A systematic Study on the Usage of Various Attention Mechanisms for Plant Leaf Disease Detection

• Published in: 2024 Asian Conference on Intelligent Technologies (ACOIT) pp. 1 - 6
• Main Authors: Vallabhajosyula, Sasikala, Sistla, Venkatramaphanikumar, Kolli, Venkata Krishna Kishore
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.09.2024
• Subjects: Agriculture; Attention; Attention mechanisms; Computer vision; Convolutional Neural Networks; Deep learning; MobilenetV2; Plant leaf disease detection; Reviews; Surveys; Systematics; Training; Transformers; Vision Transformer; Visualization
• ISBN: 9798350374933
• DOI: 10.1109/ACOIT62457.2024.10940036

Plant diseases significantly impact the agriculture sector, resulting in substantial productivity and economic losses. Effective plant health monitoring systems are crucial for sustainable agriculture, and predicting various diseases is a critical task. This work aims to provide accessible and informative visual data to farmers, enabling proactive decision-making and timely action. Deep learning models, particularly vision transformers with attention mechanisms, have performed well in numerous computer vision tasks. This comprehensive review provides an in-depth exploration of attention mechanisms in vision transformers, examining their role in enhancing image recognition, object detection, and other computer vision tasks. In this work, customized vision transfer with MobileNet as a classifier is proposed. Furthermore, it discusses future research directions and presents findings from extensive training and evaluation on diverse datasets, revealing that multi-head attention blocks significantly improve accuracy, outperforming other attention mechanisms.