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Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images. In order to achieve robust segmentation, a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper. The segmentation method was based on a decision tree which was constructed by a two-step coarse-to-fine procedure. Firstly, a coarse decision tree was built by the CART (Classification and Regression Tree) algorithm with a feature subset. The feature subset consisted of color features that was selected by Pearson’s Rank correlations. Then, the coarse decision tree was optimized by pruning. Using the optimized decision tree, segmentation of disease symptom images was achieved by conducting pixel-wise classification. In order to evaluate the robustness and accuracy of the proposed method, an experiment was performed using greenhouse cucumber downy mildew images. Results showed that the proposed method achieved an overall accuracy of 90.67%, indicating that the method was able to obtain robust segmentation of disease symptom images. Keywords: Greenhouse vegetables, Symptom images, Decision tree, Image segmentation

Water scarcity has threatened the food security and been a critical concern in China. Promoting modern agricultural irrigation technologies has been identified as an important measure against water scarcity. The overall goal of this study was to analyze the adoption of water-saving irrigation technology by farmers and to identify the major influencing factors of this decision for metropolis suburbs. Based on a field survey of Beijing of China, the results showed that 53.1% of farmers adopted water-saving irrigation technologies to cope with water scarcity, most of which adopted engineering water-saving technologies. The number of adopted water-saving irrigation technologies followed a strong negative correlation with the share of adopters. Econometric analysis revealed that education, farm size, on-farm demonstration, cooperative, training, groundwater, access to information, water use associations, drought-prone area, neighboring farmers, and policy subsidies significantly improved the adaption to water scarcity. Age, production specialization, and cost posed a negative effect on famers’ adoption of water-saving irrigation technologies. These results and implications provide an understanding of farmers’ sustainable irrigation practices and offer an insight to influencing factors to frame improved strategies and policies that enable to cope with water scarcity of metropolis suburbs.

Above ground biomass (AGB) is a critical trait indicating the growth of winter wheat. Currently, non-destructive methods for measuring AGB heavily depend on tools such as Remote Sensing and LiDAR, which is subject to specialized knowledge and high-cost. Low-cost solutions appear therefore to be a necessary supplement. In this study, an easy-to-use AGB estimation method for winter wheat at early growth stages was proposed by using digital images captured under field conditions and Deep Convolutional Neural Network (DCNN). Using canopy images as input, the DCNN was trained to learn the relationship between the canopy and the corresponding AGB. To compare the results of the DCNN, conventionally adopted methods for estimating AGB in conjunction with some color and texture feature extraction techniques were used. Results showed strong correlations could be observed between the actual measurements of AGB to those estimated by the DCNN, with high coefficient of determination (R2 = 0.808) and low Root-Mean-Square-Error (RMSE = 0.8913 kg/plot, NRMSE = 24.95%). Factors may influence the accuracy of the DCNN were evaluated. Results showed selecting suitable values of these factors for the DCNN was the guarantee to accurate estimation results. Plant density was proved to be an influence of factor to all the estimation methods based on digital images. The performances of all the methods were influenced to varying degrees while the DCNN achieved the best robustness, indicating the DCNN with RGB images could be an efficient and robust tool for estimating AGB of winter wheat at early growth stages.

Manual approaches to recognize cucumber diseases are often time-consuming, laborious and subjective. A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildew, and target leaf spots. The symptom images were segmented from cucumber leaf images captured under field conditions. In order to decrease the chance of overfitting, data augmentation methods were utilized to enlarge the datasets formed by the segmented symptom images. With the augmented datasets containing 14,208 symptom images, the DCNN achieved good recognition results, with an accuracy of 93.4%. In order to compare the results of the DCNN, comparative experiments were conducted using conventional classifiers (Random Forest and Support Vector Machines), as well as AlexNet. Results showed that the DCNN was a robust tool for recognizing the cucumber diseases in field conditions.

