Implementing Innovative Weed Detection Techniques for Environmental Sustainability

Title

Implementing Innovative Weed Detection Techniques for Environmental Sustainability

Creator

Sreeja K.A.; Pradeep A.; Arshey M.; Warrier G.S.; Ragesh K.T.

Description

Agriculture, supporting over half of India's population, grapples with the challenge of weed control. Current methods applied in plantation crops lack efficiency and pose environmental and health risks. This paper advocates a paradigm shift, emphasizing the critical need for effective weed detection using cluttered unmanned aerial vehicle (UAV) images. The research methodology integrates image processing, Mask R-Convolutional Neural Networks (R-CNN), and Internet of Things (IoT). A dataset of 200 UAV images was subjected to a thorough preprocessing. In the initial phase, weeds and crops were identified with precision employing an UAV-tailored Mask R-CNN with instance segmentation. This was found to surpass traditional methods in terms of communication between the model and the agricultural environment. For timely decision-making, real-time data were collected using IoT. Average Precision (AP) values reveal high accuracy, notably 89.1% for weeds, 88.9% for crops, and an overall precision of 89.4%. The Mask R-CNN network segments and classifies images, marking weed zones communicated to farmers via Raspberry Pi with a GSM module, enabling real-time alerts and informed decision-making for efficient weed control. This holistic approach, providing object classifications, detailed bounding boxes, and masks, addresses weed control challenges, highlighting the transformative potential of advanced technologies in agriculture. 2024, Institute for Environmental Nanotechnology. All rights reserved.

Source

Journal of Environmental Nanotechnology, Vol-13, No. 4, pp. 287-294.

Date

2024-01-01

Publisher

Institute for Environmental Nanotechnology

Subject

Agriculture; IoT integration; Mask R-CNN; Precision; UAV images; Weed control

Coverage

Sreeja K.A., Department of Electronics and Communication Engineering, SCMS School of Engineering and Technology, KL, Karukutty, India; Pradeep A., Department of Electronics and Communication Engineering, Saveetha Engineering College, TN, Chennai, India; Arshey M., Department of Computer Science and Engineering, Jain University, Infopark, KL, Kochi, India; Warrier G.S., Department of Computer Science, Christ University, KA, Bengaluru, India; Ragesh K.T., Department of Agricultural Engineering, Saveetha Engineering College, TN, Chennai, India

Rights

Restricted Access

Relation

ISSN: 22790748

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.13074/jent.2024.12.243947

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216617064&doi=10.13074%2fjent.2024.12.243947&partnerID=40&md5=02b72f29d6d3fb29ca19b9ebbae3dfac