AgroWeedGuard : Smart Weed Detection for a healthier Harvest using CNN and Deep Learning

AgroWeedGuard : Smart Weed Detection for a healthier Harvest using    CNN and Deep Learning

   T.Bhavya Sai Keerthi1, T.Bhavana Sai Sneha2, K.Yuva Surya Bhagavan3 ,M.Lajith4 ,R.Rajender5

[1-4] B. Tech Student , 5Assistant Professor ,LIET [1,2,3,4,5]Computer Science and System Engineering, Lendi Institute of Engineering and Technology, Viziangaram.

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Abstract –

Weed infestation negatively impacts crop yields and increases farming costs. AgroWeedGuard is a smart weed detection system that utilizes Convolutional Neural Networks (CNN) and Deep Learning to identify and classify weeds in agricultural fields. The model processes real-time images to distinguish weeds from crops with high accuracy. By automating weed detection, it reduces reliance on manual labor and minimizes excessive herbicide use, promoting sustainable farming. Experimental results validate the system’s efficiency in diverse field conditions, demonstrating its potential to enhance agricultural productivity and ensure healthier harvests.

Key Words:  Image Processing, Machine Learning, Object Detection, Deep Learning, Convolutional Neural Networks and Real-Time Analysis,YOLO V5.