Abstract
Germination rate test is a mandatory process before seeds sell on the market. In practical applications, seeding process is fully manual, which makes the process pricy and inefficient. Our group developed a new autonomous seeding system, which consists of four parts: sand filling, seeds placement, watering and sand covering. Seed placement, consisting of seed container and seed sigulation and placement, is the most important part. The seed placement device has been built, but its three key parameters (tilted bottom angle of seed container, brush spinning and horizontal speed configuration, and seed dropping height) have not been determined yet. In this study, we compared the performance of four tilt angles (20°, 30°, 40°, and 50°). Using seed uniformity of distribution as the evaluation index, 40° tilt angle has been determined to generate the most satisfactory performance. The hole’s seed filling performance of brush under four speed configurations was compared and it was decided that the configuration of fast spinning speed (0.15 r/s) and slow horizontal speed (0.76 cm/s) would generate the highest hole’s seed filling rate. Seed dropping heights (13 cm, 15 cm, 17 cm, and 19 cm) from aluminum sheet to sand surface was compared and it was quantitatively determined that dropping height did not affect seed layout uniformity. Thus, the height of the aluminum sheet can be adjusted according to the tray size. The results of this study provide the key parameters that machine would work in good performance. This study demonstrated that the autonomous seeding machine has a robust performance and meets the requirements of auto seeding process for germination test. It also has potential for commercial applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ruan Z, Wang X, Liu Y, Liao W (2019) Corn. In: Integrated processing technologies for food and agricultural by-products. Academic Press, pp 59–72
Flores P, Zhang Z, Igathinathane C, Jithin M, Naik D, Stenger J, Kiran R (2021) Distinguishing seedling volunteer corn from soybean through greenhouse color, color-infrared, and fused images using machine and deep learning. Ind Crops Prod 161:113223
Reidy S (2022) China expected to produce more corn in 2021–2022. https://www.world-grain.com/articles/16449-china-expected-to-produce-more-corn-in-2021-22#:~:text=BEIJING%2C
Wang Y, Peng Y, Zhuang Q, Zhao X (2020) Feasibility analysis of NIR for detecting sweet corn seeds vigor. J Cereal Sci 93:102977
Ma JN (2021) In Gansu, corn seeds prepared for spring planting. https://www.chinadaily.com.cn/a/202111/15/WS61921812a310cdd39bc75701_1.html
Times G (2022) China to strengthen regulation on seed production: agricultural ministry. https://www.globaltimes.cn/page/202203/1253560.shtml
Zhou S, Sun L, Xing W, Feng G, Ji Y, Yang J, Liu S (2020) Hyperspectral imaging of beet seed germination prediction. Infrared Phys & Technol 108:103363
Yang J, Sun L, Xing W, Feng G, Bai H, Wang J (2021) Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM. Spectrochim Acta Part A: Mol Biomol Spectrosc 253:119585
Wu N, Weng S, Chen J, Xiao Q, Zhang C, He Y (2022) Deep convolution neural network with weighted loss to detect rice seeds vigor based on hyperspectral imaging under the sample-imbalanced condition. Comput Electron Agric 196:106850
Hom NH, Becker HC, Möllers C (2007) Non-destructive analysis of rapeseed quality by NIRS of small seed samples and single seeds. Euphytica 153(1):27–34
Agelet LE, Hurburgh Jr CR (2014) Limitations and current applications of Near Infrared Spectroscopy for single seed analysis. Talanta 121:288–299
Fan Y, Ma S, Wu T (2020) Individual wheat kernels vigor assessment based on NIR spectroscopy coupled with machine learning methodologies. Infrared Phys & Technol 105:103213
Ahmed MR, Yasmin J, Collins W, Cho BK (2018) X-ray CT image analysis for morphology of muskmelon seed in relation to germination. Biosyst Eng 175:183–193
Gargiulo L, Leonarduzzi C, Mele G (2020) Micro-CT imaging of tomato seeds: predictive potential of 3D morphometry on germination. Biosyst Eng 200:112–122
Wu JZ, Li XQ, Liu CL, Yu L, Sun XR, Sun LJ (2020) Research on non-destructive testing of corn seed vigor based on THz-TDS reflecting imaging. Spectrosc Spectr Anal 40(9):2840–2844
Khoenkaw P (2016) An image-processing based algorithm for rice seed germination rate evaluation. In: 2016 international computer science and engineering conference (ICSEC). pp 1–5
Buters T, Belton D, Cross A (2019) Seed and seedling detection using unmanned aerial vehicles and automated image classification in the monitoring of ecological recovery. Drones 3(3):53
Colmer J, O'Neill CM, Wells R, Bostrom A, Reynolds D, Websdale D, Shiralagi G, Lu W, Lou Q, Cornu TL, Ball J, Renema J, Andaluz GF, Benjamins R, Penfield S, Zhou, J (2020) SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination. New Phytol 228(2):778–793
Gao S, Liu Z, Li X (2022) Study of improved Yolov5 algorithms for gesture recognition. In: 2022 IEEE 6th advanced information technology, electronic and automation control conference (IAEAC), Beijing, China, pp 378-384. https://doi.org/10.1109/IAEAC54830.2022.9929672
Peng Y, Wang X, Du Y, Chen H (2014) Using voronoi tessellation and Delaunay triangulation to evaluate spatial uniformity of particle distribution. Appl Mech Mater 614:413–6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Wei, X., Zhang, Z. (2023). Design, Construction, and Experiment-Based Key Parameter Determination of Auto Maize Seed Placement System. In: Zhang, M., Li, H., Sheng, W., Qiu, R., Zhang, Z. (eds) Sensing Technologies for Field and In-House Crop Production. Smart Agriculture, vol 7. Springer, Singapore. https://doi.org/10.1007/978-981-99-7927-1_7
Download citation
DOI: https://doi.org/10.1007/978-981-99-7927-1_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7926-4
Online ISBN: 978-981-99-7927-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)