ItemArticle

Just: towards jute pest classification by combination of supervised learning and triplet loss aided contrastive learning

Title
Just: towards jute pest classification by combination of supervised learning and triplet loss aided contrastive learning
Creator
Kundu, Shreyan; Mukhopadhyay, Souradeep; Talukdar, Rahul; Das, Semanti; Adhikari, Subhajit
Description
Jute is a vital agricultural commodity contributing significantly to the GDP of countries like Bangladesh, India, Myanmar, and China. However, because of its inaccuracy and slowness, its vulnerability to pest infestations-which are often handled by manual inspections-poses serious cost concerns. This study suggests a unique method for early and accurate pest identification that combines contrastive and supervised learning. Contrastive learning enhances feature representation by distinguishing between positive and negative samples, ensuring that instances within the same class are closely grouped while maintaining separation between different classes. It reduces false negatives by classifying some samples as negative and others with the same label as positive. Supervised learning enables precise pest identification by aligning features with distinctive characteristics of each class. Metrics including precision, recall, F1 score, ROC curve, and confusion matrix are used to assess the hybrid models performance; the findings show notable accuracy gains over conventional techniques. This scalable and dependable solution lowers losses caused by pests and provides a sustainable method of growing jute using cutting-edge advanced machine-learning techniques. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Source
Iran Journal of Computer Science;Volume;8;Issue;2;pp.365-378
Date
01-01-2025
Publisher
Springer International Publishing
Subject
Augmentation; Contrastive learning; Supervised learning; Triplet loss
Coverage
Kundu S., Computer Science and Engineering (Artificial Intelligence), Institute of Engineering and Management, Iem Management Building, D-1, Street No. 13, EP Block, Sector V, Bidhannagar, West Bengal, Kolkata, 700091, India; Mukhopadhyay S., Computer Science and Automation, Indian Institute of Science, CV Raman Road, Karnataka, Bangalore, 560012, India; Talukdar R., Computer Science and Engineering (core), Institute of Engineering and Management, Iem Management Building, D-1, Street No. 13, EP Block, Sector V, Bidhannagar, West Bengal, Kolkata, 700091, India; Das S., Department of Lifescience, Christ University, Hosur Main Road, Bhavani Nagar, Karnataka, Bangalore, 560029, India; Adhikari S., Department of Information Technology, RCCIIT KOLKATA, West Bengal, Kolkata, 700015, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 25208438;
Format
online
Language
English
Type
Article

Citation

Kundu, Shreyan; Mukhopadhyay, Souradeep; Talukdar, Rahul; Das, Semanti; Adhikari, Subhajit, “Just: towards jute pest classification by combination of supervised learning and triplet loss aided contrastive learning,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22109.

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