Deep Learning-Based Wheat Stripe Rust Disease Recognition Using Mask RCNN

Over the past 25 years, climate change has made it easier for fungal infections to spread, particularly Puccinia graminis f.sp. tritici (Pgt), which leads to wheat stripe rust and offers a grave risk to global wheat output and food security. In this paper, the usage of image processing techniques and the mask- RCNN model for independent wheat stripe rust recognition. With a precision of $\mathbf{8 2. 9 2 \%}$ and a Mean Intersection over Union (MIoU) of 95.32%, the model exhibits outstanding performance metrics after its training on a dataset of one thousand high-resolution photos. Using preprocessing techniques like normalization and scaling along with deep learning, the model correctly identifies wheat stripe rust lesions on individual wheat leaves. Later studies combining advanced AI techniques such as meta-learning and reinforcement learning show interest in increasing the model’s adaptability to various contexts. Blockchain technology could be utilized soon to monitor illnesses and guarantee data accessibility and integrity. Operating together with partners in the food systripe can encourage innovation, improve crop resilience, fight world hunger, and advance sustainable agricultural methods.