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 16mm 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) 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 show 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.
When combined with spectral data, can form the basis for an effective, automated disease
detection system.