Computer and Computing Technologies in Agriculture IX

Using Origin software, the grain filling process of hybrid wheats 1–12 and 8-1-54 and their parents was fitted by Richards equation W = A/(1 + Be−kt)1/N on computer in order to study the characteristics of grain filling in hybrids wheat. The active grain growth period of hybrid wheats 1–12 and 8-1-54 were longer than that of their parents respectively, and their final grain weight was higher than that of their parents respectively. These results suggest that hybrids 1–12 and 8-1-54 have the higher capacity of graining filling than their parents and that may be associated with longer RGFP and higher maximum grain-filling rate. It was also found that Richards equation was more suitable for fitting the grain filling process of wheat than Logistic equation.

The excellent feature set or feature combination of cotton foreign fibers is great significant to improve the performance of machine-vision-based recognition system of cotton foreign fibers. To find the excellent feature sets of foreign fibers, in this paper presents three metaheuristic-based feature selection approaches for cotton foreign fibers recognition, which are particle swarm optimization, ant colony optimization and genetic algorithm, respectively. The k-nearest neighbor classifier and support vector machine classifier with k-fold cross validation are used to evaluate the quality of feature subset and identify the cotton foreign fibers. The results show that the metaheuristic-based feature selection methods can efficiently find the optimal feature sets consisting of a few features. It is highly significant to improve the performance of recognition system for cotton foreign fibers.

The calculation of fruit growth rate (FGR) is the main part of fruit-bearing crop growth models. In this paper, greenhouse tomato was used as a research material. The purpose of this study is to better understand the regulation of single fruit growth response to environmental or genetic factors. A FGR model of tomato fruit was described based on Beta function law because it has flexible ability to generate different curve shape when adjusting its parameters. A field experiments with 4 planting densities in 2012 spring was carried out in China, using the cultivar Weichi. The parameters of Beta-law FGR model were estimated with data from the fruits in the first truss by using the optimization algorithm Nelder-Mead The results showed that the optimization procedure described in this paper found best fitted and robust parameters, which implied that those parameters are cultivar specific, and little environmental dependence. The validation against the data from the second truss and third truss with variations in planting density was found to be acceptable (R2 = 0.9117). This modeling methodology and software program have the potential to become a powerful tool for optimization of ideotype design.

The character images are the kernel in the research work of ancient Chinese characters. Nowadays, more and more optical sensing device such as scanners and digital cameras are used for transforming ancient Chinese books into digital images for character research instead of paper documents to overcome the difficulties resulted from the rarity and fragility of ancient documents. How to cooperate the images with researchers in web environment becomes a key topic for the higher efficiency and quality of research work. In this paper, considering the requirements in research work of ancient Chinese characters, a web-based cooperation and retrieval model of character images is constructed which consists of several modules including ancient Chinese image management and retrieval, research work cooperation, research conclusion data management and so on. The key techniques employed in this system are discussed. Firstly, the cooperation mechanism of ancient Chinese character images was designed to make character images cooperate with researchers to avoid the occurrence of the situations that a character image is analyzed by several researchers simultaneously or not assigned to any personal for long time. Secondly, the method of integrated image-text arrangement was constructed to adapt to the special situation of ancient Chinese character research that their study conclusion records often contain the mixture of text and images. Thirdly, the conclusion record sorting algorithm by Chinese character radicals was proposed which could arrange the records according to various kinds of dictionary radical orders to meet the needs of research works. Finally, the global and local retrieval method of ancient Chinese character images is proposed for researchers to search the similar character images in database with the global or local area features of character images. The experimental results show that the system constructed with the proposed model has effective assisting function for the research work of ancient Chinese characters.

In the actual classification problems, as a result of lack of clear boundary information between classification objects, that could lead to loss of classification accuracy easily. Therefore, this article from the spatial patterns of the sample properties to proceed, fuzzy clustering algorithm is proposed based on the sensitivity of attribute weights, through using the attribute weights to improve the classification capability between confusing samples, that is for researching and analysing soil nutrient spatial data with consecutive years to collect in Nongan town. Then through the analysis of the visualization technology to realize the visualization of the algorithm. Experimental results show that introducing weights portray attribute information could reduce the objective function value, and effectively alleviate the phenomenon of boundary data that cannot distinguish. Ultimately to improve the classification accuracy. Meanwhile, use of MATLAB to form visualization of three-dimensional image. The results provide a basis for to improve the accuracy of data classification and clustering analysis of large and complex agricultural data.

