Improving Plant Disease Recognition Through Gradient-Based Few-shot Learning with Attention Mechanisms

Authors : Gültekin IŞIK

Pages : 1482-1495

Doi:10.21597/jist.1283491

View : 83 | Download : 46

Publication Date : 2023-09-01

Article Type : Research Paper

Abstract :This study investigates the use of few-shot learning algorithms to improve classification performance in situations where traditional deep learning methods fail due to a lack of training data. Specifically, we propose a few-shot learning approach using the Almost No Inner Loop insert ignore into journalissuearticles values(ANIL); algorithm and attention modules to classify tomato diseases in the Plant Village dataset. The attended features obtained from the five separate attention modules are classified using a Multi Layer Perceptron insert ignore into journalissuearticles values(MLP); classifier, and the soft voting method is used to weigh the classification scores from each classifier. The results demonstrate that our proposed approach achieves state-of-the-art accuracy rates of 97.05% and 97.66% for 10-shot and 20-shot classification, respectively. Our approach demonstrates the potential for incorporating attention mechanisms in feature extraction processes and suggests new avenues for research in few-shot learning methods.
Keywords : Few shot learning, Meta learning, Attention mechanisms, Plant diseases, Deep learning

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