout the cells, this method will guarantee improved randomness. However, due to the need to access multiple sampling locations individually spread out over the grid area, this method could take longer. (3) Collection of samples at the intersections of grid lines (Fig. 4c). This approach will mathematically integrate the values (interpolate) between the points and enable the creation of contour maps based on the soil nutrient levels. The smaller the grid area chosen, the higher the sampling intensity thus is increasing the costs.
a Grid centred soil sampling; b random sampling within grids; c sampling at the grid intersections
Even though grid sampling is a promising technique, it has various shortcomings. First, increasing the number of soil samples cannot ensure variety capture independently. Second, different interpolation methods affect the data by averaging the effects of outliers and/or extreme peaks, leading to inaccurate suggestions.
4.5 Variable-rate technology (VRT)
Variable-rate technology is a key component of precision agriculture in which inputs are administered based on crop needs using GPS, GIS, remote sensing, and plant/soil analysis. Sensors, automated controllers, and so on. Using field variability, inputs are applied at the appropriate time, right place, right quantity, and right way in VRT. Variability (spatial/temporal) should be present in the field for VRT implementation. Variable rate strategies cannot be used without variability; nevertheless, variability is always present in farmers' fields, which expands the scope of these techniques (Chaudhary et al., 2020). VRT is classified into two types: 1. Map-based control: Prior to the operation, a map of application rates is created for the field. 2. Real-time control: Using information obtained throughout the process, choices on what rates to apply in different areas are made in real time. This necessitates using sensors to detect vital information while on the move and is often tailored for a specific purpose, such as herbicide spraying. The variable rate applicator is made up of three parts. Control computers, locators, and actuators are examples of these. A computer installed on a variable-rate applicator loads the application map (Meena & Dudwal, 2021).
A real-time visible (VIS) and near-infrared (NIR) soil sensor-based variable rate fertilization system was developed for maize crops (Maleki et al., 2008). To identify the phosphorus (P) deficit in a plant, the number of leaves and crop yield were used as growth indices. The sensor with a range of 305–1711 nm was mounted at the front of the applicator system to forecast the soil-extractable phosphorous. The LabVIEW programme was then used to obtain the soil spectrum for the scanned field. Thereafter, the application rate of phosphorus was adjusted based on the estimated deficit. The application rate was updated every 4 m2 of the field crossing or scanning area. When plots with uniform rate (UR) and variable rate (VR) treatments were evaluated, the average amount of phosphate (30 kg ha−1) applied on UR plots was 1.25 kg more than the average amount (28.75 kg ha−1) applied on VR plots. Additionally, less variance (25%) in the number of plant leaves was seen in VR-treated field plots, indicating a more even P distribution. Furthermore, VR-treated plots displayed significantly higher yields (336 kg ha−1) and lower variability.
Koundal et al. (2012) selected various components such as pulse width modulation (PWM) Raven Envizio Pro Controller, hydraulic motor, and zero till seed cum fertilizer drill for developing a Variable Rate Applicator (VRA) and evaluate its performance. The investigation revealed that the range of fertilizer rates was 25.53–237.93 kg ha−1, with variations in E-Pro II controller adjustment from 1000 to 4000 and matching variations in hydraulic motor speed from 70 to 265 rpm. In order to identify real-time N deficit in the field and to apply the required amount of fertilizer, Mirzakhaninafchi et al. (2021) designed and developed a variable rate fertilizer applicator. In this technique, the pulse-width-modulation valve was designed to receive a signal from the N sensor and transmit it to the microcontroller system, which would then change the rotational speed of the hydraulic motor to rotate the drive shaft of the metering mechanism according to the recommended amount of fertilizer. Between the N sensing and fertilizer discharge, the response time ranged from 3.49 to 4.90 s. Fertilizer was delivered at a rate of 0.00 kg ha−1 rather than 78.36 kg ha−1 using the designed machine, resulting in an increase in NDVI of 0.56–0.78 and a drop in drive shaft rotational speed of 20–0 rpm. This method could reduce the environmental impact of various agricultural practices, improving the sustainability of farming.
Another variable-rate fertilizer application for rice (Oryza sativa L.) was recently evaluated by Mirzakhaninafchi et al. (2022). A nitrogen fertilizer savings of 45% as compared to recommended dose was achieved by using the developed applicator. The study suggested that if applied on a larger scale, variable-rate technology for fertilizer application could benefit farmers, the environment, and consumers. Environmentally, the reduction in fertilizer use decreases pollution, lessening the burden of farming on the surrounding ecosystem.
