Automated Wheat Disease Detection using Deep Learning: an Object Detection and Classification Approach
Sepideh Etaati, Javad Khoramdel, Esmaeil Najafi
Abstract
The wheat crop is a crucial staple in agriculture, but its yield is often compromised by various diseases. With a growing global population, adaptive agricultural practices are essential, and early detection of wheat diseases becomes vital. Utilizing deep learning techniques can offer practical solutions for disease detection and classification. This paper explores different approaches to automate wheat head disease detection and identifies the best methods. Two distinct datasets are used: the Global Wheat Head Detection (GWHD) dataset for object detection and the Large Wheat Disease Classification Dataset (LWDC) for classification. The YOLOv4 object detection network is trained on GWHD, achieving a mean Average Precision (mAP) of 91%. The trained weights are used for domain transfer for training on LWDC dataset for further exploration, where training with three classes and COCO’s pre-trained weights yields superior mAP results. Additionally, five CNN models, including VGG19, ResNet50, EfficientNet-BO, NASNetMobile, and NASNetLarge, are evaluated on LWDC dataset for wheat disease classification. VGG19 emerges as the top-performing model, accurately classifying various wheat diseases, achieving an average F1 score of 95%. The combination of YOLOv4 for object detection and VGG19 for classification presents promising results, offering valuable insights for precision agriculture and early disease detection in wheat crops.