Employing the 3-dpoint cloud model of the wheat plant as the research object, through the establishment of mapping algorithm from the screen pixel to the coordinate point of the wheat plant body surface, this paper realized the calculating for the point coordinates on the wheat plant surface utilizing the mouse operation. This algorithm was constituted of solving a user view equation of given screen pixels, screening the point cloud near the line of sight, extracting the efficient point sets near the line of sight, surface fitting efficient point set and solving the intersection of the line of sight and the fitting surface five processing flow on the basis of scattered point cloud data preprocessing and rendering. Adopting the mark function of the FastSCAN 3-d digital scanner accessory instrumental software, the comparison validation of the wheat plant body surface coordinates points’ selection was conducted. The validation results show that the error less than 2.1 mm and had good precision. Considering the influence of the users’ perspective to the calculation of the plant surface points coordinates, the comparison validation was conducted with the10°, 20° and 0° perspective respectively. The results show that the error is less than 1.8 mm and had good accuracy. The mapping algorithm between the screen pixel and the object surface coordinate point established in the study could map the action of the mouse on the screen window to the operation of the object surface. Moreover, it also provided the technical reference for the establishment of the geometry measurement of the interactive plants based on point cloud data.

The small hydropower substitute fuel (SHSF) project is a commonweal project, give prominence to the ecology benefit. The manager of SHSF need a full and specification report for the evaluation results, rather than a database or a simple data of the result. So, based on the characteristics of the management and evaluation system of SHSF project, this paper presents a multi-indicator comprehensive evaluation module and report template, which includes environmental impacts on climate change, conversion of cropland to forest and natural forest protection project, greenhouse gas emission and the role of social and poverty alleviation. In order to achieve automated building of the comprehensive evaluation report of the small hydropower replacing firewood project, a quick and automatic generation method is constructed for the evaluation report based on C# .Net development environment. It produces evaluation report by reading, traversing and replacing the corresponding position data placeholder of customized multi-indicator evaluation report template. The results showed that the quick and automatic generation method meets the requirements of the management and evaluation system of the small hydropower replacing firewood project successfully, and can be referenced in study and practical application.

Aquaculture intelligent monitoring and management system (AIMAMS) is a web information system covering businesses, home users, and management of aquaculture information, which have many users. The information security and safety equipment of different users is an important issue that we have to consider. Role-based access control introduces the roles concept into user and access rights, and its basic feature are that divide roles depending on the security policy, assign operating license for each role, and assign roles to each user. The user can access the specified object based on their respective roles. In order to achieve different user needs for security, aquaculture access control model based on group/role is developed which is based on analysis of the role-based access control model. Aquaculture intelligent monitoring and management system consists of system administrators, business users, family farming users and technicians, each category contains a number of other users who have different permissions. For such a complex distribution of competences, the user groups are introduced here on the basis of idea of role-based access control model. Users are divided into four groups which correspond to four categories of users, each group is given the largest collection of operational authority, and the users of each group are assigned different roles, which have all or part of privileges of the current group. Technicians can access business and family farming user’s information, but cannot view some sensitive data, such as price, therefore, technicians group is controlled by field-level permissions of data tables, the sensitive fields of business and family farming user data tables is shielded. This model can effectively reduce the overhead of rights management, and can improve system’s scalability. Along with the needs of business development, the system can easily add new user groups and assign permissions and roles for them. This aquaculture access model is universal, it is not only suitable for aquaculture intelligent monitoring and management system but also has reference value for other systems.

With massive and intensive development of Chinese live pig industry, the imbalance of feeding nutrition turns out to be a primary problem. The main reason is that the nutrition needs to be adaptive to live pig’s exact growth conditions in production management process. Regarding this, the paper aims at developing a feeding selection model for pig breeding based on major nutrients predictive model proposed in the Nutrient Requirements of Swine published by U.S. NRC in 1998. The objective of this model is to achieve a minimum cost of feeding stuff under the premise that the nutrients satisfy pig’s needs. According to values proposed in NRC and nutritional elements data, a feeding selection model was developed to give proper suggestions on feeding dietary nutrients as well as exact type and quantity of pig feeding stuff. The work presented in this paper would contribute to the optimal precision feeding strategies so as to guarantee live pig growth quality in management process.

