Machine Learning Based Weed Detection System

Abstract

This abstract underscores the importance of weed detection in crop cultivation to prevent plant diseases and minimize crop losses. To address these challenges and promote eco-friendly practices, the authors propose a weed detection program employing K-Nearest Neighbors, Random Forest, Decision Tree algorithms, and the YOLOv5 neural network. The abstract also provides a concise overview of existing research in weed identification using machine learning and deep learning. The authors developed a YOLOv5-based weed detection system and evaluated the performance of the algorithm, showing traditional classifiers achieve accuracies of 83.3%, 87.5%, and 80%, while the neural network scores range from 0.82 to 0.92 for each class. The study demonstrates the effectiveness of this approach in classifying low-resolution weed images.