Klasifikasi Citra Penyakit Daun Anggur Menggunakan Radial Basis Function Neural Networks
DOI:
https://doi.org/10.47065/bulletincsr.v4i5.324Keywords:
Grape Leaf Disease; RBFNN; Mean Color; GLCM; Image ClassificationAbstract
Grapes are one of the important horticultural commodities in Indonesia, but their productivity is often disrupted by leaf diseases that affect both the quality and quantity of the harvest. Manual disease identification remains time-consuming, requires specialized expertise, and often results in inconsistent diagnoses. Challenges such as fatigue, varying levels of experience, and visual differences in leaf diseases hinder the ability to perform fast and accurate diagnosis. Therefore, this study focuses on developing an automatic grape leaf disease classification model using the Radial Basis Function Neural Networks (RBFNN) algorithm. The model utilizes color feature extraction through Mean Color to detect changes in the color distribution of infected leaves, as well as GLCM (Gray Level Co-occurrence Matrix) to analyze texture patterns that serve as disease indicators. The classification process is conducted by the RBFNN algorithm, which calculates the distance between inputs and neuron centers using radial basis functions in the hidden layer. The results of this study show that the model is capable of classifying grape leaf diseases with an overall accuracy of 92.5%, indicating that the model is highly effective in detecting and classifying both healthy and infected leaves with minimal errors.
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