Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry’s competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350–2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.

Portable hyperspectral imaging was used for field and indoor spectra acquisition of the strawberries at three ripeness stages: ripe, mid-ripe and unripe. The mean spectra were pre-processed by multiplicative scatter correction (MSC). Principal component analysis (PCA) was employed to generate score scatter plots and visualize score images for differentiating specific grouping of samples. Three methods, including X-loading weight, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were applied to extract the effective wavelengths. Two classifiers, partial least squares – discriminant analysis (PLS-DA) and least squares – support vector machine (LS-SVM) were used for ripeness assessment. The results showed that the overall accuracy of all classifiers for field assessment ranged from 91.7% to 96.7%, slightly lower than for indoor assessment. Furthermore, the LS-SVM model combined with effective wavelengths with the CARS method performed better for field assessment of strawberry ripeness, providing an accuracy of 96.7%. It can be concluded that hyperspectral imaging can be used for real-time assessment of strawberry ripeness in the field.

Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in ‘Beijing 553’ and ‘Red Banana’ sweet potatoes. Hyperspectral images were acquired from 420 ROIs of each cultivar of sliced sweet potatoes. There were 8 and 10 outliers removed from ‘Beijing 553’ and ‘Red Banana’ sweet potatoes by Monte Carlo partial least squares (MCPLS). The optimal spectral pretreatments were determined to enhance the performance of the prediction model. Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were employed to select characteristic wavelengths. SSC prediction models were developed using partial least squares regression (PLSR), support vector regression (SVR) and multivariate linear regression (MLR). The more effective prediction performances emerged from the SPA–SVR model with R_p ² of 0.8581, RMSEP of 0.2951 and RPD_p of 2.56 for ‘Beijing 553’ sweet potato, and the CARS–MLR model with R_p ² of 0.8153, RMSEP of 0.2744 and RPD_p of 2.09 for ‘Red Banana’ sweet potato. Spatial distribution maps of SSC were obtained in a pixel-wise manner using SPA–SVR and CARS–MLR models for quantifying the SSC level in a simple way. The overall results illustrated that Vis-NIR hyperspectral imaging was a powerful tool for spatial prediction of SSC in sweet potatoes.

The use of plastic film for mulching cultivation is an important agricultural production technology, which plays an important role in achieving agricultural yield increase and farmers’ income increase. China’s use of plastic mulch ranks first in the world, and China is also the country with the most severe residual plastic mulch pollution in farmland. It is of great significance to have a comprehensive understanding of the use and resource recycling of plastic mulch in China. As a result, this article reviews the recent advances in the use and recycling of plastic mulch in China. First, the types and characteristics of commonly used agricultural plastic mulch are introduced. Then, the development process and application situation of plastic mulch in China are mainly summarized, and the problems of farmland soil and environmental pollution caused by residual plastic mulch are discussed. Finally, the current situation, main practices, and existing problems of waste plastic mulch recycling in China’s farmland are explained in detail from the perspectives of government policy formulation and farmer practice. The research in this article will play an important role in further solving the problem of plastic mulch pollution in farmland soil in China and can also provide a reference for other countries.

To address the competing relationship between tuber damage and soil removal in potato combine harvesting, this study investigated the operating mechanism of a belt-rod type separator of a small-scale self-propelled potato combine harvester and the separation performance between tuber and soil. The main factors affecting the tuber-soil separation characteristics were derived from a theoretical analysis of the belt-rod angle, belt-rod linear velocity, and harvester forward speed. A simulation model based on DEM (Discrete Element Method)-MBD (Multibody Dynamics) coupling was constructed and single-factor simulation tests were carried out. Then a three-factor, three-level Box–Behnken test was conducted using the coefficient of force on the tuber and soil clearing rate as response indicators. The optimal combination of parameters resulting in low tuber damage and high soil clearing rate was obtained by solving the regression equations. The optimal parameters were a belt-rod angle of 17.5°, a belt-rod linear velocity of 1.37 m/s, and a harvester forward speed of 0.80 m/s. The simulation model was validated by field experiments and the error between the simulation model and the field harvest was found to be 3.81%. The results can be used as a reference for parameter optimization of small-scale potato combine harvesters and coupled DEM-MBD simulation of tuber-soil separation.