Variable rate applicator (VRA) devices and their essential features and applications have been described by Grisso et al. (2011). Depending on the method used for acquiring information, VR applicators are categorized into two different types: sensor-based and map-based. Sensor-based applicators carry out simultaneous assessments of the soil and application of chemicals. The first step in this method involves mapping the soil spectra. Sensor-based systems typically omit complicated processing methods. Based on the application, VRA is classified as seeding VRA and weed control VRA. The commercially available on-the-go seeding VRA was developed by Trimble Inc., which includes a ground speed sensor to assess the soil before seeding, a processor to synchronize the planter width and control the seeding pace, and a hydraulic drive unit to control the speed of the seed meter shaft.
The weed-control VRA are further classified into three types on the basis of the mechanism used to control the application: flow-based control, chemical-injection-based control (with a subset or with carrier), and modulated spraying-nozzle control system. Further, VRA is also categorized based on the parameter to be measured, such as nitrogen and phosphorus VRAs for monitoring and enhancing soil fertility and lime VRAs for managing soil pH (da Costa Lima & Mendes, 2020) Of the 108 studies conducted by Lambert and Lowenberg-DeBoer (2000), 63% indicated improved net returns with a specific precision farming technology, while 50% indicated positive net returns with all precision farming technologies, except yield monitoring. The exact variation of technologies to be applied depends on site-specific requirements; however, knowledge of diverse applications will enable the farmers to make informed decisions about the suitability of each technology.
4.6 Yield monitoring and mapping
The yield monitoring technology was initially developed in the early 1990s and is now become the most common method in precision farming. In 2019, the yield monitoring market segment had already surpassed other competitive precision farming technologies in market share. From 2014 to 2020, it has grown at a compound annual growth rate (CAGR) of 7.17%, with market share value reaching $2.51 billion. Currently, yield monitoring systems have been developed for cotton (Andrade-Sanchez & Haun, 2013), sugarcane (Magalhes & Cerri, 2007), grains (corn, wheat, oats, soybeans; Franzen & Humburg, 2016), and soybeans.
When a crop is being harvested, the geo-referenced data on grain yield and numerous other variables, such as moisture content, grain mass, and harvested area, are collected load-wise or field-wise. This collection of yield maps for at least 5 years is crucial to prevent erroneous observations caused by unfavourable weather conditions or unforeseen circumstances during a specific year. The yield maps are then averaged to aid farmers in understanding yield fluctuation within a field through data interpretation. By incorporating ground and remotely sensed observations data into a World Food Studies (WOFOST) crop simulation model, Tripathy et al. (2013) predicted the wheat yield in Punjab for 2004–05, 2006–07 and 2008–09. WOFOST could estimate crop growth by observing basic crop processes like photosynthesis and respiration. To this end, wheat crop map from multi-date Indian Remote Sensing Satellite (IRS)-Advanced Wide Field Sensor (AWiFS) data and Time-series normalized difference vegetation index (NDVI) data product from SPOT-VGT obtained through remote sensing were provided as inputs. The soil, crop, and meteorological data collection were recorded as ground observations. The field area used to generate the spatial data was divided into grids with cells 25 km2 in area. Before simulation, the model was validated using data from 2003 to 04 after being calibrated for the wheat variety PBW-343 for 2002–03. The actual proportion of wheat received from the map of the remotely sensed wheat crop was used to simulate the wheat grain yield for each grid with a variation of less than 5%. To verify the accuracy of this predicted data, the actual production at the district level, stated by the Department of Agriculture, Govt. of India, for each of the three consecutive crop seasons was, compared with the estimated yield. A spatial yield at the state level with a root mean square error of less than 0.4 tonnes·ha−1 was predicted. Similarly, for mapping and tracking the cultivation of rice at three select locations in Tamil Nadu, India, Pazhanivelan et al. (2015) employed multi-temporal, high-resolution (3 m) synthetic aperture radar (SAR) imageries from COSMO Skymed and TerraSAR X and site-specific characteristics. For the estimation of yield, the seasonal rice area, Start of season (SoS) maps, and backscatter time series were employed. Rice classification and accuracy evaluation from field observations were used to create SoS maps with an accuracy range of (87–92%). At the block level and 87% at the district level, the yield simulation accuracy standards were attained.
Singh et al. (2011a, 2011b) developed a batch-type yield monitor for grain combines to measure the spatial variation of grains. It has a load cell with a capacity of 700 kg and a drum size of 125 cm × 85 cm × 80 cm. It may be used as a single unit or installed directly in a trailer. A 145 cm × 100 cm × 85 cm auxiliary tank with a load cell installed at the bottom was installed in the main tank to create a combine mounted batch-type yield monitor. A display unit close to the driver's seat displays the yield statistics. Evaluation of batch-type yield monitor resulted in a yield variability with a coefficient of variation (CVs) of 5.46%, 27.56%, and 35.34% at three separate locations. Such systems may be used for any crop or any combine, and are inexpensive and helpful for small and marginal areas.
