Fast Detection of Plants in Soybean Fields Using UAVs, YOLOv8x Framework, and Image Segmentation
Mukhamediev R.I. Smurygin V. Symagulov A. Kuchin Y. Popova Y. Abdoldina F. Tabynbayeva L. Gopejenko V. Oxenenko A.
August 2025Multidisciplinary Digital Publishing Institute (MDPI)
Drones
2025#9Issue 8
The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature.
convolutional neural networks , crop monitoring , machine learning , object detection , precision agriculture , segmentation , weeds
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Institute of Automation and Information Technologies, Satbayev University (KazNRTU), Almaty, 050013, Kazakhstan
Management and Business Department, Transport and Telecommunication Institute, Lauvas iela 2, Riga, LV-1003, Latvia
LLP Kazakh Research Institute of Agriculture and Plant Growing, Almaty, 040909, Kazakhstan
International Radio Astronomy Centre, Ventspils University of Applied Sciences, Ventspils, LV-3601, Latvia
Department of Natural Science and Computer Technologies, ISMA University of Applied Sciences, Riga, LV-1019, Latvia
Institute of Automation and Information Technologies
Management and Business Department
LLP Kazakh Research Institute of Agriculture and Plant Growing
International Radio Astronomy Centre
Department of Natural Science and Computer Technologies
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