SPOT6 is a new remote sensing satellite launched in 2012, with high spatial resolution and strong data acquisition ability. However, a complete data preprocessing technology for the regulation of land resources has not yet been formed. According to the characteristics of SPOT6 satellite images, four different image fusion methods – Gram-Schmidt, HPF, PanSharpand PanSharpening were selected to conduct the comparison experiment by using the software platforms of ENVI, ERDAS and PCI. We evaluate the results’ performances from 3 different aspects. First, evaluating the image quality of experiment results qualitatively, then assessed quantitatively by establishing evaluation indexes including mean, standard deviation, information entropy, average gradient and correlation coefficient. Finally, evaluating the applicative effect of fused images based on the classification accuracy. The analysis results shows that the method of PanSharp is best to extract construction land information. Based on the PanSharp fusion image, in order to obtain the texture information under different scales, the authors screened the texture features according to Shannon entropy, and then used distance-based approach J-M to calculate the separation for choosing the optimal texture window. Once got the texture information, combining it with the original image to participate in the multi-scale image classification. The research result showed that multi-window texture participation in classification can improve separation of objects. Finally we extract construction land information with the method of SVM. This study may provide the technical support for application of SPOT6 image in the land resources management.

With the development both of the information technology and the aeronautics and astronautics technologies, the remote sensing technology has been used in agriculture widely and deeply. After completing the “Agriculture and Resources Inventory Surveys through Aerospace Remote Sensing” plan, remote sensing technology was widely used in agricultural sectors in the USA. Such as the planting area, yield estimation, crop spatial distribution, agricultural insurance, agricultural disaster monitoring, protection of agricultural environment, etc. And meanwhile built a basic database of agricultural remote sensing, based on the data of “Common Land Unit” CLU) and the “Cropland Data Layer” (CDL). The author went to America to visit some officers, professors and specialists, in order to learn the developing situation of remote sensing technology application in agriculture in America. Through communicating and discussing, he got a lot of information and materials and produced some thinking. This paper expounds the present situation of the development of American agricultural remote sensing from data sources, basic data platform and application of remote sensing technology, etc. At the same time, analyze the base about remote sensing technology used widely in American agriculture from based data storage, data sharing mechanism, operation and business system, the degree of automation, etc. Finally, the paper mentioned three points for discussion on the development direction of Chinese agricultural remote sensing. First, strengthen the concepts of universal, sharing, integration of remote sensing technology. Second, lay stress on the goals of guiding production, managing resource, servicing decision by remote sensing technology. Third, grasp the keys of basic data studying, data platform building, technologies advance.

In recent years, as long as the rapid development of aquaculture, the occurrence of aquatic animal diseases increase year by year, which restrict the sustained, steady, and healthy development of aquaculture. So the rapid diagnosis of aquatic animal disease is particularly important for their prevention and control. In the present work, an Android-based aquatic animal disease diagnostic system has been developed in order to provide more convenient and effective aquatic animal disease diagnostic services to ordinary farmers. The designed system includes the part of the man-machine interface (user interface, expert interface), inference engine, the case base, knowledge base and so on. Interactive interface is a visual display interface. Inference engine was written in the Java programming language. Case base and knowledge base were created using SQLite. The results show that ordinary farmers can easily use this system to realize the convenient, fast, and accurate access to the findings of disease diagnosis and prevention measures as well as aquaculture expert can add, delete, modify, view the case base and knowledge base at any time.

This paper investigated the relationship between vegetation coverage and rural settlements in Horqin Left Back Banner. There are 4 spatial patterns about the relationship between vegetation coverage and rural settlements, including high region-low region, high region-low region-high region, high region-low region-high region-low region, and high region. Around rural settlements, land is used as cultivated land and vegetation coverage is high. The overlap effect of overgrazing and extensive cultivation creates the first low region of vegetation coverage. With increasing distance from rural settlements, human disturbance to vegetation growth decreases, vegetation coverage increases, and the high region appears again. The second low region of vegetation coverage is caused by the overgrazing of another rural settlement. At the border region of 2 adjacent villages, 3 different conditions are presented with the vegetation coverage and rural settlements. In the first condition, vegetation coverage is low and is located at the intersection of both low regions of the 2 rural settlements. In the second condition, the vegetation coverage is high and is located at the intersection of both high regions of the 2 rural settlements. In the third condition, vegetation coverage is low and is located at the intersection of both low regions of the 2 rural settlements. According to the changing patterns and reasons for such patterns, we present strategies for anti-desertification, including banning grazing, stopping extensive cultivation, promoting optimal choice of sand-fixation plant, and clarifying land property rights.

The purpose of topsoil stripping reuse is to provide a good cultivation platform for agricultural site conditions, namely increasing soil thickness and improving soil fertility levels. Based on soil type, using the thickness of the soil and organic matter content as criteria, the natural suitability of stripped topsoil from different regions were evaluated. The evaluation results were divided into five levels: Most suitable, suitable, moderately suitable, less suitable, and unsuitable. The results show that the highest degree of topsoil stripping natural suitability is mainly distributed in the Northeast region, along the middle and lower Yangtze River, and the region south of the Yangtze River. The most unsuitable regions for topsoil stripping natural suitability are mainly located in the hilly regions of the Sichuan Basin and the eastern part of the Northwest. The topsoil stripping process must be based on the soil properties of both the original site and the transplantation location.

