Abstract: The integration of IoT, automation, and advanced technologies such as artificial intelligence (AI) and deep learning has sparked a significant transformation in modern agriculture. In particular, the utilization of deep learning techniques, notably convolutional neural networks (CNNs), has emerged as a promising approach for disease detection in crops. This paper presents a comprehensive review of recent advancements in using deep learning, specifically focusing on the application of convolutional neural networks like VGG-16 in identifying plant diseases from leaf images. By harnessing the power of deep learning and leveraging tools like PyTorch, this study aims to revolutionize disease identification processes in agriculture. The automatic learning and feature extraction capabilities of deep learning offer a more objective and efficient means of detecting plant diseases compared to traditional methods. Moreover, the implementation of deep learning in agricultural settings promises faster and more accurate detection, enabling timely interventions for disease management. The review also addresses current challenges and future directions in the field, providing valuable insights for researchers and practitioners working on disease detection and pest control in agriculture.
Keywords: - Convolutional neural network, Deep neural networks, Deep structured learning, Machine learning.
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DOI:
10.17148/IJARCCE.2024.13495
