AgriIDIA: Early Detection of Plant Diseases Using Transfer Learning on 24-Channel Multispectral Stacks with EfficientNet-B0

Plant diseases pose a serious threat to agriculture, causing yield losses of 20 to 40 percent each year, resulting in more than USD 220 billion in annual economic losses and significantly affecting the global food supply. Traditional plant health monitoring practices involve visual inspection of plant tissue and can only detect the presence of disease once visual symptoms are already evident. This article presents AgriIDIA, a multispectral plant disease classification system based on 24-channel image stacks multispectral images derived from six optical filters (BlueIR, Hotmirror, K590, K665, K720, and K850) and six vegetation indices (NDVI, GNDVI, NDRE, EVI, REI, and SAVI). First, an exploratory data analysis is conducted on the diagnostic capability of the described 24-channel data representation, using 1266 image stacks labeled with six classes (diseased/healthy papaya, diseased/healthy potato, and diseased/healthy tomato). Next, using the results of the exploratory data analysis, the manuscript describes the training and cross-validation performance of AgriIDIA, with a macro-F1 score of 83.91 ± 3.42% and an accuracy of 84.00 ± 3.17% on the validation set. Finally, the performance of the trained model is evaluated on the reserved test set (N = 190), demonstrating an accuracy of 80.53%, a macro-F1 score of 0.7398, and a weighted ROC-AUC of 0.9383. The results of this study suggest that the 24-channel multispectral representation has significant diagnostic potential for distinguishing healthy and diseased plant tissue under the evaluated conditions.