Light Weight ResNet for Detection of Wheat Yellow Rust over Mobile Captured Images from Wheat Fields

Plant diseases act as a major threat to small-scale farmers as they lead to major destruction in the overall food supply. In this, yellow rust disease of wheat is a major cause of concern in wheat producing countries. To provide effective measures for detection and avoidance of the destruction of the yellow rust an early identification is required. Plant disease identification from the images is an interesting research in agriculture and computer fields. The data was collected using the mobile phones and GPS meter from wheat fields of humid subtropical parts of Haryana (India). A total of 10000 images were used in the machine learning program out of which 5000 images from 2022 dataset and 5000 images from 2023 dataset. The dataset consist two categories of healthy and yellow rust images. The ResNet, is a popular deep learning method, was applied to detect the yellow rust disease of wheat. The result of 2022 dataset shows the training accuracy as 98.80% and the test accuracy as 93.19% and the result of 2023 dataset shows the training accuracy as 99.69% and the test accuracy as 99.10%.