Automated Wheat Disease Detection Using Deep Learning: An Object Detection and Classification Approach
Abstract
Wheat is among the key staple crops in the world, greatly influenced by plant diseases. As global population grows, there is urgency to establish sustainable agricultural methods requiring early and accurate diagnosis of wheat diseases. This paper examines deep learning solutions for automated wheat head disease detection using two benchmark datasets: the Global Wheat Head Detection (GWHD) dataset for object detection, and the Large Wheat Disease Classification Dataset (LWDC) for disease classification. YOLOv4 trained on GWHD achieves a mean Average Precision (mAP) of 91%. Transfer learning with COCO pre-trained weights improves multi-class detection on LWDC. Among five CNN models evaluated — VGG19, ResNet50, EfficientNet-B0, NASNetMobile, and NASNetLarge — VGG19 achieves the best F1 score of 95%. The combined YOLOv4 + VGG19 pipeline demonstrates strong potential for real-time wheat disease monitoring in precision agriculture.
References
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