Crop Diseases Identification Using Deep Learning in Application

Abstract

This comprehensive review paper explores the profound impact of deep learning in the context of agriculture, with a specific focus on its crucial role in crop disease analysis and management. Deep learning techniques have exhibited remarkable potential to revolutionize agricultural practices, enhancing efficiency, sustainability, and resilience. The introductory section sets the stage by emphasizing the significant role of deep learning in agriculture, offering insights into its transformative applications, including disease detection, yield prediction, precision agriculture, and resource optimization. Subsequent sections delve into the fundamental aspects of deep learning, beginning with an exploration of its relevance and its practical implementations in crop disease detection. These discussions illuminate the essential techniques and methodologies that drive this technology, stressing the critical importance of data quality, model generalization, computational resources, and cost considerations. The paper also addresses ethical and environmental concerns, emphasizing the imperative of responsible and sustainable deep learning applications in agriculture. Furthermore, the document outlines the limitations and challenges faced in this field, encompassing data availability, ethical considerations, and computational resource accessibility, offering valuable insights for future research and development. This paper underscores the immense potential of deep learning to revolutionize agriculture by improving disease management, resource allocation, and overall sustainability. While persistent challenges exist, such as data quality and accessibility, the promise of harnessing deep learning to address global food security challenges is exceptionally encouraging. This comprehensive review serves as a foundational resource for ongoing research and innovation within the agricultural domain.