Safe food is healthy, nutritious and environmentally friendly by green, sustainable and clean production. The objectives of this study were to examine factors influencing the consumers' purchase intention and willingness to pay a premium price for safe vegetables. To achieve the objectives, this paper developed the model of consumers' purchase safe vegetables. Based on 840 valid questionnaires from Beijing of China, the model was confirmed by the logistic regression method. The results showed that food safety, strict production and processing, and healthy environment were the top three perceived advantages of safe vegetables for consumers. In addition, 93.0% and 87.1% of consumers were familiar with organic vegetables and green vegetables, respectively. Two-thirds of consumers identified safe vegetables via certification labels on vegetable packages. Furthermore, 67.6% of consumers were willing to buy safe vegetables and 65.8% would pay a premium price for safe vegetables. Meanwhile, consumers' purchase intentions for safe vegetables were positively affected by family food expenditure, children, familiarity, differential cognition, safety awareness, nutritional health, packaging, label trust, and online shopping experience but price level, safety status, and freshness had a negative effect. Consumers’ willingness to pay a premium price for safe vegetables was positively affected by family income, familiarity, differential cognition, safety awareness, nutritional health, packaging, label trust, and online shopping experience but family food expenditure, price level, price fluctuations, and safety status had a negative effect. The findings of this study have important implications to improve the consumption and guide cleaner production of safe vegetables. The findings obtained can also provide references for other similar studies in other areas.

Cycling tourism has grown in popularity worldwide, motivating its inclusion in the development strategies of tourism organizations. This paper builds an analysis model based on spatial analysis and Empirical Mode Decomposition, providing a data-driven way to utilize GPS cycling trajectory data to analyze the spatiotemporal behavior of Chinese cycling tourists using a case study example of the Tibet Autonomous Region. The results show that: (1) The fluctuation of the number of cycling trajectories in Tibet presents multi-modal characteristics, corresponding to fluctuations in China's tourism industry and economy; (2) Cycling tourists in Tibet prefer to use national roads (arguably due to the safety of the road infrastructure and the availability of supporting tourism infrastructure); (3) Cycling tourism is highly concentrated in and around the popular attractions of Tibet. The presented data and methods help to understand the spatiotemporal behavior of cycling tourists, which is of great significance for tourism management.

With the increasing demand for food worldwide, it has attracted increasing attention how to improve the agricultural production efficiency. This paper aims to develop a technical efficiency evaluation system for vegetable production to provided decisions for the practice of precision agriculture. The paper analyses the system-needs and business processes, and proposes a system framework which has three tiers architectures, based on B/S model. The stochastic frontier analysis (SFA) algorithm model which is the incorporated into the system is established. The system was tested and evaluated by real business data, which were from Beijing from 2003 to 2011 to test system performance based on the temporal perspective and China during 2011 and 2012 to test system performance based on the spatial characteristics. The results shows that the system achieves the business requirements with an intelligent tool for data management and technical efficiency evaluation for vegetable production to improve automation, efficiency and convenience.

Leaf area index (LAI) is a key growth trait to characterize the winter wheat growth at early stages. However, there needs more study on the low cost and fine-scale estimation of LAI of winter wheat. In this study, a LAI estimation method of winter wheat at the early stages was proposed based on low-cost RGB images and deep learning. The time-series canopy images of winter wheat at early stages were collected for two consecutive growth seasons (growth season 2018 and 2019), based on which the proposed model, as well as the compared models, were built. In the following step, the performances of these models were compared and analyzed. The influences of the input image with different pixel resolutions and the network depth on the model performance were discussed. Moreover, transfer learning was used to test the generalization ability of the proposed model. The results showed that the proposed estimation model could reflect the time-series variation of LAI of winter wheat at early stages. The proposed model with the input image of 128 × 128 pixel resolution achieved the best performance (R2 = 0.82, NRMSE = 24.89%), outperforming the compared models. The generalization test showed that the proposed model had a good generalization ability, achieving accurate LAI estimations for growing season 2019. However, deepen the network by adding extra SAME convolutional layers could not improve the model performance. In conclusion, based on the convolutional neural network (CNN) and low-cost RGB images, the proposed model is fast and accurate in estimating the LAI of winter wheat at early stages. This method can meet the need for LAI estimation of winter wheat at early stages and provide support for growth monitoring and agronomic management of winter wheat at early stages.

