A single-wavelength laser relaxation spectroscopy-based machine learning solution for apple mechanical damage detection.

Detecting and mitigating mechanical damage in apples during picking and transportation is a critical concern in the agricultural industry. This paper investigates the optimization of a pattern recognition model using single-wavelength laser relaxation spectroscopy for the purpose of apple mechanical damage detection. We conducted experiments using Red Fuji apples as our sample dataset and designed a single-wavelength laser relaxation spectroscopy system to collect spectral data. The 1823 sets of data from 45 apples were collected. To enhance the quality of the data, we applied the Min–Max standardization algorithm for preprocessing. Subsequently, we employed multiple pattern recognition models, including Support Vector Machine (SVM), Cross-Validation Optimized Support Vector Machine (CV-SVM), Relevance Vector Machine (RVM), and Sparrow Search Algorithm Optimized Relevance Vector Machine (SSA-RVM), to establish models for apple damage detection. Our study involved a comparison of the efficiency and accuracy of these models. Our findings indicate that CV-SVM emerged as the most stable model for apple damage detection, achieving an impressive accuracy rate of 93.19%. Furthermore, to enhance the classification performance, we applied Multiple Measurement Classification Recognition (MMCR). The CV-SVM-MMCR model demonstrated superior classification abilities, with a detection accuracy rate of 97.5%. Notably, our proposed method offers several advantages, including ease of operation, rapid analysis, and cost-effectiveness. [ABSTRACT FROM AUTHOR]