https://doi.org/10.4081/jae.2022.1453
Key technology of crop precision sowing based on the vision principle
Published: 25 August 2022
Abstract Views: 1667
PDF: 345
HTML: 45
HTML: 45
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
The growth of crops is seriously affected in the process of precision planting of crops due to many external environmental interference factors, low precision of sowing technology, and large significant relative errors. To solve this problem, machine vision technology is introduced to study the key technology of crop precision sowing based on the vision principle. After pre-processing the crop image, the corresponding histogram is established. Then, the segmentation threshold method is used to gray the image and determine the best threshold to have a good recognition effect. Finally, according to the growth height and colour analysis of crops in the image, predict the growth of crops and realise the precision sowing of crops. The comparative experimental results show that under the application of the new sowing technology, the estimation accuracy of the crop planting area is high, the recognition accuracy of planting position is also high, and the fertilisation uniformity is close to the actual data, which can provide an important basis for improving the quality of crop sowing.
Abdollahzadeh S., Sepehr A., Rashki A. 2021. Detecting degraded, prone and transition ecosystems by environmental thresholds and spectral functions. Remote. Sens. Appl. Soc. Environ. 22: 100503.
Addesso P., Vivone G., Restaino R., Chanussot J. 2020. A data-driven model-based regression applied to panchromatic sharpening. IEEE. Trans. Image. Process. 29:7779-94.
Akhter M.J., Kudsk P., Mathiassen S.K., Bo M. 2020. Rattail fescue (Vulpia myuros) interference and seed production as affected by sowing time and crop density in winter wheat. Weed. Sci. 69:1-35.
Alfarisy F.K., Andriyani I., Bowo C. 2020. Evaluation of water quality due to the use of intensive fertilizer on farmer level in the upstream of bedadung jember watershed, East Java, Indonesia. J. Degraded. Min. Lands. Manage. 7:2301-12.
Booth B.G., Sijbers J., Beenhouwer J.D. 2020. A machine learning approach to growth direction finding for automated planting of bulbous plants. Sci. Rep. 10:661.
Buthelezi D., Mafeo T.P., Mathaba N. 2020. Preharvest bagging as an alternative technique for enhancing fruit quality: a review. Horttechnology. 31:1-10.
Fca B., Phh C. 2020. Prediction of human odour assessments based on hedonic tone method using instrument measurements and multi-sensor data fusion integrated neural networks. Biosyst. Eng. 200:272-83.
Fue K., Porter W.M., Barnes E., Rains G. 2021. Ensemble method of deep learning, color segmentation, and image transformation to track, localize, and count cotton bolls using a moving camera in real-time. Trans. ASABE. 64:341-52.
Gao X.Y., Li J., Zhang C.X. 2020. Similarity calculation of 3D model by integrating improved ACO into HNN. IEEE. Access. 8:155378-88.
Goodwin D., Holman I., Pardthaisong L., Visessri S., Ekkawatpanit C., Rey Vicario D. 2022. What is the evidence linking financial assistance for drought-affected agriculture and resilience in tropical Asia? A systematic review. Reg. Environ. Change. 22:1-13.
Guo B., Zhang D., Pei L., Su Y., Wang X., Bian Y., Zhang D., Wanqiang Y., Zhou Z., Guo L., 2021. Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017. Sci. Total. Environ. 778:146288.
Li J., Xu K., Chaudhuri S., Yumer E., Zhang H., Guibas L. 2017. GRASS: generative recursive autoencoders for shape structures. ACM. T. Graphic. 36:1-14.
Ni J.N. 2021. Research on navigation technology of precision seeder based on optical and ultrasound joint positioning. J. Agric. Mec. Res. 43:211-15.
Oad V.K., Dong X., Arfan M., Kumar V., Mohsin M.S., Saad S., Lü H., Azam M.I., Tayyab M. 2020. Identification of shift in sowing and harvesting dates of rice crop (l. oryza sativa) through remote sensing techniques: a case study of larkana district. Sustainability. 12:1-15.
Talaat M., Arafa I., Metwally H., 2020. Advanced automation system for charging electric vehicles based on machine vision and finite element method. IET. Electr. Power. Appl. 14:2616-23.
Tian H., Qin Y., Niu Z., Wang L., Ge S. 2021. Summer maize mapping by compositing time series sentinel-1A imagery based on crop growth cycles. J. Indian. Soc. Remote. Sens. 49:2863-74.
Towers P.C., Poblete-Echeverría C. 2021. Effect of the illumination angle on NDVI data composed of mixed surface values obtained over vertical-shoot-positioned vineyards. Remote. Sens. 13:855.
