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

作     者：Kundu, Shreyan Mukhopadhyay, Souradeep Talukdar, Rahul Das, Semanti Adhikari, Subhajit 

作者机构：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 Computer Science and Automation Indian Institute of Science CV Raman Road Karnataka Bangalore 560012 India 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 Department of Lifescience Christ University Hosur Main Road Bhavani Nagar Karnataka Bangalore 560029 India Department of Information Technology RCCIIT KOLKATA West Bengal Kolkata 700015 India 

出 版 物：《Iran Journal of Computer Science》 (Iran J Comput Sci)

年 卷 期：2025年第8卷第2期

页      面：365-378页

主　　题：Augmentation Contrastive learning Supervised learning Triplet loss 

摘      要：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 model’s 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.