On basis of ZigBee network theory, a corn farmland monitoring system was designed, integrates free maintenance and low cost ZigBee network, convenient access GPRS network, and strong GIS software with map process and data statistics. It can be used to monitor and warn the real-time state of corn farmland through measuring sensors. Also help technicians and farmers reducing workload on farm management as well as improving corn planting level.

Winter wheat is mainly planted in water shortage area, such as North China and Northwest China. As a key field management measure, irrigation plays an important role in the production of winter wheat. This paper focuses on the improvement of regional winter wheat yield estimation technique in county scale by adjusting the irrigation management measure in crop growth model. The WOFOST (World Food Study) model was used by dividing the whole county into a number of EMUs (Elementary Mapping Units) and then running the model in each unit in sequence. While running, the measured soil moisture and LAI were used to rate the irrigation parameters. Finally, the calibrated irrigation parameters were used to run the model again. The results showed that the simulated winter wheat growth process was normal. During the whole growing period of winter wheat, the change trends of the time series of soil moisture and LAI were basically consistent with that of the measured. The precision of simulated yield was between 87.26 % and 98.68 % among the 5 units, and the average of the precision was 94.56 %. The precision of simulated winter wheat yield was well, and could meet the needs of winter wheat yield estimation in county-wide. This study may provide basis for estimating crop yield in regional area by using the crop growth model.

This paper studies artificial neural network algorithm as a means of modelling and forecasting the financial market data. Such method bypasses traditional statistical method to deal with financial time series data. A recurrent neural network model, Elman network, is implemented to incorporate autocorrelation in time series data. A 3-parameter model is chosen to fit and forecast S&P 500 index. The experimental data is from 2000–2007, to screen out the abnormal market environment after 2008 financial crisis.

The accuracy of crop rows line extraction is the key to the automatic navigation of agricultural machinery. In the paper, an improved algorithm is proposed to solve the problem of poor connectivity, single pixel and redundant pixels. Firstly, image pre-processing operations is used in order to obtain a binary image, then the binary image is thinned. In the refinement process, the connectivity of the skeleton is maintained by the introduction of Euclidean distance. Experimental results show that the proposed method has good adaptability to the row crop, and the skeleton lines that are extracted is more accurate. Compared with the traditional algorithms, the error of the navigation line is relatively small by using this algorithm, which could meet the needs of the practical application.

Accurate measurements of moisture content are indispensable for maintenance of a detecting sensor is designed in this paper to determine the grain moisture content by measuring dielectric constants. To optimize the performance of the designed sensor, electromagnetic waves with suitable frequencies are chosen first followed by deep studies on its transmission characteristic in grain media. Taking wheat as testing samples and applying a total of six frequencies, the network analyzer from Agilent Technologies E5061A is used to measure the dielectric constant and loss tangent of grains with different moisture content. The variation of dielectric constants against moisture under various frequencies is obtained based on which the grain moisture is deduced. According to high frequency transmission line theory, as the impedance of probes wrapped in wet media varies with dielectric constants and is mismatched with transmission lines, standing waves are generated by the composition of reflected waves and incident waves. The strength of reflections depends on probe and characteristic impedances. The relationship of moisture content and dielectric constant can be deduced by analyzing the dynamic variation of reflected and incident waves. Key hardware circuits have been designed involving the moisture detecting circuit, signal generator circuit, high frequency transmission lines, etc. Calibration experiments are carried out, compared with drying method, a cubic polynomial relationship of moisture and output voltage with $$ {\text{R}}^{2} = 0.995 $$R2=0.995 was obtained by regression analyses. The standard error of predicted results from regression analysis and measured results is 1.09 %. It indicates that the proposed method has a high accuracy.

In order to realize the deployment of wireless sensor network in large areas of plastic greenhouses. The propagation characteristics of radio wave in plastic greenhouse were studied. The Received Signal Strength Index (RSSI) in greenhouse and between greenhouses were studied, and the logarithmic path loss model for RSSI was established. The results show that the radio wave attenuation parameters A and n between greenhouses were 30.785 and 2.89. The attenuation index was larger than the index of free space. In the plastic greenhouse planted with tomatoes, the radio wave attenuation parameters A and n in the ground were 34.99 and 3.64, and in the top of canopy were 35.14 and 2.85, its show that the radio wave transmission has been significantly affected by the crop in the plastic greenhouse.

