中国耕地利用绿色转型效率的时空分异特征及其影响因素

耕地利用绿色转型(Green Transformation of Cultivated Land Use,GTCLU)是对绿色中国和经济转型等国家发展战略的积极响应,也是破解耕地利用生态困境的有力举措.该研究提出GTCLU这一概念,并据此构建GTCLU测度指标体系,选取2003—2017年中国30个省市区数据,运用三阶...

Spatial-temporal differentiation characteristics of the efficiency of green transformation of cultivated land use and its affecting factors in China

Abstract: Inner quality of fruit has been widely concerned as the impacts of a transition in lifestyles of different consumer segments in recent years. The grading fruits can be used to improve the added value in a fruit industry, according to the internal quality. It is urgent to rapidly detect the internal quality of fruits using non-destructive testing (NDT) grading evaluation. However, the practical application of current NDT technology was restricted seriously, including the low ability of anti-interference in a complex environment, high modeling cost, low model applicability, and complex sensor system. Alternatively, the near-infrared spectrum technology has been by far the most powerful NDT fruit internal quality, due to the easy operation, high precision, and objective condition. But the technology cannot be conducive to the popularization and application in the food, agricultural products processing industry, due to the high cost and energy consumption. Specifically, the spectrometer was integrated for the secondary development, while the light source was the tungsten halogen lamp. A small, low-cost, low-power multi-origin NDT system can be highly required for a broad application prospect in the apple quality evaluation. In this study, a novel portable near-infrared NDT detector was developed to estimate the inner quality of apples using a cloud model. The near-infrared diffuse reflectance spectroscopy was used to integrate the broad spectrum LED light source and fruit characteristic response narrow band photo detector. 14 LED light sources were also symmetrically arranged on a circle, where the luminous intensity was effectively controlled using the current intensity, according to the light intensity for the different types of apple. The detection section was designed with rubber gaskets and shielding rings for better use in the outdoors. The system software was adopted the modular design to import different Apple models, according to the needs of users to achieve multi-use of one machine. A prediction model was loaded onto the cloud server, and then the system transmitted the data to the cloud through built-in 5G/4G and GPS modules. The cloud model was invoked to realize the data storage, result feedback, and display for the detection suitable for the remote sharing and updating of the model. Taking the fruit quality handheld near-infrared NDT system as a sensing terminal, a design scheme was built for the fruit quality Internet of Things (IOT) monitoring platform. Online communication technology was also selected to detect and monitor the fruit quality in real time, providing support for fruit quality control during the intelligent development of the fruit industry. The apples from the 17 producing areas were selected as the research objects, in order to verify the performance of the system. The diffuse reflectance spectra of 500-1 050 nm were obtained by the detection system, and the soluble solids content, hardness, and vitamin C content were determined by the destructive experiments. 529 apple samples were randomly divided into the calibration set and prediction set in a ratio of 3:2. The calibration set was used to establish the model, and the stability of the model was then tested by the prediction set. A Savitzky-Golay (SG) smoothing pretreatment was then used to eliminate the baseline drift and skew for the more stable model. The competitive adaptive re-weighted sampling and genetic algorithm (GA) were also used to extract the characteristic wavelengths, in order to simplify the model for better applicability. The quantitative prediction models were determined for the soluble solid content, firmness, and vitamin C content of apples in different regions. Specifically, the predicted correlation coefficients were 0.926, 0.798, and 0.704, respectively. The predicted root mean square errors were 0.585 %, 1.405 kg/cm2 and 0.968 mg/100g, respectively. Consequently, the testing system can be widely expected to realize the rapid nondestructive detecting of apple quality indexes in multiple production areas. This finding can provide a strong reference for the inspection of fruit quality using near-infrared spectroscopy.