INTELLIGENT WEED DETECTION IN SMART AGRICULTURE USING FUZZY INFERENCE AND IMAGE PROCESSING

Abstract

Weed detection through image processing is an emerging and rapidly advancing field with the potential to revolutionize modern agriculture. This technology enables farmers to accurately identify and monitor weed growth, facilitating targeted and efficient weed control practices. This study presents the development of an image acquisition and processing system, integrated with a fuzzy logic–based decision-making platform, to determine the appropriate pesticide dosage and application areas within agricultural fields.

MATLAB was employed as the processing environment for analyzing field images and identifying grassy weed regions. The fuzzy inference system utilizes weed coverage and patch values, supported by membership functions, to guide herbicide application rates for specific field zones. By enabling precision targeting, this approach reduces chemical overuse, enhances sustainability, and supports healthier agricultural practices.

With growing global food demand and increasing pressure on natural resources, sustainable farming methods are essential. The findings of this work highlight the potential of combining image processing with fuzzy logic as a practical, intelligent solution for advancing smart and sustainable agriculture.