An Intelligent System for Improving Crop Quality and Detecting Weeds Using Machine and Deep Learning

Abstract

Agriculture plays a vital role in ensuring food security and sustainable development, where
maintaining high crop quality and controlling weed growth are critical challenges. Traditional
methods of crop monitoring and weed management are often labor-intensive, timeconsuming,
and less efficient. To address these issues, this study proposes an intelligent
system for improving crop quality and detecting weeds using Machine Learning and Deep
Learning techniques. The system analyzes agricultural data and plant images to identify crop
conditions and detect the presence of weeds at an early stage. Advanced image processing
methods combined with deep learning models such as Convolutional Neural Networks (CNN)
are used to extract meaningful features from crop images, enabling accurate classification of
crops and weeds. Machine learning algorithms further assist in predicting crop health and
recommending appropriate actions to enhance crop quality. The proposed approach helps
farmers monitor fields more effectively, reduce manual effort, and minimize crop loss caused
by weeds. Experimental evaluation demonstrates that the system achieves improved detection
accuracy and efficient crop monitoring compared to traditional approaches. This intelligent
framework contributes to the development of smart agriculture by supporting data-driven
decision-making and promoting sustainable farming practices