ItemConference Paper

HRL-ViT: Human-Robot Collaborative Vision Transformer for AIoT-Enabled Leaf Disease Detection in Precision Agriculture

Title
HRL-ViT: Human-Robot Collaborative Vision Transformer for AIoT-Enabled Leaf Disease Detection in Precision Agriculture
Creator
Champatiray, Chiranjibi; Samal, Sonali; Gadekellu, Thippa Reddy; Srivastava, Gautam; Bahubalendruni, Mva Raju
Description
The combination of artificial intelligence and Internet of Things (AIoT) technologies is changing precision agriculture by making it possible to automatically check the health of crops. Early detection of leaf diseases is still important for stopping yield losses, but regular convolutional neural networks (CNNs) often don't work as well when they have to deal with different textures, lighting changes, and noise on the field level. To address these constraints, this study presents HRL-ViT, a Human-Robot Collaborative Learning framework that utilizes Vision Transformers for leaf disease identification. The frame-work merges the global attention feature of Vision Transformers with a human-in-the-loop approach, wherein predictions with low confidence are validated by experts and used to improve the model over time. The system is also made for edge-based AIoT deployment, which lets you analyze data in real time in agricultural settings. Experimental research utilizing both benchmark datasets and field-acquired images demonstrates that HRL-ViT consistently surpasses baseline CNN and Transformer models, attaining superior accuracy, precision, and recall while minimizing false detections. Transformers' attention maps can be visualized to make them even easier to understand, which helps users trust them and make decisions. In general, HRL-ViT shows a lot of promise for use in autonomous robotic platforms. It offers an explainable and scalable way to find diseases in precision agriculture. 2025 IEEE.
Source
Proceedings - 2025 IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
AIoT; Edge intelligence; Explainable AI; Human-robot collaboration; Leaf disease detection; Precision agriculture; Vision Transformer (ViT)
Coverage
Champatiray C., Christ University, Department of Mechanical & Automobile Engineering, Bengaluru, India; Samal S., Alliance University, Dept. of Computer Science & Engineering, Bengaluru, India; Gadekellu T.R., College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China, Lovely Professional University, Division of Research and Development, Phagwara, India; Srivastava G., Brandon University, Department of Mathematics and Computer Science, MB, Canada; Bahubalendruni M.V.A.R., NIT Puducherry, Dept. of Mechanichal Engineering, Puducherry, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833156634-0;
Format
online
Language
English
Type
Conference paper

Citation

Champatiray, Chiranjibi; Samal, Sonali; Gadekellu, Thippa Reddy; Srivastava, Gautam; Bahubalendruni, Mva Raju, “HRL-ViT: Human-Robot Collaborative Vision Transformer for AIoT-Enabled Leaf Disease Detection in Precision Agriculture,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/25794.

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