A Novel Hyperspectral Imaging based Framework for Tomato Leaf Disease Detection

Abstract

Background: 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. Methods: 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. Result: 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.