Comparative evaluation of classical machine learning and deep learning models for early weed detection in precision agriculture

Unless treated and controlled early, weeds considerably impede the growth of crops as they compete over vital factors (resources) like nutrients, water, and light. To overcome this problem, an automated weed species early detection and classification system using machine learning-based image analysis is suggested. The framework integrates classical machine learning algorithms Support Vector Machines (SVM), Random Forests and k-Nearest Neighbors (k-NN), using handcrafted features like texture, shape, and color with deep learning models, Convolutional Neural Networks (CNNs) which automatically learn discriminative features in the data. The study comparatively evaluates classical ML and deep learning models. In order to test the experiments, a publicly available Sugar Beet dataset was used, as well as a custom maize seedling dataset. To enhance generalization and robustness, some preprocessing steps were performed, including normalization, background removal, and augmentation. Common metrics used to assess the performance of the models are F1-score and Intersection over Union (IoU). The experimental results show deep learning models outperforming the traditional machine learning methods, especially CNNs The CNN model also exhibited a classification accuracy of 98.7%. These results demonstrate the promise of deep learning to quickly, reliably, and in large scale detect weeds in the early stages of precision agriculture. This study highlights the feasibility of machine learning systems in proactive management of weed as well as in supporting healthy crop growth at the early stages of development.