Bacterial leaf scorch is a harmful disease for pecan production, which can cause premature canopy defoliation, reduced kernel weight, and significant yield losses. The disease can cause a 10-13% reduction in shell nut weight and a 14-19% decrease in kernel weight, adversely affecting the quality and quantity of pecan yields. Early detection and precise management are important for minimizing economic losses and sustainable disease management. Current methods, such as manual scouting and conventional imaging, are inadequate for pecan orchards due to the trees' height and their inability to capture temporal changes or disease transmission patterns. Spatio-temporal modeling is a useful technique that enables tracking disease spread across time and location, identifying hotspots and transmission patterns to prioritize areas needing urgent intervention. This study aimed to develop a spatio-temporal model to visualize and evaluate the progression of bacterial leaf scorch disease in pecan orchards. High-resolution multispectral images of pecan trees were collected using a drone- mounted multispectral camera. A 3D point cloud was generated using Pix4D to create a reference NIR point cloud, and other vegetation indices point clouds were then aligned to provide sufficient data for model training. Ten vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge Index (NDRE), Modified Red Edge Simple Ratio (MRESR), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Atmospherically Resistant Vegetation Index (ARVI), Chlorophyll Index - Red Edge (CI_RE), and Simple Ratio Index (SR), were used to evaluate disease sensitivity. Individual tree canopies were segmented using the 3D DBSCAN algorithm for each index. A point transformer deep learning model was trained with 3D vegetation indices of each tree with the ground truth to develop the prediction ability of the model for classifying individual each tree disease severity level. The early results indicate that the model can classify disease severity in the 3D point clouds, capturing the disease stress in the individual tree. Additionally, integrating a temporal embedding layer into the trained model enables the transformer block to track changes in vegetation indices and quantify disease progression over time. The findings of this study facilitate the monitoring of disease progression and support site-specific management decisions, thereby enhancing the sustainability of pecan production. Keywords: Artificial intelligence, Spatio-temporal modeling, Multispectral imaging, Spectral analysis, Drone-based Scouting.
