Comparison of machine learning techniques for identifying weeds in maize crops

Abstract

ABSTRACT The present study was conducted at the Federal Rural University of Rio de Janeiro, in the years 2024 and 2025, to evaluate the performance of three supervised classifiers - maximum likelihood, random forest, and support vector machine - for spectral discrimination between maize (Zea mays L.) and the weed (Cyperus rotundus), considering different phenological stages and two cropping seasons (main and second crop), using multispectral images acquired by remotely piloted aircraft. Overall accuracy, as well as class metrics of precision, recall, and F1-score, were used to evaluate the performance of the algorithms in classifying four targets: maize, weed, soil, and shadow. The performance of the classifiers improved as the phenological stage progressed. In the first year (second crop), the V8 phenological stage showed the greatest differentiation between the plants, and the highest-performing classifier was maximum likelihood, with an overall accuracy of 89%. For the maize class, precision was 0.88, recall was 0.81, and F1-score was 0.85, and for the weed class, precision was 0.68, recall was 0.89, and F1-score was 0.77. In the second year (main crop), the best differentiation occurred at the V6 stage, and the maximum likelihood classifier again presented the best performance, with an overall accuracy of 88%, precision of 1.00, recall of 0.78, and F1-score of 0.88 for the maize class, and precision of 0.52, recall of 1.00, and F1-score of 0.68 for the weed class. Thus, across the years analyzed, the maximum likelihood classifier performed best.