Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading

This study proposes integrated image processing and deep learning approaches for wheat rust disease detection and grading. Wheat stripe rust is one of the diseases affecting the crop in nearly all wheat-growing countries, where humidity and high temperatures characterize the growing season. The early and prompt detection of this disease is essential for establishing timely remedial measures to contain its spread. We present several image-processing and deep-learning approaches for detecting wheat stripe rust. The image processing techniques used in this research include single-band and dual-band processing, computing the Visual Atmospheric Resistance Index (VARI), image segmentation, and image cropping. These techniques are evaluated with different deep learning approaches like transfer learning and other Convolutional Neural Networks (CNNs) architectures. The results obtained on the dataset show that processing the images to eliminate the background as much as possible before training a suitable deep learning model with a low number of epochs optimally classifies the wheat stripe rust into the target classes (i.e., healthy, resistant, and susceptible) with an overall accuracy of 84.1% on the holdout test set. Besides the comparative analysis of various image processing approaches with different deep learning models, this study suggests multiple future research directions.