Deep Learning-Based Classification of Wheat Leaf Diseases for Edge Devices

This paper proposes a deep learning-based approach for classifying wheat leaf diseases such as stripe rust and septoria for edge devices. The study used a 407 wheat leaf images dataset with three classes: healthy, stripe rust, and septoria. Data augmentation techniques created more training images once the dataset was divided into training, validation, and testing sets. The classification was done using a convolutional neural network (CNN) with a test set accuracy of 98.77%. The outcomes show that deep learning techniques are effective for accurately classifying wheat leaf diseases using cutting-edge devices, with potential for early detection in the field. Future work can include exploring advanced deep-learning techniques and larger datasets to enhance the model's performance significantly. The proposed method can be used in various applications, such as mobile phone apps, to quickly and accurately detect wheat leaf diseases in the field. The potential impact of the proposed approach is significant, as it can help prevent crop loss and increase crop yield, leading to a more sustainable and food-secure future.