Evaluation of Weed Infestations in Row Crops Using Aerial RGB Imaging and Deep Learning

Plamena D. Nikolova, Boris I. Evstatiev (), Atanas Z. Atanasov and Asparuh I. Atanasov
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Plamena D. Nikolova: Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria
Boris I. Evstatiev: Department of Automatics and Electronics, Faculty of Electrical Engineering, Electronics, and Automation, University of Ruse “Angel Kanchev”, 7004 Ruse, Bulgaria
Atanas Z. Atanasov: Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria
Asparuh I. Atanasov: Department of Mechanics and Elements of Machines, Technical University of Varna, 9010 Varna, Bulgaria

Agriculture, 2025, vol. 15, issue 4, 1-19

Abstract: One of the important factors negatively affecting the yield of row crops is weed infestations. Using non-contact detection methods allows for a rapid assessment of weed infestations’ extent and management decisions for practical weed control. This study aims to develop and demonstrate a methodology for early detection and evaluation of weed infestations in maize using UAV-based RGB imaging and pixel-based deep learning classification. An experimental study was conducted to determine the extent of weed infestations on two tillage technologies, plowing and subsoiling, tailored to the specific soil and climatic conditions of Southern Dobrudja. Based on an experimental study with the DeepLabV3 classification algorithm, it was found that the ResNet-34-backed model ensures the highest performance compared to different versions of ResNet, DenseNet, and VGG backbones. The achieved performance reached precision, recall, F1 score, and Kappa, respectively, 0.986, 0.986, 0.986, and 0.957. After applying the model in the field with the investigated tillage technologies, it was found that a higher level of weed infestation is observed in subsoil deepening areas, where 4.6% of the area is infested, compared to 0.97% with the plowing treatment. This work contributes novel insights into weed management during the critical early growth stages of maize, providing a robust framework for optimizing weed control strategies in this region.

Keywords: weed detection; non-contact methods; maize; neural networks; classification (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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