Predicting soluble solids content in “Fuji” apples of different ripening stages based on multiple information fusion

Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection

In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by most nondestructive technologies is complex, time-consuming, and requires highly skilled operators. Meanwhile, the general applicability of various chemometric or statistical methods is affected by noise, sample, variability, and data complexity that vary under various testing conditions. Nowadays, machine learning (ML) techniques have a wide range of applications in the food industry, especially in nondestructive technology and equipment intelligence, due to their powerful ability in handling irrelevant information, extracting feature variables, and building calibration models. The review provides an introduction and comparison of machine learning techniques, and summarizes these algorithms as traditional machine learning (TML), and deep learning (DL). Moreover, several novel nondestructive technologies, namely acoustic analysis, machine vision (MV), electronic nose (E-nose), and spectral imaging, combined with different advanced ML techniques and their applications in food quality assessment such as variety identification and classification, safety inspection and processing control, are presented. In addition to this, the existing challenges and prospects are discussed. The result of this review indicates that nondestructive testing technologies combined with state-of-the-art machine learning techniques show great potential for monitoring the quality and safety of food products and different machine learning algorithms have their characteristics and applicability scenarios. Due to the nature of feature learning, DL is one of the most promising and powerful techniques for real-time applications, which needs further research for full and wide applications in the food industry.

Feature Normalization Reweighting Regression Network for Sugar Content Measurement of Grapes

The measurement of grape sugar content is an important index for classifying grapes based on their quality. Owing to the correlation between grape sugar content and appearance, non-destructive measurements are possible using computer vision and deep learning. This study investigates the quality classification of the Red Globe grape. The number of collected grapes in the range of the 15~16% measure is three times more than in the range of <14% or in the range of the >18% measure. This study presents a framework named feature normalization reweighting regression (FNRR) to address this imbalanced distribution of sugar content of the grape datasets. The experimental results show that the FNRR framework can measure the sugar content of a whole bunch of grapes with high accuracy using typical convolution neural networks and a visual transformer model. Specifically, the visual transformer model achieved the best accuracy with a balanced loss function, with the coefficient of determination R = 0.9599 and the root mean squared error RMSE = 0.3841%. The results show that the effect of the visual transformer model is better than that of the convolutional neural network. The research findings also indicate that the visual transformer model based on the proposed framework can accurately predict the sugar content of grapes, non-destructive evaluation of grape quality, and could provide reference values for grape harvesting.

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology

To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R, G, B, H, S, and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc < 0.7) and a mature stage (after which color changes occurred) (0.7 ≤ Mc < 1). When predicting grape maturity based on the R, G, B, H, I, and S color values, the R, G, and I as well as G, H, and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc's predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.