Version 1
: Received: 5 December 2023 / Approved: 6 December 2023 / Online: 6 December 2023 (08:01:56 CET)
Version 2
: Received: 4 September 2024 / Approved: 5 September 2024 / Online: 5 September 2024 (08:20:49 CEST)
How to cite:
Inamdar, R.; Bhalerao, S. R.; Shinde, G. U.; Gupta, O. Precision Pest and Disease Monitoring System using Agricultural Robot. Preprints2023, 2023120314. https://doi.org/10.20944/preprints202312.0314.v2
Inamdar, R.; Bhalerao, S. R.; Shinde, G. U.; Gupta, O. Precision Pest and Disease Monitoring System using Agricultural Robot. Preprints 2023, 2023120314. https://doi.org/10.20944/preprints202312.0314.v2
Inamdar, R.; Bhalerao, S. R.; Shinde, G. U.; Gupta, O. Precision Pest and Disease Monitoring System using Agricultural Robot. Preprints2023, 2023120314. https://doi.org/10.20944/preprints202312.0314.v2
APA Style
Inamdar, R., Bhalerao, S. R., Shinde, G. U., & Gupta, O. (2024). Precision Pest and Disease Monitoring System using Agricultural Robot. Preprints. https://doi.org/10.20944/preprints202312.0314.v2
Chicago/Turabian Style
Inamdar, R., Gopal U Shinde and Omkar Gupta. 2024 "Precision Pest and Disease Monitoring System using Agricultural Robot" Preprints. https://doi.org/10.20944/preprints202312.0314.v2
Abstract
The agricultural industry prioritizes crop protection, particularly against threats like pink bollworm infestations in cotton crops. This paper presents a comprehensive solution using the Robot Operating System (ROS) to deploy an autonomous robot for detecting pink bollworm infestations and tracking red illnesses. Drones collect multispectral photos of cotton fields, aiding path planning via Normalized Difference Vegetation Index (NDVI) computations. A bespoke dataset of RGB and multispectral images trained a YOLOv8 model for object detection, achieving a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. The robot was tested in two separate cotton fields, with rows around 20 meters long and the camera at 60 cm height for optimal real-time detection. The findings, compared to hand counts and confirmed against NDVI estimates, showed an average accuracy of 76.04% for B.T. Cotton and 67.54% for Organic Cotton fields. This study demonstrates the effectiveness of the proposed system in accurately detecting and managing crop threats. Combining ROS, drones, NDVI calculations, and a robust detection model, it provides a scalable approach for autonomous crop monitoring and protection, with the potential for broader agricultural adoption.
Keywords
Object tracking and Counting; Precision Agriculture; Pink Bollworm; ROS; UGVs
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