4.7 Sensors for agriculture weather stations
Throughout agricultural fields, self-contained agricultural weather stations are positioned in various spots. These stations use a combination of sensors to collect data on air temperature, soil temperature at various depths, rainfall, leaf wetness, chlorophyll, wind speed, dew point temperature, wind direction, relative humidity, solar radiation, and atmospheric pressure. These measurements are recorded at predetermined intervals based on the local climate and the sown crop. The gathered data is subsequently transmitted wirelessly at predetermined intervals to a central data logger. Such weather stations are appealing to farms of all sizes because of their portability and falling costs (Triantafyllou et al., 2019). The concept of integration of low-cost smart sensors and transducers based on IEEE 1451 for physical interfacing, ICT-enabled agricultural machinery based on ISO 11783 for controlling, and an energy-efficient wireless communication network (6LoWPAN) for device communication was first introduced by Gangwar and Tyagi (2016). The combination carried out automatic sensing, collaboration, synthesis, and application of the data to create a full package for agro-economical applications. By raising awareness of rural resources and their integrated use with cutting-edge technology devices, the Himalayan Environmental Studies and Conservation Organization (HESCO) is taking action to protect the environment, and enhancing the economic well-being of farmers in the Himalayan region (Anonymous, 2018). The company has installed a Dynalab weather station with an automatic data logger system to gather information from 8 different application-specific sensors, including wind speed, wind direction, wind vane, air temperature, platinum resistance, relative humidity solid state capacitive, atmospheric pressure bellows connected strain gauge, rain gauge tipping bucket, and solar radiation, before sending the information to a specific mobile number via global system for mobile communication (GSM) module. An IoT-based, low-cost, real-time, all-weather monitoring system for precision agriculture in India was created by Math and Dharwadkar (2018). A microcontroller called the ESP32 processed the data that was gathered by the rain sensor (YL-83 + LM393), air temperature and humidity sensor (DHT22), atmospheric pressure sensor (BMP180), and LDR-based intensity sensor. The data were analysed and displayed for specific agricultural activities using Thing Speak, a Matlab-based programme.
4.8 Weedseeker sensor
The Weedseeker sensor detects the presence of weed and instructs a spray nozzle to release a precise amount of chemical, ensuring that the weed is specifically sprayed on and not bare ground. Such sensors work best in locations with sporadic weed growth. An intelligent sensing and spraying system was developed by Tian et al. (2000) to accurately identify weed-infested zones. A weed sensor was developed by Feyaerts et al. (1998) utilizing an imaging spectrograph. Based on the research on the spectral properties of weeds, crops, and soil, Wang et al. (2001) developed an optical weed sensor. Plant temperature fluctuations brought on by a green bug infestation were detected using an infrared plant-temperature transducer created by Michels et al. (2000). Besides these, numerous weed sensors are commercially available. For instance, Trimble has developed a dual-wavelength, LED-based optical sensor to identify weeds in fields. Its foundation is the idea of light reflecting at two different wavelengths (soil and plant).
For the targeted delivery of herbicides, Trimble’s weed seeker spot spray system uses an optical approach based on an internal dual-wavelength LED light source. The presence of chlorophyll results in a specific reflectance profile, and the sensor compares the reflected light from soils and plants at two different wavelengths. The system, generally mounted on a spray boom, detects the presence of a plant (weed) by detecting the chlorophyll signature and activating a quick solenoid valve that sprays the herbicide onto the plant rather than the naked ground.
4.9 Guidance systems
Self-propelled equipment, such as tractors with implements, combine harvesters, and sprayers, are indispensable for modern agriculture. With additional engine power, these machines enable faster speeds, wider working areas, and better performance. However, drivers of such equipment are frequently required to lead the operations over long days and night, in haze and dust, and on slippery soil surfaces. Therefore, any automation focused on guidance is beneficial to reduce the overall workload on humans and to enable consistent steering accuracy during a complete workday. An automatic guiding system that can track a moving vehicle within a range of 30 cm may replace the conventional equipment markers for spraying or sowing (Goddard, 1997). The functioning of farm-level systems usually depends on the working conditions. The advancement in communication and sensor technologies has motivated using navigation-based guidance systems in agriculture. Navigation system-assisted agricultural vehicles have reduced the requisite amount of effort from the operator, decreasing work arduousness (Thuilot et al., 2002) and the probability of fatigue. Navigation systems have been associated with agricultural vehicles to determine a suitable trajectory for the operators (Yao et al., 2005). Satellite navigation systems have replaced drill disk markers for making parallel swaths across a field. Consequently, the operators can significantly reduce skips and overlaps, which frequently occur while using conventional methods reliant on visual estimation of swath distance and/or counting rows (Grisso et al., 2009). Besides the reduction of labour intensity and enhancement of efficiency, such systems enable the farmers to work during low visibility field conditions such as dust, fog, and darkness, increasing the precision of tasks (Grisso et al., 2009; Thuilot et al., 2002). To summarize, navigation systems implemented in agricultural practices provide many advantages like saving fuel and operation time, ease of operation, reduction in production losses, and saving resources leading to an overall increase in field efficiency (Hamada et al., 2009). The major advantage of navigation systems (global positioning system and Global Navigation Satellite System) for agricultural vehicles is to provide absolute position measurement along with the generation of an ideal reference path. Navigation systems provide direct instructions to the tractor operator regarding the requisite extent and direction of steering wheel movement to follow a predefined trajectory (Santana-Fernández et al., 2010).