A comprehensive color feature and its detection method are proposed.The method can segment disease spots images captured under real field conditions.The method adopts a region growing method based on CCF map to obtain the disease spots segmentation.The method guarantees an accurate input to CNN based disease identification. This paper presents a novel image processing method using color information and region growing for segmenting greenhouse vegetable foliar disease spots images captured under real field conditions. Disease images captured under real field conditions are suffering from uneven illumination and complicated background, which is a big challenge to achieve robust disease spots segmentation. A disease spots segmentation method consisting of two pipelined procedures is proposed in this paper. Firstly a comprehensive color feature and its detection method are presented. The comprehensive color feature (CCF) consists of three color components, Excess Red Index (ExR), H component of HSV color space and b component of Lab color space, which implements powerful discrimination of disease spots and clutter background. Then an interactive region growing method based on the CCF map is used to achieve disease spots segmentation from clutter background. To evaluate the robustness and accuracy, the proposed segmentation method is assessed by cucumber downy mildew images. Results show that the proposed method can achieve accurate and robust segmentation under real field conditions.

Vegetable price predictions are of great significance to vegetable growers, particularly regarding production and management to ensure a balance in the regional supply and demand of vegetables. In the current paper, in order to improve the accuracy and efficiency of vegetable price forecasting, we propose an optimized neural network combined model based on the induced ordered weighted averaging operator. Our frameworks integrate the fruit fly algorithm (FOA) with the induced ordered weighted averaging (IWOA) operator for an enhanced performance. In particular, the FOA is employed for the parameter optimization of the generalized regression neural network (GRNN) and radial basis function neural network (RBFNN), reducing the adverse influence of man-induced factors in the model construction process and improving the learning ability of both GRNN and RBFNN. The IWOA operator calculates the weights of the single GRNN and RBFNN to address the problem of fixed weights in combination forecasting models. Monthly vegetable price data in Beijing was used to compare our method with nine single forecasting models, revealing that the optimization of the GRNN and RBFNN parameters by the FOA, the prediction accuracy of the FOA-GRNN model and FOA-RBFNN model surpass those of GRNN and RBFNN, respectively. Furthermore, results from four evaluation indexes reveal that the IOWA-based optimized neural network model exhibited a stronger predictive ability than the other nine prediction models. Results demonstrate the effectiveness of our framework for the prediction of vegetable price series, with potential applications in agricultural products of similar characteristics.

Improving the extraction of cucumber leaf spot disease under complex backgrounds.Redefining the feature distance between pixel xj and clustering center vi.Calculating the two-dimensional neighborhood mean gray value as a sample point.Proposing a new weighting method for gray value and neighborhood gray value. Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as xj2-vi2. Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.

China is the world's largest energy consumer and is also leading in the solar photovoltaic industry. The solar energy industry, with advantageous clean and highly efficient production, is integral in maintaining the sustainable energy supply in an environmentally sensitive manner. Further development of the Chinese photovoltaic industry requires a status assessment for the current industrial setting. An analysis on the 58 Chinese photovoltaic listed enterprises is conducted in this study to analyze the operating performance, industry agglomeration and spatial characteristics of Chinese photovoltaic industry. Comprehensive analysis and evaluation of the enterprises’ operating performance are based on financial data by utilizing the Data Envelopment Analysis (DEA) method; Systematic analysis examines spatial characteristics through the application of the spatial autocorrelation analysis method. Results confirm the Chinese photovoltaic industry preliminarily appeared industry agglomeration with enterprises mainly distributed in east and north China, and gathered in developed coastal provinces. Superior performing photovoltaic enterprises are specifically located in north China. Most of the enterprises are in the up-stream or mid-stream of the photovoltaic industrial chain. Overall operating performance of the photovoltaic industry in China suffers from weak profits due to low technical efficiency. The key to improve the future development of these enterprises is the improvement of operating performance by strengthening technical efficiencies. Concurrently, governmental guidance should address profitable photovoltaic industry investments and improvement of production rates.

