Grading the Severity of Peach Leaf Shot Hole Disease Using Hyperspectral Imaging

Shot hole disease is one of the major diseases affecting the health of peach trees. To achieve effective grading-based prevention and control and enable precise grading of peach leaf shot hole disease severity, this study proposed a disease grading method based on hyperspectral imaging (HSI) combined with machine learning and deep learning. First, hyperspectral imaging equipment was used to acquire region-of-interest (ROI) information from leaves at different disease grades; after calibration, shot hole disease was divided into five disease grades from Grade 0 to Grade 4. Second, to fully exploit the spectral information, multiple preprocessing methods were systematically compared, and the grading performance of seven machine learning classifiers and two deep learning classifiers under full-spectrum conditions was evaluated. To reduce data dimensionality and redundancy, eight feature wavelength selection methods were introduced, and the performance metrics of the classifiers under different feature wavelengths were compared and analysed. The experimental results showed that scatter correction was the most effective preprocessing strategy at the leaf level: the standard normal variate transformation (SNV) combined with the Elman recurrent neural network reached the highest spectrum-level test accuracy, 77.17%, together with 100% at the leaf level and 94.12% for the leaf-mean spectra, and the linear classifiers logistic regression and linear discriminant analysis reached 94.12% at the leaf level under several preprocessing schemes. Savitzky–Golay smoothing combined with the first derivative (SG+D1) remained clearly superior to the raw spectra, and its combination with the support vector machine (SVM) reached 70.08% at the spectrum level and 76.47% at the leaf level. In terms of feature wavelength selection, eight variable selection methods were compared. The experimental results showed that feature wavelength selection effectively reduced data dimensionality while causing a certain degree of accuracy loss; among the feature-wavelength configurations, the best spectrum-level test accuracy was obtained with the stepwise informative spectral screening (SISS) subset combined with logistic regression (66.93%, and 82.35% at the leaf level), whereas the best leaf-level result of the ten-band subsets was obtained by the synergy interval partial least squares (siPLS) subset with the k-nearest neighbours classifier (94.12%); all the feature subsets remained clearly below the best full-spectrum result, making the accuracy–dimensionality trade-off explicit. Finally, the results indicated that HSI combined with spectral preprocessing can effectively achieve accurate identification of peach leaf shot hole disease grades under full-spectrum conditions, and feature wavelengths can effectively reduce data dimensionality while maintaining a certain level of accuracy. This study provides methodological support and an experimental basis for the precise grading, prevention, and control of peach leaf shot hole disease.