j. inst. sci. and tech.

Journal of the Institute of Science and Technology

2536-4618

Igdir University

10.21597/jist.1283491

Computer Software

Bilgisayar Yazılımı

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

https://orcid.org/0000-0003-3037-5586

Işık

Gültekin

IĞDIR ÜNİVERSİTESİ

09 01 2023

13 3 1482 1495 04 14 2023 05 05 2023

2011

Journal of the Institute of Science and Technology

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 (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 (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.

Few-shot learning Meta-learning Attention mechanisms Plant diseases Deep learning

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