Farmer's Solution (Weed Detection Using Machine Learning)
Abstract
Abstract— This paper presents a machine learning-based approach for automated weed detection in agricultural fields. Weed control plays a crucial role in improving crop yield and minimizing the use of herbicides. Using Convolutional Neural Networks (CNN), the model classifies images of crops and weeds such as broadleaf, grass, soil, and soybean. The proposed model is integrated with a Flask-based web interface for real-time prediction, allowing farmers and researchers to upload or capture field images for instant weed detection. Keywords: Weed Detection, Convolutional Neural Networks, Flask, Machine Learning, Image Classification
Fields of Study & Subjects
Smart Agriculture and AI
Innovations in Aquaponics and Hydroponics Systems
Plant Disease Management Techniques
Publication details
- Journal / source
- INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
- Volume
- 10
- Issue
- 01
- Pages
- 1–9
- Publication date
- 6 January 2026