Wheat disease monitoring using deep learning

¹ ABBES Laghrour University, Algeria
² Laboratory of SATIT, Department of Industrial Engineering, Abbes Laghrour University, Khenchela 40004, Algeria
³ Telecommunications Laboratory (LT), Institute of Telecommunications, 8 Mai 1945 – Guelma University, Guelma, Algeria
⁴ ECAM-EPMI, LR2E Laboratory, 13 bd de l’Hautil, 95092, Cergy-Pontoise France
⁵ Department of Computer Sciences, University of khenchela, Algeria
⁶ Department of Electronic and Télécommunication, University of Echahid Larbi Tebessi, Tebessa Algeria

Abstract

Wheat is a primary carbohydrate-rich food commodity in the world, and the health of the wheat plants influences its production. Farmers can face a challenge due to decreased productivity caused by infected plants. However, wheat farmers have used conventional methods to survey diseases; however, these methods are often ineffective and inefficient, taking a lot of time and labor. And for enhanced wheat production, new strategies must be employed. Deep learning, specifically computer vision-based techniques, has proven significant capability in tasks like image classification, segmentation, and object detection. Deep learning techniques such as You Only Look Once (YOLO) models are state-of-the-art neural network algorithms used for accurate object detection. this study presents a comparative evaluation of two state-of-the-art object detection models, YOLOv5 and YOLOv8, for disease detection. Data augmentation techniques such as image noise, rotation, and flipping were implemented to improve the model’s performance during the training phase. The model’s performance was evaluated using metrics such as precision, recall, F1-score, and mean Average Precision (mAP).. The results show that the YOLOv5 and YOLOV8 models achieved good performance. They were able to detect the healthy and disease in images, These findings demonstrate that while both models are highly effective, but YOLOv8 offers greater robustness and accuracy for real-time detection.