Agricultural fossil energy consumption increases carbon dioxide emissions and is a critical concern in China. Photovoltaic agriculture refers to combining agricultural activities and photovoltaic power generation without changing the agricultural land and affecting agricultural production. It is a new agricultural production approach and has been identified as an important measure to deal with environmental pollution and fossil energy consumption. The goal of this study was to analyze the key factors that influence the willingness, behavior, and willingness-behavior consistency of farmers to adopt photovoltaic agriculture. A survey with 643 participants was conducted in China. The bivariate probit model and the binary logistic regression were used to test nineteen influencing factors. The results showed that the proportion of farmers whose adoption willingness was consistent with the adoption behavior was 37.1%, whereas 62.9% of farmers exhibited inconsistency between adoption willingness and adoption behavior. Differences were observed in the key factors influencing the willingness, behavior, and willingness-behavior consistency of adopting photovoltaic agriculture. The usefulness perception and technical training had significant positive impacts on the adoption willingness, adoption behavior, and willingness-behavior consistency of the farmers, whereas the photovoltaic investment cost had a negative impact. The results of this study provide an understanding of the factors influencing the promotion and dissemination of photovoltaic agriculture, a basis for optimizing related policies, and references to facilitate the implementation of photovoltaic agriculture in rural areas in other countries.

Fast and accurate segmentation of winter wheat ears from canopy images can significantly promote the field phenotyping of ears by improving efficiency. In this study, a semantic segmentation based method, i.e., EarSegNet, was proposed to perform pixel-wise classification to segment wheat ears from canopy images captured in field conditions. The EarSegNet integrated the encoder-decoder structure and dilated convolution, aiming to further improve the segmentation accuracy and efficiency for the ears of winter wheat. The results showed that the proposed EarSegNet was able to achieve accurate segmentation of wheat ears from canopy images captured at the flowering stage (segmentation quality = 0.7743, F1 score = 87.25%, structural similarity index = 0.8773). In order to validate the proposed method, the performance of the proposed EarSegNet was compared to the widely used segmentation methods, i.e., SegNet, Two-stage method, and Panicle-SEG. Results showed that the proposed EarSegNet outperformed the compared methods, making a robust and efficient tool to segment ears of winter wheat from canopy images captured at the flowering stage. Generalization tests showed that the proposed EarSegNet achieved superior performances to the compared method, suggesting that the EarSegNet had great potentials for field applications. Obtained results showed that the depths of the encoder, i.e., VGG16, had no significant influences on the performance of EarSegNet, however, deepening the VGG16 would improve the performance of the EarSegNet on the evaluation metric of recall. The results showed that the EarSegNet was a promising tool for ears of winter wheat at flowering stage.

Segmenting ears of winter wheat from canopy images was considered to be an important procedure prior to the extraction of related traits. Current segmentation method based on computer vision was susceptible to noise, which is limited in practical applications. In this study, a two-stage segmentation method for ears of winter wheat based on digital images of unit ground area and the state-of-the-art deep learning techniques was proposed. In the coarse segmentation stage, a deep convolutional neural network (DCNN) was constructed to classify the superpixels generated by entropy rate superpixel algorithm, achieving the coarse results. In the fine segmentation stage, a fully convolutional network (FCN) allowing pixel-wise semantic segmentation was constructed to eliminate the non-ear pixels in the coarse results. To compare the results of the proposed two-stage segmentation method, conventionally adopted methods for image segmentation were used. Results showed that the proposed two-stage segmentation method was able to accurately segmenting ears of winter wheat from canopy images captured at flowering stage (Qseg = 0.7197, F1 score = 83.70%, SSIM = 0.8605), outperforming the other compared methods. Generalization tests were conducted to evaluate the utility of the proposed two-stage segmentation method. Results showed that the two-stage segmentation method was still capable of accurately segmenting ears of winter wheat, even though the performance slightly decreased. Change of winter wheat cultivar and lack of descriptive information were two factors that could degrade the performance of the two-stage segmentation method. Tests of the methods on Unmanned Aerial Vehicle (UAV) based RGB images showed the Fully Convolutional Network stride 8 predictions (FCN-8s) had a good chance to achieve satisfactory performances on UAV based canopy images.

