Leaf disease detection is a critical task in agriculture as it enables early identification and intervention, leading to improved crop yield and reduced economic losses. However, accurate and timely detection of leaf diseases remains a challenging problem due to the complexity of leaf images and the presence of various environmental factors. This research proposes a novel approach to improve the classification accuracy in leaf disease detection by utilizing Principal Component Analysis (PCA) for the extraction of statistical features from leaf images. PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space, retaining the most important information while discarding the less significant variance. To achieve accurate and efficient disease classification, the extraction of meaningful statistical features from leaf images is crucial. Principal Component Analysis (PCA) is a widely-used dimensionality reduction technique that can aid in improving the classification accuracy by reducing the feature space.

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