Enhancing Plant Disease Detection through Deep Learning

Abstract

Progress in the field of deep learning has displayed significant potential in transforming the detection of plant diseases, presenting automated and efficient approaches to support agricultural processes. This investigation explores the major obstacles associated with the detection of plant diseases, emphasizing constraints related to data availability, generalization across different plant species, variability in environmental conditions, and the need for model interpretability in the realm of deep learning. The deficiency and imbalanced distribution of labeled data present considerable challenges when training accurate disease identification models. Moreover, the necessity for broad generalization across various plant species underscores the requirement for versatile models capable of proficiently identifying diseases in a diverse range of plants. Variability in environmental conditions introduces inconsistencies and noise in the data, underscoring the significance of resilient models that can adapt to diverse conditions. Additionally, ensuring that deep learning models are interpretable and explainable is crucial for establishing user confidence and enabling informed decisionmaking. This research aims to propose innovative methodologies and approaches to alleviate these obstacles and advance plant disease detection. By tackling these challenges, our objective is to contribute to the creation of dependable, interpretable, and widely applicable deep learning solutions, ultimately promoting sustainable agriculture and enhancing food security