THE USE OF VISION TRANSFORMERS FOR TOMATOES PLANT DISEASE DETECTION: A REVIEW

Early detection and diagnosis of plant diseases are crucial for effective disease management and prevention. This study proposes the use of vision transformers for plant diseases detection, most especially tomato plant. The proposed approach tend to conglomerate two of more vision transformers, each trained on a different subset of the data, to improve overall performance and efficacy. The methodology used for this study is EcoGreenVit. It will be used because of its ability to Transfer Learning with Pre-trained ViTs and also has the ability to incorporate multispectral data and is crucial for deploying these model on resource constrained devices such as mobile phones and edge devices used in precision agriculture. This study contributes to the development of accurate, robust, and explainable deep learning models for plant diseases detection, paving the way for the creation of decision-support systems that can aid farmers, agricultural experts, and policymakers in mitigating the impact of plant diseases on global food security.