Can Hyperspectral Reflectance Thresholds Achieve Spatial Partitioning of Sweet Potato Leaf Deformation Types on UAV Multispectral Imagery?

Timely detection and monitoring of diseases in sweet potato crops are important for hunger alleviation and food security. This study aimed to evaluate the efficacy of the optimized field spectrometric reflectance thresholds in spatially partitioning sweet potato crops on the unmanned aerial vehicle (UAV) multispectral imagery based on infection types. A field survey was carried out to sample deformed leaves for laboratory diagnosis of possible identification of sweet potato leaf infection types. Laboratory analysis results revealed nutrient deficiency, SPVC-positive, fungi isolates (i.e., alternaria, bipolaris, fusarium, phoma), and mechanical damage as the causes of leaf deformation. Overlap analysis results revealed reflectance overlaps across all leaf deformation types, making it difficult to spatially partition sweet potato crops based on deformation types. Instead, sweet potato crops were spatially partitioned by considering the minimum and maximum thresholds acquired from the whole dataset. Area covered by deformed sweet potato leaves in blue, green, red, red edge and NIR were found to be 11.91%, 28.71%, 43.66%, 46.41% and 30.6% respectively. Coefficient of determination results revealed poor classification results, with R2 value of 0.23, 0.19, 0.28, 0.17 and 0.63 for blue, green, red, red edge and NIR respectively. However, the NIR spectral band yielded R2 value closer to the acceptable value of 0.7.