Singh et al. (2021) examined the relative performance of two commercially available navigation systems—the LED light bar guidance system and the offset value guidance system—installed on tractors for tillage operations to those of tractors driven without navigator aid. The current study demonstrates the improvement in the performance of implements with navigator-assisted tillage operations. According to the study, comparative evaluations of two operators clearly show that the use of navigators leads to a significant reduction in fuel waste by reducing overlapping areas and significantly increases the area of soil pulverization by minimizing the missed area. The study also suggested that the overall performance can be further enhanced by increasing the operators’ understanding of the navigator and directions for its use.
A nine-tyne tractor-operated cultivator and spinner-style fertilizer spreader were assessed by Magar et al. (2014). In order to monitor the missing area, overlapping area, and actual productivity of the machines, the tractor was operated both with and without a navigator. In the cases where the cultivator was assisted by a navigator, the proportion of missing space decreased to 8.5%, as opposed to a missed space fraction of 23.5% of the total cultivated area when navigator was absent. Missed areas for smaller fields under trials were 4.2 and 14%, respectively, of the total area of 0.48 ha. However, at a wider area of 1.62 hectares, the average percentage of missed are in the absence and presence of navigator trials, respectively, was 19.8 and 5.5%. The overlapping area during cultivation without a navigator was found to be 0.042 ha, or 18%, as opposed to 0.006 ha, or 3%, by utilizing a navigator. For smaller fields, it was shown that areas of overlap during fertilizer distribution for experiments without and with navigators were 0.066 ha or 13.75% and 0.002 ha or 0.4%, respectively. However, on a more extensive field, 0.027 ha, or 1.7% of the total area of 1.62 ha, was found without navigator trial overlap, while 0.048 ha, or 2.9%, was observed with navigator trial overlap. In comparison with the actual production of the cultivator with the navigator, which was 0.75 ha h−1 with 8.5% missing area, the operation without the navigator produced 0.53 ha h−1 with 23.5% missing area. The actual productivity of the satellite-guided cultivator was 1.42 times more than the actual output of the cultivator when operated without the navigator. The real productivity of the fertilizer spreader operated with the navigator was 1.64 times higher than that of the unit run without the navigator. Fertilizer spreaders with satellite navigation were roughly 1.37 times more productive on larger fields than spreaders without navigation.
The use of navigators prominently enhances the area of soil pulverization by minimizing missing regions while significantly decreasing fuel consumption by eliminating overlapping areas. By improving the operators' comprehension of the navigation and its instructions, the entire performance may be improved even further. A comprehensive view of various precision agriculture technologies is shown in Fig. 5.
Various precision agriculture technologies
5 Environmental effects of precision agriculture
It has long been assumed that precision agriculture will benefit the environment. A wide range of environmental benefits are provided by a more focused application of inputs with less fertilizer and pesticide losses to the environment, decreased water use, and decreased greenhouse gas emissions. However, the scope of these impacts is frequently unknown or very unpredictable (Balafoutis et al., 2017; Zhang et al., 2002). In addition, most research highlights potential implications based on experimental findings or model predictions rather than actual impacts. Few studies demonstrate a causal relationship between precision agriculture’s environmental performance and actual agricultural usage. With these advices in mind, some of the most important environmental advantages of precision agriculture are summarize.
5.1 Reduction in greenhouse gas emissions
Precision agriculture lowers greenhouse gas (GHG) emissions all around. First, because there is less overlap in agricultural operations, machine guidance and controlled traffic farming reduce fuel usage. According to Shockley et al. (2011), guidance systems can reduce fuel consumption by 6%, and Jensen et al. (2012) report a 25% decrease in fuel costs. For large-scale fields, these savings are greater and come with additional benefits, such as decreased soil compaction, runoff, and erosion (Balafoutis et al., 2017). Second, a decrease in effluents suggests, for instance, a decrease in nitrogen losses as ammonia and nitrogen oxides (Balafoutis et al., 2017, for example). Nitrous oxide emissions were reduced by 34% as a result of VRT nitrogen treatment in a case study on maize production in Germany (Sehy et al., 2003). Pampolino et al. (2007) demonstrated the ability of site-specific nutrient management to boost yields with increasing nitrogen fertilizer use while retaining low N2O emissions for locations in Southern India, the Philippines, and Southern Vietnam. Thirdly, if inputs are used more effectively, the indirect energy consumption footprint from inputs like fertilizer, seeds, and pesticides (e.g. Böcker et al., 2019) can be decreased.
