Enhanced weed and crop species classification using optimized machine learning and ensemble techniques

Abstract Accurate identification of weed and crop species is essential for implementing precision agriculture practices that minimize chemical usage and improve yield. This study presents an advanced machine learning framework that employs optimized classification techniques to distinguish between multiple plant species based on image features. Utilizing a dataset comprising 11,500 images from diverse agricultural classes—including crops such as maize and sugar beet, and weeds like thistle and wild oat—the framework extracts color, texture, and shape features for high-resolution pattern recognition. Several supervised machine learning algorithms, including eXtreme gradient boosting, light gradient boosting machine, and ensemble stacking methods, are explored and fine-tuned using feature selection techniques such as recursive feature elimination (RFE) and principal component analysis (PCA). The experimental results demonstrate superior performance, with the best models achieving classification accuracy of up to 96.1% and reduced inference times suitable for real-time agricultural applications. These findings highlight the potential of optimized machine learning pipelines in advancing sustainable weed management and automated field monitoring.