A new method for extracting key frames of greenhouse videos was proposed.The method took full considerations of characteristics of greenhouse videos.The method combined the visual saliency and online clustering.The IG algorithm was modified by two parameters confirmed by a new measurement. Research reported in this paper aims to improve the identification of greenhouse vegetable diseases based on the greenhouse monitoring video. It presents a method that combines the visual saliency and online clustering to extract the key frame from greenhouse vegetables monitoring video. Firstly X2 histograms are used to measure the similarity of each frame to the first frame, which eliminates the meaningless frames and improve data processing efficiency and costs. Then, all frames will be converted to HSV color space and a saliency map of each frame is generated based on H component value and S component value. According to the saliency map, the salient region can be obtained. During the process of extracting the salient region, there is a possibility that the information of disease spots is lost. Therefore, morphological method would be utilized to restore the lost information. Finally, online clustering is performed to classify the salient regions into different clusters, and mean pixels value is used to select the key frames. The results indicate that this method can obtain information of entire leaf area of vegetables and extract the key frame effectively.

The paper presents a method that addresses the problem of using catastrophe theory to evaluate the informatization level in four Chinese regions. We developed an index evaluation system that consists of five categories (secondary indices) of Economic strength, Information infrastructure, Information terminal equipment, Human resources and Information utilization, and fourteen tertiary indices as the evaluation index system for the rural informatization level. The effectiveness of this method is tested by evaluating the level of information technology application in four Chinese rural regions (eastern, central, western, and northeastern regions). The results show that the catastrophe progression values (CPV) averaged at 0.806 in mainland China. The CPV for the eastern region is ranked the highest at 0.871, the northeast region second at 0.841, the central region third at 0.553, and the western region the lowest at 0.213. The results are found to be consistent with a priori expectations proving that the catastrophe progression method works well.

With the largest population living in rural areas, the lack of clean energy supply is an important problem in China, and the utilization of renewable energy not only meets the demand of energy, but also provides a basis for environmental protection and sustainable development. This paper reviews use of clean energy in a resource-rich region, including the basic principle of the development of recycling agriculture, the potential, the present status and the future of renewable energy in the region rural areas. If the project will be finished, there will be 300,000 families benefiting from it and it can bring at least 2.2 × 108 US dollars revenue increase directly, the indirect revenue increase will be 0.15 billion US dollars, the environment will be improved and the living standard in Jincheng's rural areas is also be greatly improved because of developing biogas project. Based on the developing status and constraint conditions, the solutions to further promote renewable energy projects in this region are also proposed.

Agricultural domestic support is a sensitive issue for all countries producing agricultural commodities. To simulate the dynamic influence of changes in agricultural subsidies on non-agricultural sectors, this paper improved the Indian Storm models by embedding the labor force module based on the new Austrian model. The simulation result showed that a steady increase in the rate of producers' subsidy had little effect on capital flows, non-agricultural sectors and the rate at which agricultural surplus labor force transfers to non-agricultural industries. The non-agricultural industry should benefit from an increase in the rate of producer subsidies, as total consumption expenditure by non-agricultural industry is reduced. Producer subsidies facilitate transfer of labor force from agriculture to the light manufactures sector the most, the fertilizer and service sectors second, and the heavy manufactures sector the least.

This paper, from innovation adoption perspective, investigates people's perceptions and attitudes toward adopting e-learning to explore the key factors affecting the e-learning adoption behavior in China. Based on Rogers' innovation adoption theory, 33 factors of perceived innovative attributes will be quantificationally analysed to test the relationship between the perceived innovative attributes and adoption intention of e-learning by an analytic hierarchy process (AHP). The result shows that some factors of perceived innovative attributes, such as cost, quality, agility, schedule control, certification of degree, personal demands and so on, have more influence on peoples' adoption of e-learning.

The paper presents a method that addresses the problem of the amount of green space required for the environment areas of lower biodiversity by the ecological element threshold method. The original habitat had been highly disturbed by human activities in most developing countries. Taking the population carrying capacity, the balance of carbon–oxygen, and the supply–demand equilibrium of water resource as a group of conjugate restriction factors of green space planning, it quantifies the total amount of green space required to keep the ecological system in balance for the town of Shaliuhe, Hebei Province as the case study. The results show that the main restrictive factor at Shaliuhe town is the imbalance between the supply and demand for the water resource, 89.34% of which is used in agriculture. Therefore, the effects of various ecological improvements are calculated for the years 2005, 2010 and 2015. This case study could be used as a model for the planning of other towns on the northeast China plain to improve the environment, ecology and sustainability. Similarly, the Chinese scenario might provide a useable reference to other developing countries.