5.2 Reduction in agriculture input losses
Losses of essential environmental inputs are often reduced by enhancing application efficiency. In order to identify tree health issues and direct the application of pesticides, Du et al. (2008) compare airborne multispectral analysis with human inspection for Texas citrus production.
Pesticide use was reduced by more than 90% as a result of the airborne multispectral method and variable-rate technology (VRT). Accordingly, Balafoutis et al. (2017) demonstrate that using precision application in various arable crops could minimize the use of herbicides by 11% to 90% across numerous tests. Dammer and Adamek (2012) demonstrate that sensor-based precision management of aphids, instead of uniform spraying, could cut insecticide use in wheat output by more than 13% at an experimental site in Germany. Kempenaar et al. (2018) show that the potential for pesticide savings (and Nitrogen) is based on a VRT of about 25% average. Irrigation has been found to increase at variable rates and Increase water use efficiency, potentially leading to water savings of up to 20–25% (e.g. Evans et al., 2013; Sadler et al., 2005). However, findings regarding the use of PF in irrigation vary widely (e.g. for reference techniques and soil and weather conditions) (e.g. Balafoutis et al., 2017).
Overall, emissions from agricultural systems into water bodies are reduced by the introduction of VRT (Balafoutis et al., 2017; Tey & Brindal, 2012). However, the current scale of these effects is uncertain primarily. For example, Harmel et al. (2004) reported that in a Texas corn experiment, VRT nitrogen application reduced total nitrogen emissions by 4–7% compared to uniform application, but effluent water quality was similar to VRT's. For case studies of potato production in the Netherlands and olive production in Greece, Van Evert et al. (2017) show that the introduction of VRT resulted in even more significant reductions in fertilizer use. VRT is not the only approach to curtail needless input consumption. Rejesus and Hornbaker (1999) used corn and soybean crop rotation modelling approach in Illinois to show that nitrate contamination can be reduced not only by VRT, but also by improved fertilization timing.
5.3 Reduction in consumption of cultivation inputs
Both Aggelopoulou et al. (2010) with apple trees and Vatsanidou et al. (2017) with pear trees assessed nitrogen’s variable treatment by taking into account the plantation’s preceding year’s output map and calculating the quantity of N for each plant and orchard acreage obtained from the soil. For the same crop output, the variable application of N allowed for up to 38% and 53% N reductions in the yields of apples and pears, respectively. Using precision irrigation Conesa et al. (2021) conducted a field experiment in a nectarine orchard. The crop’s evapotranspiration was the base of irrigation dose in traditional irrigation, while the soil–water sensors monitoring the volumetric soil–water content using multi-depth capacitance sensors was the base of the precision irrigation system. In the case of precision irrigation, 43% less water was used than with traditional irrigation. In peach trees Mounzer et al. (2008) used the similar procedure and reported 18% in precision irrigation water savings compared to the non-precision irrigation treatment. Using the same approach, Mounzer et al. (2008) found an 18% saving in water usage in precision irrigation compared to non-precision irrigation in peach plants. Precision agriculture eventually helps to improve the environment by minimizing the amount of cultivation/agricultural inputs used.
In summary, the literature submits that precision agriculture positively affects the environment, but there is little uncertainty about the extent of these effects. Therefore, additional research on the environmental impacts of precision agriculture is needed to advocate further private and public support.
6 Precision farming implements in India for small land holders
The agricultural environment is changing significantly in the twenty-first century, mainly because of precision farming. However, PA interventions are still in the preliminary stages of acceptance, even though Indian farmers are remarkably transitioning from traditional to technology-driven methods. One of the main limitations that has impeded the popularity of PA technology is the small field size since small landowners are unable to buy expensive modern technology. Further technical challenges, such as incompatibility with existing and traditional farming methods in India, high maintenance costs associated with sophisticated instrumentation and equipment, and limited access to relevant services and software, prevent Indian farmers from fully embracing precision agriculture. The application of precision agriculture is further hindered by the fragmented and diverse agricultural data, which comes from varied agro-climatic conditions. Additionally, the analytical and interpretive methodologies used to analyse and understand these data only sometimes meet local specifications. Therefore, replacing expensive, complex technology with simpler, more affordable alternatives must be the top priority.
Some of the reasonably priced tools recommended for Indian farms, particularly in the Indo-Gangetic Plains, include the laser land leveller, tensiometer, leaf colour chart (LCC), paired row trench planter, and micro-irrigation methods. These technologies have aided in improving on-farm water use efficiency and other farm inputs for better yields.
