Early Detection and Differentiation of Dragon Fruit Plant Diseases Using Optical Spectral Reflectance

Dragon fruit (Hylocereus spp.) is an emerging crop in the tropics and subtropics, but its production is increasingly threatened by diseases that reduce yield and profitability. Early diagnosis of these diseases is crucial for timely intervention, yet visual symptoms often appear only after significant infection has occurred. The study aims to evaluate how optical spectral reflectance can detect dragon fruit diseases and identify the most responsive spectral regions. In this study, six major dragon fruit stem diseases: Neoscytalidium stem canker, stem sunburn, anthracnose, Botryosphaeria stem canker, Bipolaris stem rot, and bacterial soft rot were characterized by the goal of identifying unique spectral signatures for early detection and differentiation of each disease. Seventy-two potted dragon fruit plants of three distinct species were grown under four organic vermicompost treatments (0, 5, 10, 20 tons/acre) in both open-field and high-tunnel conditions together, in a randomized complete block design. A handheld spectroradiometer (350–2500 nm) was used to collect reflectance from the diseased and healthy cladodes (stem segment). Various spectral vegetative indices were computed to identify disease-specific features. The results revealed distinct spectral features for each disease. Infected cladodes consistently exhibited higher reflectance especially in the visible region (400–700 nm) and the near-infrared region (900–2500 nm) of the spectrum than healthy cladodes. The Normalized Difference Vegetative Index (NDVI), Green Normalized Difference Vegetative Index (GNDVI), and Spectral Ratio (SR) spectral indices were significantly higher in healthy plants than in diseased ones, reflecting higher chlorophyll concentration and plant biomass. Conversely, the 1110/810 ratio was lower in healthy plants than in diseased plants, suggesting a more compact internal plant structure. Statistical analysis revealed highly significant differences (p < 0.00001) between healthy and diseased spectra in the Red, Green and NIR regions. Linear Discriminant Analysis(LDA) achieved the highest classification accuracy (OA = 0.642, κ = 0.488), though performance was limited for minority classes. These findings demonstrate that targeted spectral sensing can identify dragon fruit diseases before obvious symptoms emerge. By pinpointing disease-specific spectral indices, our study paves the way for early-warning tools such as targeted multispectral sensors or drone-based imaging that would enable growers to intervene sooner and limit losses. These results highlight the potential for development of UAV-based or portable spectral sensors for large-scale, near real-time disease monitoring in dragon fruit production.