Non-Destructive Prediction of Soluble Solid Content in Kumquats Using a Multi-Scale Convolutional Neural Network

Abstract

Traditional methods for detecting the soluble solid content (SSC) of kumquats are often destructive, time-consuming, and inefficient. In this study, a multi-scale convolutional neural network (MS-CNN)-based method is proposed for the rapid and non-destructive prediction of kumquat SSC. By integrating near-infrared spectroscopy (900–1700 nm) with deep learning, 424 spectral samples of kumquats were collected and modeled using the MS-CNN framework. The proposed model adopts a multi-scale feature extraction structure inspired by the Inception architecture, which effectively enhances the representation of spectral features and reduces overfitting. Experimental results showed that the MS-CNN achieved an Rp2 of 0.88, an RMSEP of 0.62 °Brix, and an MAEP of 0.51 °Brix on the internal prediction set. Among the evaluated models, the MS-CNN achieved the highest Rp2, while its RMSEP was comparable to that of PLSR and lower than those of SVR, BP, CNN, and BiLSTM. The proposed approach enables fast, accurate, and non-destructive prediction of kumquat SSC, providing a novel technical solution for fruit quality assessment. This work holds significant theoretical and practical value, and future efforts will focus on expanding the dataset, optimizing the network structure, exploring multi-index joint prediction, and promoting its real-world application.

Plant phenotyping relevance

カンキツ果実のSSCという植物器官形質を、近赤外分光とMS-CNNで非破壊推定する手法の開発・比較評価が研究の中心である。

Code and data availability

The supplied article blocks describe a kumquat NIR spectral dataset (424 samples) and an MS-CNN model, but contain no data availability statement, no public dataset deposit, and no code availability language or authors' public URL for code, trained models, or spectral data. No paper-specific public asset is identified.

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