6.1 Laser land leveller (LLL)
Laser land levelling is a simple method for preparing the ground before sowing, which leads to increased yields and effective water use. Farmers have adopted this technology and micro-irrigation systems in their farms, resulting in increased grain yield by 13% while saving 31.8% of irrigation water for rice (Jat et al., 2003). Utilizing LLL for wheat resulted in an 11.9% increase in grain yield and a 13% reduction in water usage (Singh et al., 2011a, 2011b). Additionally, the application of this technology lead to energy conservation, because less water was needed to be pumped.
The management of landscape variability through accuracy and levelling is anticipated to be a crucial step in controlling soil and yield variability. Compared to traditional levelling, laser field levelling significantly increased the agronomic efficiency of the applied N, P, and K in wheat and rice (Choudhary et al., 2002; Pal et al., 2003). LLL was featured by Trimble at the 2019 Smart Farm Tech Exhibition in Raipur India. For farmers struggling due to receding water tables, a high-accuracy laser-based land levelling device was developed specifically. According to research accomplished at S.V. Patel Agricultural University in Meerut, rice and sugarcane yields have increased by (7–8%) and (12–13%), respectively. Also, reductions in tractor fuel usage of up to 24% and water savings of 20–30% were recorded (Anonymous, 2019).
Additionally, LLL enables water management optimization in the terrace field cropping system. (SRP, 2020). Additionally, with carefully levelled fields, water can be promptly regulated and optimized according to the needs of agricultural growth. Due to these benefits of LLL, weed issues are remedied, whereas crop productivity, grain quality, and water use efficiency are enhanced (Abdullaev et al., 2007; Agarwal & Goel, 1981; Aryal et al., 2015; Naresh et al., 2017). Similarly, enhanced agronomic use efficiency, decreased water usage during irrigation, and improved spatial and temporal management contribute to substantially increasing energy efficiency and curtailing greenhouse gas emissions associated with rice cultivation. More specifically, rice production that is carried out with less water usage also significantly decreases methane emissions (Sander et al., 2014). Several reports (Dobermann et al., 2004; Johansen, 1996; Kitchen et al., 1996; Pierce & Nowak, 1999; Whelan & McBratney, 2000) have declared LLL essential for ensuring optimal spatial and agronomic inputs in precision agriculture.
In fact, this practice of laser land levelling is universally recommended, without any bias towards wealthy farmers or huge land owners. Moreover, operations require approximately 4–5 h per hectare of land and can be conducted once every 3 years to achieve superior results. Its cost-effectiveness may further be enhanced by hiring the machine for shorter periods or collective purchasing (Dharini, 2015). The operational view of the laser land leveller is shown in Fig. 6.
A operational view of laser land leveller
6.2 Tensiometer
A tensiometer is an essential tool to detect soil moisture or water potential, defined as the force exerted by the roots to uptake water from the soil at the existing moisture level. Tensiometers are highly helpful for irrigation interventions and are widely applied in practice. Field tests using the PAU tensiometer technique were carried out at farmer’s fields throughout Punjab for scheduling irrigation to crops (Sidhu et al., 2008). These tests revealed that 25–30% of the irrigation water was saved without having any adverse effects on rice grain yield in comparison with that achieved with intermittent irrigation at an interval of two days. Tensiometer-based irrigation reduced the amount of irrigation water needed to grow rice in unlevelled settings by 14.9%. Since 2009, Punjab Agricultural University (PAU) has encouraged the paddy farmers in Punjab to utilize tensiometers. Tensiometers have been developed such that three coloured bands of green, yellow, and red indicate the urgency of irrigation, instead of the customary gauge. When the water level is green the tensiometer denotes adequate water level, while the yellow level suggests that irrigation is required. A red level indication denotes deficient water levels that can negatively impact the crop yield.
To calculate the soil–water characteristic curve (SWCC), Sun et al. (2010) suggested combining a tensiometer (single electrode sensor) and a frequency domain approach. The proposed method showed substantial similarity with the traditional laboratory-based measurement method; however, SWCC was restricted within the matric potential range of 0–80 kPa because of the air-entry value of the ceramic cup and the water cavity column inside the tensiometer. Tensiometers were used by Sujitha and Shanmugasundaram (2017) to manage greenhouse marigold watering. The flower yield·ha−1 was reported at 20.4 and 17.1 tonnes for the inside and outside the greenhouse, respectively, after the water was applied using a tensiometer with a 50% depletion of the available moisture. Growing marigolds inside a greenhouse yields 16% more while using 34% less water than growing them outside, outperforming open-air marigold farming.
