Shape Signature Features of Healthy and Diseased Tomato Leaves Using Contour Metrics

This study investigates the role of leaf shape in detecting disease in tomato plants, grounded in the observation that plant leaves often undergo structural changes in response to infection. Healthy and diseased tomato leaves are characterized by extracting shape signature features from images and analyzing their spectral characteristics. Leaf images were captured using a Sony ZV-E10 Mark II mirrorless camera equipped with a Sigma 16 mm f/1.4 DC DN lens. Each leaf was placed flat on a matte white surface under a controlled overhead photography setup. The camera was mounted at a fixed height on a tripod, and uniform illumination was achieved using two symmetrically positioned LED spotlight lamps, minimizing shadows and glare. The dataset comprises 200 samples: 100 healthy and 100 diseased tomato leaves, representing a range of morphological and pathological variations. Three primary shape metrics were extracted from the images to characterize the structural differences: (1) the Centroid Contour Distance measured the radial distances from the leaf centroid to its outer contour; (2) the Hausdorff Distance quantified the geometric dissimilarity between contours; and (3) the Dice Similarity Index assessed the degree of overlap. In addition, spectral characteristics were derived from the RGB channels: mean intensities of red, green, blue, and the Excess Green Index. Results show that both shape and spectral features are valuable for detecting plant diseases: PCA shows clustering patterns between the two classes of leaves, and correlation analysis highlights the relationship between several pairs of geometric and color features. In conclusion, shape is an essential aspect of plant health, as it reflects the structural changes that occur as a result of disease.