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
- Wei Deng, Chunjiang Zhao, Liping Chen, Xiu Wang, Constant pressure control for variable-rate spray using closed-loop proportion integration differentiation regulation , Journal of Agricultural Engineering: Vol. 47 No. 3 (2016)
- Ameneh Sobhani, Shawkat B.M. Hassan, Giovanna Dragonetti, Raffaella Balestrini, Mauro Centritto, Antonio Coppola, Alessandro Comegna, Comparing actual transpiration fluxes as measured at leaf-scale and calculated by a physically based agro-hydrological model , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Guanglai Wang, Congcong Wang, Dayang Liu, Detection of early collision and compression bruises for pears based on hyperspectral imaging technology , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Yevgeny Beiderman, Mark Kunin, Eli Kolberg, Ilan Halachmi, Binyamin Abramov, Rafael Amsalem, Zeev Zalevsky, Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle , Journal of Agricultural Engineering: Vol. 45 No. 4 (2014)
- Hemanthakumar R. Kappali, Sadyojatha K.M., Prashanthi S.K., Parametric evaluation of segmentation techniques for paddy diseases analysis , Journal of Agricultural Engineering: Vol. 54 No. 4 (2023)
- José Roberto Moreira Ribeiro Gonçalves, Gabriel Araújo e Silva Ferraz, Étore Francisco Reynaldo, Diego Bedin Marin, Patrícia Ferreira Ponciano Ferraz, Manuel Pérez-Ruiz, Giuseppe Rossi, Marco Vieri, Daniele Sarri, Comparative analysis of soil-sampling methods used in precision agriculture , Journal of Agricultural Engineering: Vol. 52 No. 1 (2021)
- Aristide Timene, Haman Djalo, Design of a five-bar duckbill-type mechanism for sorghum transplanting , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Changjian Zhou, Zhenyuan Zhao, Wenzhuo Chen, Yuquan Feng, Jia Song, Wensheng Xiang, Residual attention based multi-label learning for apple leaf disease identification , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Qingyu Chen, Rui Kang, Naishuo Wei, Yunlei Fan, Zeyu Wang, Yu Chen, Jun Chen, Design and experiment optimize of the vibration harvesting machine of Lycium barbarum L. , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Jacopo Bacenetti, Sara González-García, Aira Mena, Marco Fiala, Life cycle assessment: an application to poplar for energy cultivated in Italy , Journal of Agricultural Engineering: Vol. 43 No. 2 (2012)
<< < 3 4 5 6 7 8 9 10 11 12 > >>
You may also start an advanced similarity search for this article.
