Work

TWFSL-MM: Few-Shot Learning using Meta-Learning and Metric-Learning for Disease Detection in Azadirachta Indica

Year: 2025

Type: article

Abstract: Few-Shot Learning (FSL) is one of the emerging and promising approaches used in machine learning for image classification and prediction. This work proposes a Two-Way Five-Shot Learning with Meta-learning and Metric-learning (TWFSL-MM) model that can detect plant diseases with limited data, reducing the cost of implementation and improving the quality of Azadirachta Indica. The proposed method addresses the drawbacks of FSL by employing meta-learning and metric-learning approaches. Experimental results showed that the proposed model achieved an accuracy of 92.09%, an average loss of 0.18, an average precision of 0.94, a recall of 0.93, and an F1 score of 0.93. FSL is a promising strategy for plant disease detection, achieving higher accuracy with a limited dataset. The TWFSL-MM model outperforms other state-of-the-art models, demonstrating its potential to improve crop yields and quality.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.9886

Authors: H. A. Vidya, M. S. Narasimha Murthy

Institution: Visvesvaraya Technological University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $431