Multiclass Plant Leaf Disease Prediction Using Fuzzy Multimodal Feature Extraction
DOI:
https://doi.org/10.46604/aiti.2025.14032Keywords:
modified co-occurrence matrix (MCCM), fuzzy hue saturation value (HSV), local binary pattern (LBP), multi-class support vector machine (MSVM)Abstract
Delayed identification of crop diseases, which significantly impact agricultural yields, remains a critical challenge. Crop diseases are a major factor contributing to reducing productivity. Since leaves are the mirrors of crop health, by investigating the leaves, a prediction of crop health can be made. This study aims to predict crop disease in the vegetative growth phase with greater efficiency. The two most prominent features, color and texture of the leaves, are extracted with different techniques, followed by fuzzification of these features. Two machine learning models, the bootstrap model and the multi-class support vector machine (MSVM), are employed for disease prediction. The findings show that for multi-class disease prediction, the bootstrap model with histogram and modified co-occurrence matrix features obtains a superior average accuracy of 98.07%, while the MSVM with fuzzy features delivers an average accuracy of 80.11% in the potato crop with early blight disease.
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