Supervised learning for crop/weed classification based on color and texture features

TL;DR

This paper explores supervised learning using color and texture features to accurately classify soybean crops and weeds from UAV images, achieving over 96% accuracy, aiding precision agriculture.

Contribution

It introduces a method combining color and texture features with SVM for crop-weed classification, demonstrating high accuracy on UAV imagery.

Findings

Color and LBP features yield over 96% accuracy.

Combining color and texture features improves classification performance.

UAV images effectively support crop and weed discrimination.

Abstract

Computer vision techniques have attracted a great interest in precision agriculture, recently. The common goal of all computer vision-based precision agriculture tasks is to detect the objects of interest (e.g., crop, weed) and discriminating them from the background. The Weeds are unwanted plants growing among crops competing for nutrients, water, and sunlight, causing losses to crop yields. Weed detection and mapping is critical for site-specific weed management to reduce the cost of labor and impact of herbicides. This paper investigates the use of color and texture features for discrimination of Soybean crops and weeds. Feature extraction methods including two color spaces (RGB, HSV), gray level Co-occurrence matrix (GLCM), and Local Binary Pattern (LBP) are used to train the Support Vector Machine (SVM) classifier. The experiment was carried out on image dataset of soybean crop, obtained from an unmanned aerial vehicle (UAV), which is publicly available. The results from the experiment showed that the highest accuracy (above 96%) was obtained from the combination of color and LBP features.