Towards an Improved Deep Learning Plant Disease Doctor for Sustainable Food Security: A Comprehensive Survey

Monday Abutu Idakwo, Yusufu Ramat Ubimago, Johnson Adeiza Obari, Emmanuel Olufemi Osaji, Joshua Sokowonci Mommoh, Olusogo Julius Adetunji

Abstract


The global agricultural sector faces persistent challenges from plant diseases, which threaten food security, economic stability, and sustainable agriculture. Traditional methods of disease diagnosis, reliant on manual scouting by human experts, are often slow, labour-intensive, and prone to error. The advent of deep learning, a subset of artificial intelligence, has catalysed a paradigm shift in how plant diseases are detected and diagnosed. This paper surveys this transformation by synthesising recent advancements in deep learning architectures, primarily Convolutional Neural Networks, as well as Transformers and generative models, for automated plant disease detection and classification using visual data (e.g., leaf images). The paper meticulously outlines the standard pipeline, encompassing data acquisition, preprocessing, model training, and deployment. Furthermore, it highlights critical challenges such as the need for large, curated, and diverse datasets, model generalisation across different environmental conditions, and the path towards real-world deployment in the form of AI-powered Plant Doctor systems. Finally, future research directions, including the integration of multimodal data and explainable AI, are critically discussed. Findings show that deep learning is poised to revolutionise plant disease management, enabling precise, rapid, and scalable diagnostics for farmers worldwide.


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