Virk S.S., Fulton J.P., Porter W.M., Pate G.L. 2020. Row-crop planter performance to support variable-rate seeding of maize. Precis. Agric. 21:603-19.
Xie Y., Sheng Y., Qiu M., Gui F. 2022. An adaptive decoding biased random key genetic algorithm for cloud workflow scheduling. Eng. Appl. Artif. Intell. 112:104879.
Yang Y., Li T., Wang Y., Cheng H., Chang S., Liang C., An S. 2021. Negative effects of multiple global change factors on soil microbial diversity. Soil. Boil. Biochem. 156:108229.
Yi X.Q., Liu J. 2021. Wavelet threshold extraction method of local shadow feature of CT image. Comput. Simul. 38:181-4+403.
Yu H., Chen X., Ren M., Yin L., Wu Q., Zhan J., Liu Q. 2021. A coupled bend-twist deformation monitoring method based on inclination measurement and rational cubic spline fitting. Mech. Syst. Signal. Process. 147:107084.
Zhang X., Lu H., Guo D., Bao L., Huang F., Xu Q., Qu X. 2021. A guaranteed convergence analysis for the projected fast iterative soft-thresholding algorithm in parallel MRI. Med. Image. Anal. 69:101987.
Zhao Z.Y., Wang P.Y, Xiong X.B., Wang Y.B., Zhou R., Tao H.Y., Grace U.A., Wang N., Xiong, Y.C. 2022. Environmental risk of multi-year polythene film mulching and its green solution in arid irrigation region. J. Hazard. Mater. 435:128981.
Zheng W., Yin L., Chen X., Ma Z., Liu S., Yang B. 2021. Knowledge base graph embedding module design for Visual question answering model. Pattern. Recognit. 120:s108153.
How to Cite
Li, B. and Li, J. (2022) “Key technology of crop precision sowing based on the vision principle”, Journal of Agricultural Engineering, 54(1). doi: 10.4081/jae.2022.1453.
Copyright (c) 2022 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Sara Cucchiaro, Laura Carretta, Paolo Nasta, Federico Cazorzi, Roberta Masin, Nunzio Romano, Paolo Tarolli, Multi-temporal geomorphometric analysis to assess soil erosion under different tillage practices: A methodological case study , Journal of Agricultural Engineering: Vol. 53 No. 1 (2022)
- Kai Tian, Jiefeng Zeng, Tianci Song, Zhuliu Li, Asenso Evans, Jiuhao Li, Tomato leaf diseases recognition based on deep convolutional neural networks , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Lorenzo Vergni, Alessandra Vinci, Francesca Todisco, Francesco Saverio Santaga, Marco Vizzari, Comparing Sentinel-1, Sentinel-2, and Landsat-8 data in the early recognition of irrigated areas in central Italy , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- José F. Reyes, Elías Contreras, Christian Correa, Pedro Melin, Image analysis of real-time classification of cherry fruit from colour features , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Gabriella Impallomeni, Francesco Barreca, Agrivoltaic systems towards the European green deal and agricultural policies: a review , Journal of Agricultural Engineering: Vol. 56 No. 1 (2025)
- Albert Min, Nam Nguyen, Liam Howatt, Marlowe Tavares, Jaho Seo, Aeroponic systems design: considerations and challenges , Journal of Agricultural Engineering: Vol. 54 No. 1 (2023)
- Zhongkuan Wang, Sheng Wen, Yubin Lan, Yue Liu, Yingying Dong, Variable-rate spray system for unmanned aerial applications using lag compensation algorithm and pulse width modulation spray technology , Journal of Agricultural Engineering: Vol. 55 No. 1 (2024)
- Alina Evelyn Badillo-Márquez, Jonathan J. Cid-Galiot, Rubén Posada-Gómez, Alberto Alfonso Aguilar-Lasserre, Automated system for the detection of risk in agricultural sugarcane harvesting using digital image processing and deep learning , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- José Luis Morales-Reyes, Héctor-Gabriel Acosta-Mesa, Elia-Nora Aquino-Bolaños, Socorro Herrera Meza, Aldo Márquez Grajales, Anthocyanins estimation in homogeneous bean landrace (Phaseolus vulgaris L.) using probabilistic representation and convolutional neural networks , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Gabriele Mattachini, Elisabetta Riva, Francesca Perazzolo, Ezio Naldi, Giorgio Provolo, Monitoring feeding behaviour of dairy cows using accelerometers , Journal of Agricultural Engineering: Vol. 47 No. 1 (2016)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
