Research on the Soybean Disease Identification Method Using Fused Spectral Data of the Leaf’s Front and Back Sides

To investigate whether spectral information from the backside of soybean leaves can help to improve the accuracy of disease identification models, based on spectral data from the front and back surfaces of leaves, as well as fused spectral data derived from them, a classification model was established using machine learning algorithms in this study. The study first used a spectral acquisition system to obtain spectral information from the front and back surfaces of the leaves, respectively, and calculated the averages of the two types of data to generate fused spectral data from both surfaces. For the three types of spectral data mentioned above, the following four preprocessing methods were applied: Savitaky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), and second-order derivative (2nd Der). At the same time, five modeling methods—support vector machines (SVM), partial least squares discriminant analysis (PLS-DA), convolutional neural network (CNN), random forest (RF), and back propagation neural network (BPNN)—were introduced to establish classification models of soybean leaf diseases, with the aim of selecting the optimal model that achieves the highest identification accuracy in each type of data. The results of the study indicate the following: Among the classification models based on spectral data from the front surface of the leaves, the BPNN model constructed after SG smoothing preprocessing (SG-BPNN) performed the best, achieving recognition accuracy of 88.89% on the testing set. Among the models based on spectral data from the back surface of the leaves, the MSC-PLS-DA model was identified as the optimal model, achieving an accuracy of 98.61% on the testing set. Among the models based on fused spectral data from both front and back surfaces, the MSC-PLS-DA model also demonstrated optimal performance, achieving a classification accuracy of 100% on the testing set. Its accuracy was 11.11% higher than that of the best model using only front-surface data and 1.39% higher than that of the best model using only back-surface data, which verified the effectiveness of fused spectral information from the front and back surfaces of the leaves in improving the accuracy of the soybean disease classification models. Therefore, this study provides a new approach and theoretical basis for the non-invasive, efficient, and precise detection of soybean diseases, and offers valuable reference for promoting the practical application of spectroscopy in the diagnosis of agricultural diseases.