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To address the problem of the lack of rapid discrimination technology for comprehensive evaluation of product quality and grade in the field of tea quality control, this study uses near-infrared spectroscopy, electronic eye, electronic tongue, and electronic nose technology to sense the external and internal quality sensing characteristics of tea leaves, and realizes comprehensive evaluation of four quality factors such as "color, aroma, taste and shape" of tea leaves through cross-sensor multimodal quality characteristics data fusion. For the first time, a comprehensive method for assessing the grade quality of tea based on deep learning algorithms combining cross-sensor multimodal fusion features such as color, texture, shape, optical, electronic tongue, and electronic nose is established. The results showed that the constructed deep convolutional neural network (CNN) classification model had a misclassification rate of only 0.86% in the prediction set, which was better than the modeling results based on traditional classification methods. This study reveals that the CNN algorithm can effectively evaluate the quality grade of tea samples and realize the digital evaluation of multiple evaluation factors of tea sensory quality. [Display omitted] • Description of tea quality using deep learning and multi-sensor feature fusion. • A comprehensive evaluation for tea "color, aroma, taste and shape" is proposed. • The constructed CNN model has a misclassification rate of only 0.86%. [ABSTRACT FROM AUTHOR]

• Assessing black tea quality by Vis-NIR spectra. • Selecting spectral features by the Si-PLS, CARS, iVISSA, and VISSA. • Chemometric models for black tea quality developed using SVM-based kernel methods. • The CARS-linear kernel SVM model shows promising results. Current evaluation systems for tea quality assessment are difficult to use and unstable. A rapid and cheap assessment method is required to distinguish the quality levels of tea. In this study, a visible-near-infrared (Vis–NIR) spectrometer and support vector machine (SVM)-based kernels were used for the qualitative categorization of black tea. First, a Vis–NIR system was used to acquire the spectral data of seven levels of tea samples. The obtained spectra were preprocessed using the Savitzky–Golay smoothing combined with the first derivative and standard normal variate transformation. Then, four characteristic wavelength selection algorithms, namely synergy interval partial least square, competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), and the interval VISSA, were used to obtain preprocessed spectral features. Finally, the SVM was used with four kernel functions, namely the linear, Gaussian, quadratic, and cubic functions, to develop models based on the variables obtained from the selected features for tea quality classification. The results revealed that the CARS–linear kernel SVM model exhibited the best results, with a correct identification rate of 91.85 % in the validation process. Our findings demonstrate that Vis-NIR spectroscopy can be a rapid, inexpensive, efficient, and alternative method for predicting the quality of black tea. [ABSTRACT FROM AUTHOR]

Abstract Agricultural non-point source (NPS) pollution in water source protection areas poses serious challenges for governments in developing countries. It is important to consider the environmental behavior of farmers when exploring the causes of NPS pollution as well as when establishing scientific controls and management measures. However, the poor understanding of factors influencing the environmental behavior of farmers and the lack of a suitable environmental socio-psychological model limit the application of the environmental behavior of farmers in NPS pollution management. In this study, we therefore integrated the theory of planned behavior (TPB) and the protection motivation theory (PMT) to identify the main determinants of the NPS pollution-related environmental behavior and intention of farmers in the Water Source Area of the Middle Route of the South-to-North Water Diversion Project in China. Results indicated that the integrated model provided a better understanding of the environmental behavior and intention of farmers than that provided by each component when used individually, and revealed that farmers perceived that the susceptibility and severity of threats caused by water deterioration influenced environmental intention through the mediating effects of subjective norm and attitude toward adopting pro-environmental behavior. At the same time, the perceived vulnerability of farmers was relatively high and their perceived severity was relatively low. Furthermore, the subjective norm, attitude, self-efficacy (i.e., the perceived behavioral control), and response efficacy positively and significantly influenced intention. However, response cost had a significantly negative effect on intention. Among them, subjective norm had the largest effect on intention. Intention was the key determinant for the actual environmental behavior of farmers, while self-efficacy also had a significantly positive effect on behavior. Managing and controlling agricultural NPS pollution requires a multi-disciplinary and comprehensive approach. Therefore, an integrated theoretical framework was developed in this study by integrating TPB and PMT to gain insight into the environmental behaviors and intention of farmers. The results provided a theoretical basis for NPS pollution control. Highlights • Integrated TPB and PMT to study farmers' environmental behavior. • Subjective norm had the greatest effect on farmers' environmental intention. • Attitude and subjective norm mediate the effects of threat appraisal on intention. • Pro-environmental behavior should be facilitated through comprehensive measures. [ABSTRACT FROM AUTHOR]