A Survey on Deep Learning Approaches for Crop Disease Analysis in Precision Agriculture
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Abstract
Precision agriculture has emerged as a transformative paradigm in modern farming, leveraging advanced technologies to optimize crop management. This paper presents a comprehensive survey of deep learning approaches for crop disease analysis in precision agriculture. The investigation focuses on four key aspects: leaf disease detection through deep learning techniques, leaf shape-based disease analysis, crop weed detection utilizing deep learning methods, and crop damage detection using aerial images. The survey encompasses a review of recent advancements, methodologies, challenges, and future prospects in each of these domains. By exploring the intersection of deep learning and precision agriculture, this paper aims to provide a holistic understanding of the current state-of-the-art and inspire further research initiatives to enhance crop health monitoring and management.
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