  Amidst growing food production demands, early plant disease detection is
essential to safeguard crops; this study proposes a visual machine learning
approach for plant disease detection, harnessing RGB and NIR data collected in
real-world conditions through a JAI FS-1600D-10GE camera to build an RGBN
dataset. A two-stage early plant disease detection model with YOLOv8 and a
sequential CNN was used to train on a dataset with partial labels, which showed
a 3.6% increase in mAP compared to a single-stage end-to-end segmentation
model. The sequential CNN model achieved 90.62% validation accuracy utilising
RGBN data. An average of 6.25% validation accuracy increase is found using RGBN
in classification compared to RGB using ResNet15 and the sequential CNN models.
Further research and dataset improvements are needed to meet food production
demands.
