Abstract
The demand for food in the world tends to increase day by day with the growth of the population. Precision agriculture, through data mining, can address this issue to a large extent. Data mining is used in agricultural sector to analyze different factors that affect agricultural production. Agriculture plays a significant role in the economic development and employment of India. The major problem of Indian farmers is that they do not choose the proper crop based on their soil needs and environmental conditions. Therefore, they are facing major production setbacks. We are attempting to address this problem by creating a recommendation model to recommend optimal crops for farmers to grow crops based on different parameters. It will assist them in making an informed decision before cultivation using sequential forward feature selection. The experimental results on publicly available datasets show the efficacy of the proposed model.
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References
Gates B, Pitron G, Mann ME, Gates BBDCATHA 978–0385546133, Pitron TRMWBG, 978–1950354313 (2022) How to avoid a climate disaster, the new climate war, The Rare Metals War 3 Books Collection Set. Knopf/Scribe US Ltd
https://www.thehindu.com/news/national/india-to-be-most-populous-by-2027-un/article28067167.ece
https://agricoop.nic.in/sites/default/files/Web%20copy%20of%20AR%20%28Eng%29_7.pdf
Pande SM, Ramesh PK, Anmol A, Aishwarya BR, Rohilla K, Shaurya K (2021) Crop recommender system using machine learning approach. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, pp 1066–1071
Raja SKS, Rishi R, Sundaresan E, Srijit V (2017) Demand based crop recommender system for farmers. In: 2017 IEEE technological innovations in ICT for agriculture and rural development (TIAR). IEEE, pp 194–199
Doshi Z, Nadkarni S, Agrawal R, Shah N (2018) AgroConsultant: intelligent crop recommendation system using machine learning algorithms. In: 2018 fourth international conference on computing communication control and automation (ICCUBEA). IEEE, pp 1–6
Kumar PTMSAKV (2022) Introduction to data mining, 2nd edn. Pearson India
Kirar JS, Agrawal RK (2018) Relevant frequency band selection using sequential forward feature selection for motor imagery brain-computer interfaces. IEEE Sympos Ser Comput Intell (SSCI) 2018:52–59. https://doi.org/10.1109/SSCI.2018.8628719
James G, Witten D, Hastie T, Tibshirani R (2021) An introduction to statistical learning: with applications in R (Springer Texts in Statistics) (2nd ed. 2021 ed.). Springer
Kirar JS, Agrawal RK (2018) Relevant feature selection from a combination of spectral-temporal and spatial features for classification of motor imagery EEG. J Med Syst 42(5):1–15
Yan Y-S (2015) Decision tree methods: applications for classification and prediction. Shanghai, Shanghai Municipal Bureau of Publishing.ncbi.nlm.nih.gov
Armin S (2005) “SVM.” Data mining and knowledge discovery handbook I:2
Shou X (2016) Bayesian Naïve Bayes classifiers to text classification. J Inf Sci 44(1):59. https://doi.org/10.1177/0165551516677946
Zhang Z (2016) Introduction to machine learning: k-nearest neighbours. Ncbi 1(1):1
https://www.kaggle.com/atharvaingle/crop-recommendation-dataset
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Mondal, S., Upadhyaya, B., Nayan, K., Singh, V., Kirar, J.S. (2023). Enhancing Agricultural Outcome with Multiple Crop Recommendations Using Sequential Forward Feature Selection. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_5
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