6.3 Leaf colour chart (LCC)
The experience and understanding gained with field-level experiments are crucial components of PA. The insights gained from field observations are employed to make informed decisions regarding the adequate application of agricultural inputs, which otherwise can have devastating impacts if inappropriately used. For instance, improper use of nitrogen (N) fertilizer leads to nutrient waste, plant lodging, and environmental pollution. Farmers incur higher costs due to the overuse of pesticides and fertilizers, necessitating precise nitrogen application. For a healthy plant and environment, N must be applied at the proper time and dosage. Chlorophyll levels are directly correlated with nitrogen status in rice, indicated by leaf colour intensity. The N condition of a particular crop has been measured over time using a variety of instruments, including a leaf colour chart (LCC) and chlorophyll meters. These portable diagnostic equipment can be used to regulate the amount of nitrogen (N) applied to rice based on crop needs. LCC was first created by Japanese researchers (Furuya, 1987). Later, an upgraded version of the six-panel LCC was created by the International Rice Research Institute (IRRI) in collaboration with the agricultural research systems of various Asian nations. LCC is a reference tool for assessing leaf colour, owing to the six-colour panel representing various hues of green. Another leaf colour chart (LCC) was created by the International Rice Research Institute and the Philippines Rice Research Institute to assist farmers with real-time nitrogen management in rice production. Most rice farmers with limited resources could readily afford the technology because of its low cost. In the Indian state of West Bengal, real-time N management in rice was tested using LCC, which initiated farmer-participatory research (Islam et al., 2007). A study was undertaken to gauge the acceptance and impact of LCC after 3 years of validation research in the areas of intervention and nearby control villages. Data was gathered from 20% of randomly chosen farm households (Islam et al., 2007). Later, Singh et al., (2012a, 2012b) used demand-based N-fertilizer to irrigate wheat and used an LCC to determine a threshold leaf colour greenness. The application of N fertilizer based on actual demand enhanced the agronomic and recovery efficiencies. For real-time regulation of nitrogen in maize (Zea mays L.), Mathukia et al. (2014) also used LCC. A field experiment was carried out at the Instructional Farm of the Junagadh Agricultural University in Gujarat, India, over two consecutive rabi seasons (2011–2013) by applying 80 kg of nitrogen per hectare at LCC 4 and LCC 5, in addition to a basal dose of 20 kg of nitrogen per hectare divided into two splits. Even though the cultivation costs were slightly higher, the results showed a 62.7% increase in net returns (Rs. 25,649 ha−1) with higher yields than the fixed-time application.
6.4 Paired row trench planter
In a paired row planting strategy, the seeds of a crop are sown in widely spaced pairs of rows to maximize light exposure to sustain dense, tall perennial grasses for yield enhancement or to allow intercrop plantation within the intermittent spaces. The collection of techniques included the paired row trench planting method for sugarcane, suggested by PAU, Ludhiana (Singh et al., 2011a, 2011b). Sugarcane trenches 32 cm wide and 20–25 cm deep were placed in two rows. The soil remaining between two rows was used to cover the cane sets after they had been positioned at the bottom of the trenches. A 90-cm gap existed between each trench. Trenches were created using a recently developed tractor-operated PAU trencher.
In comparison with the flooding approach, this method has led to a significant increase in cane yield, simplified support operations, decreased dependence on lodging, and allowed conservation of 10–15% of irrigation water. In subtropical India, Singh et al. (2012a) investigated ways to increase sugarcane (Saccharum hybrid complex) productivity using modified trench planting. For 2 years, field tests were executed to examine the effects of different trench planting configurations for sugarcane. The combinations included planting in conventional furrows 90 cm apart (T1), planting in deep furrows (20 cm) 90 cm apart and covering setts with 2.5 cm of soil layer (T2), planting in paired rows at 120:60:120 cm (T3), and planting in paired rows of deep furrows (20 cm) at 120:60:120 cm and covering setts with 2.5 cm of soil layer (T4). Planting in conventional furrows 120 cm apart and covering setts with 2.5 (light irrigation T5). In each of the years of the trial, the T5 planting method yielded cane yields much higher than those of the other planting methods, at 118.7 and 121.7 tonnes·ha−1. Even though the modified trench method (T5) had a higher cost of cultivation due to a higher seed cane rate (8 tonnes ha−1) than the other planting methods (6–7 tonnes·ha−1), a higher profit margin was still achieved due to an increase in sugarcane production (18.72%) as a minimum to a maximum of 27.01%. The study thus concluded that the modified trench method of planting (T5) offers benefits in generating higher yields of cane and sugar over other conventional methods of planting, and that would be worth adopting by farmers for increased sugarcane productivity in sub-tropical India.
