A Novel Approach for Weed Identification using Deep Learning

Authors:

Nagesh Vadaparthi, Professor, Information Technology, MVGR College of Engineering, Vizianagaram.

Pandiri Krishna Kishore, Information Technology, MVGR College of Engineering, Vizianagaram.

Vadigalla Sulochana, Information Technology, MVGR College of Engineering, Vizianagaram.

Vadali Lalitha, Information Technology, MVGR College of Engineering, Vizianagaram.

Vaddadi Manikanta, Information Technology, MVGR College of Engineering, Vizianagaram.

Abstract -Weed detection is essential in modern agriculture as weeds compete with crops for vital resources, reducing yield and quality. Traditional methods like manual removal and excessive herbicide use are time-consuming, costly, and harmful to the environment. This research focuses on applying deep learning techniques for efficient weed detection. Convolutional Neural Networks (CNNs) are used to classify and differentiate between crops and weeds from field images. The model is trained on annotated datasets to ensure accurate learning. It is optimized to perform well under varying conditions such as lighting, background complexity, and growth stages. The proposed approach improves detection accuracy compared to traditional image processing methods. It also enables real-time weed identification in agricultural fields. This system supports precision farming by enabling targeted weed control. Overall, it reduces herbicide usage, lowers costs, and promotes sustainable agricultural practices.

Keywords: Weed Detection, Deep Learning, Convolutional Neural Network (CNN), Image Classification, Precision Agriculture, Computer Vision, Agricultural Image Processing, Feature Extraction