Fan Wang

The necessity for precise determination of fruit storage durations is paramount in both industrial and domestic spheres, with hyperspectral imaging (HSI) technology emerging as a pivotal tool for prognosticating the physicochemical attributes indicative of mature fruit quality. This investigation employed hyperspectral imaging to conduct a noninvasive analysis of variations in soluble solids content (SSC), hardness, and moisture content (MC) in jujube fruit over the course of storage at divergent temperatures. Throughout the storage intervals (0, 7, 14, and 21 days) at varying temperatures (4 and 20°C), both physicochemical and spectral data were amassed. The raw spectral information underwent preprocessing through multiple scattering correction (MSC), standard normal variation (SNV), and Savitzky–Golay (SG) algorithms to refine the methodologies for SSC, hardness, and moisture content. Subsequently, the competitive adaptive reweighted sampling (CARS) algorithm facilitated the discernment and elimination of extraneous variables, thereby enhancing feature wavelength extraction. This process underpinned the development of partial least squares regression (PLSR), back propagation (BP), and genetic algorithm‐back propagation (GA‐BP) models predicated on CARS‐derived features, culminating in the selection of an optimal model. The findings underscore the capability of hyperspectral imaging technology to swiftly and nondestructively ascertain the SSC, hardness, and MC of jujube throughout the storage phase, thereby enabling the assessment of quality attributes over varying storage durations and facilitating the surveillance of jujube quality maintenance during storage.Practical Applications: The winter jujube garners appreciation from consumers owing to its exquisite flavor, yet its quality diminishes over time in storage due to various factors, thereby impacting its market value. Consequently, it is imperative to surveil the quality alterations of jujube throughout its storage to mitigate the degradation of its quality. Hyperspectral imaging technology offers a sophisticated means to forecast the physicochemical index changes indicative of the fruit's quality at maturity. This research delineates the development of predictive models for the soluble solids, hardness, and moisture content of jujube at divergent temperatures throughout the storage interval, selecting the paramount model through a holistic assessment, thereby fully harnessing the capabilities of hyperspectral imaging technology in monitoring jujube quality during storage. Moreover, the methodology employed herein is adaptable to other fruits in storage, harboring the potential for future application in the real‐time quality monitoring of fruits as they exit the storage facilities.

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700–1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R²) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R² values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.

Tea plants are one of the most widely planted agricultural crops in the world. The traditional method of surveying germination density is mainly manual checking, which is time-consuming and inefficient. In this research, the Improved YOLOv5 model was used to identify tea buds and detect germination density based on tea trees canopy visible images. Firstly, five original YOLOv5 models were trained for tea trees germination recognition, and performance and volume were compared. Secondly, backbone structure was redesigned based on the lightweight theory of Xception and ShuffleNetV2. Meanwhile, reverse attention mechanism (RA) and receptive field block (RFB) were added to enhance the network feature extraction ability, achieving the purpose of optimizing the YOLOv5 network from both lightweight and accuracy improvement. Finally, the recognition ability of the Improved YOLOv5 model was analyzed, and the germination density of tea trees was detected according to the tea bud count. The experimental results show that: (1) The parameter numbers of the five original YOLOv5 models were inversely proportional to the detection accuracy. The YOLOv5m model with the most balanced comprehensive performance contained 20,852,934 parameters, the precision rate of the YOLOv5m recognition model was 74.9%, the recall rate was 75.7%, and the mAP_0.5 was 0.758. (2) The Improved YOLOv5 model contained 4,326,815 parameters, the precision rate of the Improved YOLOv5 recognition model was 94.9%, the recall rate was 97.67%, and the mAP_0.5 was 0.758. (3) The YOLOv5m model and the Improved YOLOv5 model were used to test the validation set, and the true positive (TP) values identified were 86% and 94%, respectively. The Improved YOLOv5 network model was effectively improved in both volume and accuracy according to the result. This research is conducive to scientific planning of tea bud picking, improving the production efficiency of the tea plantation and the quality of tea production in the later stage.

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R²_v) of 0.93 and root mean square error (RMSE_v) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears.