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

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.