Advanced Machine And Deep Learning Approaches For Improving Crop Quality And Weed Management In Agriculture
DOI:
https://doi.org/10.46647/rdems0204034Keywords:
Precision Agriculture, Machine Learning, Deep Learning, Crop Quality Assessment, Weed Detection, Computer Vision, Convolutional Neural Networks (CNN), Image Processing, Smart Farming, Agricultural Automation, Yield Optimization, Decision Support Systems, Remote Sensing, IoT in Agriculture, Sustainable Farming.Abstract
Agriculture faces increasing challenges from climate change, rising global food demand, and the need to minimize chemical usage while maximizing crop quality. This paper presents machine learning (ML) and deep learning (DL) approaches for enhancing crop quality assessment and controlling weed growth. By leveraging computer vision, precision agriculture sensors, and predictive analytics, the system can detect weeds in real time, classify crop health, and recommend optimized interventions. Experimental results demonstrate higher weed detection accuracy compared to traditional methods, reduced chemical wastage, and improved crop yield predictions. The integration of AI-based solutions has the potential to revolutionize modern agriculture by making it sustainable, cost-effective, and environmentally friendly.