Penerapan Algoritma Support Vector Machine Untuk Mendeteksi Penyakit Daun Jagung Berdasarkan Citra Daun
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Abstract
This study aims to develop an accurate and efficient image-based model for corn leaf disease detection to address the limitations of manual disease identification in agricultural practices. The proposed approach employs a supervised learning method using Support Vector Machine (SVM) combined with color feature extraction in the Hue, Saturation, and Value (HSV) color space and texture features based on the Gray-Level Co-occurrence Matrix (GLCM). The dataset used in this study was obtained from the PlantVillage and PlantVillage+PlantDoc datasets, consisting of 4,188 corn leaf images categorized into four classes: blight, gray leaf spot, common rust, and healthy leaves. The dataset was divided into training and testing sets with an 80:20 ratio. Experimental results demonstrate that the proposed SVM model achieved an accuracy of 92.4%, with an average precision of 91.8%, recall of 91.2%, and F1-score of 91.5%. These results indicate that the combination of color and texture features effectively represents visual disease characteristics in corn leaves. This study confirms that an optimized SVM model remains a relevant and computationally efficient solution for precision agriculture applications with limited computational resources.
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