A robust deep learning model for multi-class wheat disease classification

Wheat diseases present serious threats to global agricultural productivity resulting in yield loss and economic stagnation. For efficient management, a rapid diagnosis is crucial; however, traditional approaches consume time and need experience-based or skill-based human intervention. This study applies deep learning techniques for the automated classification of wheat diseases on a custom dataset with nine disease classes. The dataset is subject to pre-processing to get rid of class imbalance and image variability. The classification is executed using the EfficientNetV2B0 model due to its optimized architecture and feature extraction capabilities. Various processes of pre-processing and augmentation are introduced to boost model performance. The study gives a vivid review of some advanced approaches noted in deep learning-based detection of wheat diseases. Results show that deep learning can take charge of the diagnosis of diseases such that it becomes a skillful alternative to manual inspection. With the help of the said model, precision agriculture will be enabled through rapid identification and accurate diagnosis of diseases in support of critical timely decisions by the farmers. Future research can look into hyperspectral imaging, multimodal learning, or onto deploying in real-time field studies, thus giving testament to how AI will redefine, especially, sustainable wheat production and plant disease management.