Estimation of Physical Characteristics of Unhealthy Peach Leaves Using a Two-Step Algorithm

Evaluation of the health status of peach leaves is a common practice in both botany and agriculture. The study aims to identify physical characteristics including width, length, area, perimeter, and color ratios to assess the health status of peach leaves. The study compares six image segmentation algorithms: K-means clustering, active contour model, region growing algorithm, OTSU’s method, K-nearest neighbors (KNN), and support vector machine (SVM), using a custom metric function incorporating pixel accuracy, sensitivity, precision, Dice coefficient, Jaccard index, and specificity. The active contour method yields the highest performance with a metric score of 0.9535, demonstrating its effectiveness in segmenting the leaf from its background. The proposed two-step algorithm offers an effective framework for quantifying a leaf’s diseased status by estimating color ratios, allowing for disease progression tracking. The results validate the effectiveness and viability of this approach.