REVOLUTIONIZING AGRICULTURE: MACHINE AND DEEP LEARNING SOLUTIONS FOR ENHANCED CROP QUALITY AND WEED CONTROL
DOI:
https://doi.org/10.5281/zenodo.19510480Keywords:
Machine Learning, Deep Learning, Precision Agriculture, Crop Quality, Weed Detection, Convolutional Neural Networks (CNN), Image Processing, Smart Farming, Sustainable Agriculture, Predictive AnalyticsAbstract
Revolutionizing agriculture through the integration of machine learning and deep learning technologies offers a transformative approach to improving crop quality and effective weed control. Traditional agricultural practices often rely on manual monitoring and generalized treatments, which can lead to inefficiencies, increased costs, and environmental impact due to excessive use of fertilizers and herbicides. This project proposes an intelligent agricultural system that leverages advanced computational techniques to analyze crop conditions, detect weeds, and optimize farming decisions. Machine learning algorithms such as Random Forest and Support Vector Machines are utilized for classification and prediction tasks, while deep learning models, particularly Convolutional Neural Networks (CNNs), are employed for image-based crop health analysis and weed detection. The system processes data from sources such as satellite imagery, drones, and field sensors to identify patterns related to plant health, soil conditions, and weed growth. By enabling precise identification of weeds, the system supports targeted herbicide application, reducing chemical usage and promoting sustainable farming practices. Additionally, predictive models assist farmers in making informed decisions regarding irrigation, fertilization, and harvesting, thereby enhancing crop yield and quality. The proposed approach also incorporates real-time monitoring and visualization tools, allowing users to track field conditions and receive actionable insights. Experimental results indicate improved accuracy in weed detection and crop classification compared to traditional methods. Overall, this research contributes to the development of smart agriculture solutions that increase productivity, reduce environmental impact, and support precision farming. The integration of machine learning and deep learning technologies paves the way for a more efficient, data-driven, and sustainable agricultural ecosystem.