6.5 Pneumatic precision planter
Pneumatic planters are based on air suction mechanisms. In such planters, air is drawn through a rotating disc with numerous holes. Every seed in contact with the holes in the disc gets attached to it and is released at the desired location when suction is interrupted. The timing of the dropping of seeds is determined by the seed spacing intended. Since the seeds are controlled by air suction, no mechanical damage occurs (Singh et al., 2018). Precision sowing is recommended over other sowing techniques since it ensures more consistent seed spacing than the others (Yasir et al., 2012), and such accurate and precise seed planting is enabled by pneumatic planters. Because precise seed distribution allows each plant to receive the ideal amount of sunlight, water, and nutrients, increased crop yield is noted while preventing the wastage of expensive seeds (Singh et al., 2004).
Singh et al. (2018) assessed multiple commercially available indigenous and imported planters (Fig. 7) and compared their effectiveness with the conventional ridge planting (farmer's practice) approach. In comparison with ridge planting, the quality feed index of pneumatic planters was higher by 6.5%, and the yield was also increased by 3.8%, owing to the increased plant population (5.5%). However, the quality feed index, plant population, and output did not differ significantly between the planters. When compared to manual ridge planting, the overall operating cost of planting with pneumatic planters was 70% lower.
(Source: Singh et al., 2018)
Operational view of indigenous and imported pneumatic planter.
6.6 Micro-irrigation methods
The highly ineffective traditional methods of water transportation and irrigation have not only lead to tremendous water waste but also caused several environmental issues such as water logging, salinization, and soil deterioration (Madramootoo & Morrison, 2013). The most effective alternative for efficiently using surface and groundwater resources is to adopt current irrigation techniques, such as drip and sprinkler irrigation. In comparison with 50–60% for traditional irrigation techniques, the water consumption efficiency of these systems can reach up to 95%. Additionally, fertigation combined with drip irrigation produces the highest yields of quantity and quality while using the least amount of water possible. Fertigation enables the application of fertilizer in moderate doses at a time by the nutrient requirements of plants, ensuring that fertilizer is used as effectively as possible (Aakash et al., 2020). This method guarantees that the root zone has the proper amount of water and nutrients, meeting the long-term and short-term needs of the plant. Further, Kumar et al. (2015) created a micro-irrigation system powered by solar energy, allowing small landowners to acquire dry land areas. The water gathered in a small reservoir (or pond) at higher altitudes was elevated by a solar pumping unit and then supplied to the plant through a micro-tube irrigation system. Contrary to the traditional one-row command system, the gravity-fed micro-tubes were used in a star configuration to enable laterals to control four rows of crop. The system showed potential for small landowners, with a 92% uniform allocation.
According to the “Per Drop More Crop component of Pradhan Mantri Krishi Sinchayee Yojana (PMKSY)”, the Indian government has announced that it will provide up to 55% subsidy to marginal or small-scale farmers and up to 45% for other farmers for the installation of micro-irrigation systems, such as drip and sprinkler irrigation, on their farms. Such measures will facilitate the adaptation of these practical measures and allow maximization of productivity.
7 Summary
Nearly 78% of all farmers in India possess small plots of land—two acres or less—to cultivate food crops. Hence, it is imperative to restructure the entire agricultural system towards low-input, high-efficiency, and sustainable farming to maximize the utilization of each square foot of soil. Precision farming is a method where inputs are utilized in specific volumes to get amplified average yields compared to conventional farming techniques. Precision farming offers a novel system-based answer to today's agricultural difficulties, such as the need to balance production and ecological issues. It is built on cutting-edge information technology. It characterizes and simulates soil and plant species variance and combines agricultural practices to fulfil site-specific constraints. It maximizes economic returns while lowering agricultural energy input and ecological impact.
The present study has reviewed various widely used sensors essential to developing precision agriculture technology. These technologies are applied to create simpler, more affordable farming equipment that dramatically increases crop performance. A select few of them are examined for their current state, potential, and potential obstacles from an Indian perspective. However, the biggest obstacles to successfully implementing these technologies are more education and awareness on precision farming methods, affordable solutions, and a better farming model ecosystem, considering the land size and an impending need for regulations concerning data security and privacy. Furthermore, precision farming is still a notion in numerous developing countries, and strategic assistance from the public and commercial sectors is required to accelerate its implementation. However, successful adoption requires at least three stages: exploration, analysis, and implementation. Precision agriculture has the potential to tackle both the economic and ecological issues that affect agriculture production. Although questions involving cost-effectiveness and the most efficient uses of existing technology tools persist, the notion of “doing the appropriate thing in the correct location at the right moment” has a significant intuitive value. Given today’s pressing necessity, an all-out effort should be made to leverage new technical inputs to transform the “Green Revolution” into an “Evergreen Revolution.” Finally, the success of precision agriculture is heavily dependent on how effectively and rapidly the information required to guide the new technology can be obtained.
Data availability
All the relevant data are provided in the paper only.
Code availability
Not applicable.
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Sharma, A., Prakash, A., Bhambota, S. et al. Investigations of precision agriculture technologies with application to developing countries. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04572-y
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DOI: https://doi.org/10.1007/s10668-024-04572-y