Enhancing Plant Disease Detection through Transfer Learning by Incorporating MemoryAugmented Networks and Meta-Learning Approaches

Abstract

Transfer learning has revolutionized automated plant disease detection by leveraging pre-trained convolutional neural networks (CNNs) on large-scale datasets like ImageNet. This paper explores advanced methodologies in transfer learning, focusing on the integration of memory-augmented networks and meta-learning approaches. These enhancements aim to improve model adaptation to new disease types and environmental conditions, thereby enhancing accuracy and robustness in agricultural applications. The paper reviews existing literature, discusses methodologies, and suggests future research directions to advance the field of AI-driven plant pathology.