Intelligent Weed Detection for Sustainable Agriculture Using Deep Learning

• Author(s): Pranesh S J ; Narendhar Kumar S ; Kalai Varshaa G R ; Dr. V Anandhkumar
• Paper ID: 1707733
• Page: 90-98
• Published Date: 04-04-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 10 April-2025

Abstract

It is a study to create intelligent farming technology in sustainable weed classification by using a self-operated device that utilises image processing and CNN in proper classification. The system can deploy in real time, making use of the chemical less while easing farm functions. Through lesser use of herbicide, it minimises contamination of soil and water, defending human health as well as non-pesticidal insects, and encouraging natural methods of weeding. Moreover, precision fertilizer management reduces expenses, making sustainable agriculture more feasible. The model combines environmental protection with farm productivity, leading to a robust food supply chain. The model attains 99.3% accuracy, 99% precision, recall, F1 score, and weighted average of 600 for support with visualized performance graphs.

Keywords

CNN (Convolutional Neural Network), Accuracy, Precision, Recall, F1 Score, machine learning, Image processing.

Citations

IRE Journals:
Pranesh S J , Narendhar Kumar S , Kalai Varshaa G R , Dr. V Anandhkumar "Intelligent Weed Detection for Sustainable Agriculture Using Deep Learning" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 90-98

IEEE:
Pranesh S J , Narendhar Kumar S , Kalai Varshaa G R , Dr. V Anandhkumar "Intelligent Weed Detection for Sustainable Agriculture Using Deep Learning" Iconic Research And Engineering Journals, 8(10)