A Novel Hyperspectral Imaging based Framework for Tomato Leaf Disease Detection

The importance of early identification of tomato bacterial leaf spot (BLS) is essential to minimize the losses in yields and enhance the use of precision agriculture.

The paper demonstrates a lightweight deep learning model, the Dual Multi-Layer Perceptron Feature Fusion Network (DMLPFFN) as a hyperspectral-based approach to the four tomato disease stages, which includes healthy, asymptomatic, early and late infection. The USDA hyperspectral data (168300 bands) has spectral heterogeneity which is solved by per-image Principal Component Analysis (PCA) to normalize the inputs into a single 128-dimensional space. Spectral-spatial patches of 9×9 are overlapping and normalized and then trained upon before training the model. The DMLPFFN combines the feature extraction process that consists of multi-scale dilated convolutions and global contextual modelling with lightweight element-wise fusion. Training strategies that are imbalance-aware are useful in increasing robustness.

The model had a weighted F1-score of 0.9718 and a validation accuracy of 98.22% and test accuracy of 97.18%. These findings can be defined as good generalization and lower computational complexity, which makes the framework applicable to real-time agricultural application.