首页|Study Findings from University of Douala Broaden Understanding of Support Vector Machines (A robust segmentation method combined with classification algorithms for field-based diagnosis of maize plant phytosanitary state)
Study Findings from University of Douala Broaden Understanding of Support Vector Machines (A robust segmentation method combined with classification algorithms for field-based diagnosis of maize plant phytosanitary state)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on have been published. Acc ording to news reporting from the University of Douala by NewsRx journalists, re search stated, "Early diagnosis of maize-plant phytosanitary state in the field is crucial to prevent crop damage and optimize yield." Our news correspondents obtained a quote from the research from University of Do uala: "However, this field diagnosis presents a challenge due to the variable ba ckground of the field environment, which can hinder the performance of classific ation algorithms. In this article, we introduced a novel segmentation technique using a combined normalized difference vegetation index that effectively isolate s the features of interest, such as the leaves, from the surrounding image, whic h includes the diverse field background. To assess the effectiveness of our segm entation approach, we conducted early diagnosis of maize plants in the field usi ng supervised classification algorithms. We generated a dataset that incorporate d four essential texture features: energy, entropy, contrast, and inverse. These features were extracted from each of the segmented images using grayscale co-oc currence matrices."
University of DoualaAlgorithmsEmergi ng TechnologiesMachine LearningSupport Vector MachinesVector Machines