Version 1
: Received: 5 December 2023 / Approved: 6 December 2023 / Online: 6 December 2023 (08:01:56 CET)
How to cite:
Inamdar, R.; Bhalerao, S.R.; Shinde, G.U.; Gupta, O. Precision Pest and Disease Detection System using Agricultural Robot. Preprints2023, 2023120314. https://doi.org/10.20944/preprints202312.0314.v1
Inamdar, R.; Bhalerao, S.R.; Shinde, G.U.; Gupta, O. Precision Pest and Disease Detection System using Agricultural Robot. Preprints 2023, 2023120314. https://doi.org/10.20944/preprints202312.0314.v1
Inamdar, R.; Bhalerao, S.R.; Shinde, G.U.; Gupta, O. Precision Pest and Disease Detection System using Agricultural Robot. Preprints2023, 2023120314. https://doi.org/10.20944/preprints202312.0314.v1
APA Style
Inamdar, R., Bhalerao, S.R., Shinde, G.U., & Gupta, O. (2023). Precision Pest and Disease Detection System using Agricultural Robot. Preprints. https://doi.org/10.20944/preprints202312.0314.v1
Chicago/Turabian Style
Inamdar, R., Gopal U Shinde and Omkar Gupta. 2023 "Precision Pest and Disease Detection System using Agricultural Robot" Preprints. https://doi.org/10.20944/preprints202312.0314.v1
Abstract
The agricultural industry places a high priority on crop protection, especially when it comes to protecting cotton crops against pink bollworm infestations. Rapid disease identification is essential for effective crop management at the same time. In order to overcome these difficulties, this study offers a thorough process for utilizing the Robot Operating System (ROS) to deploy an autonomous robot. The robot is intended to identify infestations of pink bollworm in addition to counting and tracking red Disease that impact cotton harvests. Drones are used to check the condition of cotton fields, and the autonomous robot's path planning component is closely related to this process. Large fields are photographed by these drones in multispectral mode, and the robot's course planning technique is done by calculating the Normalized Difference Vegetation Index (NDVI) based on these images. This approach ensures targeted surveillance and intervention, optimizing the use of resources. A customized dataset was created especially for this application to improve the robot's detecting abilities. The dataset was utilized to train a YOLOV8 model, a state-of-the-art object detection architecture. Performance characteristics of the trained model are impressive; it has a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. These metrics highlight how well the model works to precisely locate and measure interesting occurrences in the cotton fields. In order to address the particular challenges presented by pink bollworm infestations and crop diseases in the context of cotton cultivation, this research contributes a comprehensive solution for autonomous crop monitoring and protection by seamlessly integrating ROS, drones, NDVI calculations, and a robust detection model.
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.