Leaf temperature is the guarantee for the plant to carry out the life activities and closely related to plants’ healthy growth and crops’ planting management. The accurate measurement of leaf temperature is significant to understand the physiological condition, guide farmland irrigation, select variety and forecast production, etc. The development of plant leaf temperature measurement and requirements of application in recent years at home and abroad were briefly summarized and reviewed in the paper. Firstly, the status of application research and achievements of leaf temperature were introduced from the methods of measurement and scientific experiments. Then it analyzed and compared the principle, advantages and disadvantages and measurement of several common methods in detail including thermal resistance measurement, thermocouple measurement, infrared temperature measurement, infrared thermal imaging measurement and the leaf temperature model. At last, some problems urgently needed to be solved and the development direction of the field were presented, which could provide a reference for the further study of the leaf temperature sensor.

This paper focused on the SPAD values response to different treatment levels of heavy metal Cu, Zn and Cd stress on growth of wheat. Random blocks design experiment has been carried out to simulate five concentration levels’ heavy metal Cu, Zn and Cd respectively. The in-situ SPAD values were measured for each treatment on December 8th 2014 (seedling stage), December 13th 2014 (tillering stage), January 8th 2015 (tillering stage), April 18th 2015 (elongation stage), April 30th 2015 (booting stage), May 12th 2015 (heading stage) and June 3rd 2015 (filling stage) respectively using Minolta SPAD-502. The standard deviation of SPAD in different between check plot (CK) and level i, Normalized SPAD values and their variation coefficients among five heavy metal treatments for Cd, Zn and Cu have been analyzed at different measurement time. SPAD values of CK treatment decreased slightly from seedling stage to tillering stage, followed by rapid increase to elongation stage, keeping a shoulder to heading stage, then rapid decrease to filling stage. With the increase of heavy metal treatment levels, standard deviations of SPAD values between CK and level i increased gradually, and the minimum of standard deviation appeared at booting and heading stage. The Normalized SPAD had been calculated by the SPAD ratio of heavy level i to CK. The higher Normalized SPAD, the lower Normalized SPAD value with the minimum at booting and heading stage. Variance coefficients of Normalized SPAD of Cd treatments at seedling stage and tillering stages lower than Zn and Cu treatments. Normalized SPAD among Cd, Cu and Zn treatments had lowest and similar variance at booting stage and heading stages.

Choosing the example of Jilin province, the construction method of small scale soil database is discussed; the materials gather from the second soil survey projects are digitized; Jilin province 1:1000000 small scale soil database are established. Commonly, soil database includes the three parts of the spatial database, the attribute database and the metadata database. In the end of this paper, the application prospect of soil database is forecasted; the shortage of existing research results is discussed. The method would provide reference for the construction of small scale soil database in other provinces and regions.

Integrated agricultural environmental information real-time collection, transmission and management is extremely critical for precision agriculture (PA). This paper describes the basic knowledge and primary principles used in an agricultural environmental information collection device. The device is based on Raspberry Pi, combining with GPS module, some digital sensors and analog sensors to measure environmental temperature and humidity, barometric pressure, light intensity, soil moisture and other environmental information accurate collection. All collected data was real-time transmitted to a remote server specified database for management. In the experiment process, the device has been proved to have the ability to catch the distributed information and also can get different centralized data management. The results show the integration of operational information collection, transmission and management, as well as the use of open source software to make it easy to collect multiple types of data parallelism, which provides an important solution for the rapid transformation of agricultural production.

In order to realize low-cost automatic control of field irrigation and water metering, an integrated low-power irrigation monitoring terminal based on wireless data communication was designed. Powered by battery or solar, the terminal could acquire and store data of sensors and water meters in real-time. Taking valve as control object, the terminal can provide safe irrigation strategy based on multiple control logic, such as data overrun, time and manual operation. The detection of fault on-site and reasonable judgment of irrigation can be carried out by using self-check function, querying of local data and remote data transmission based on GPRS and Modbus Protocol. Experimental results showed that the terminal could work stably, and control irrigation water usage accurately with low power cost.

In this paper, the pH value of cold fresh pork was non-destructively detected based on hyperspectral imaging (HSI) technique, and some useful data processing methods were discussed. After some sample set partition methods, some spectral pretreatment methods, and some optimum wavelength selection methods were compared respectively, the most suitable data processing method was chosen and the robust hyperspectral model for predicting the pH value of cold fresh pork was established. The results indicated that the pH value hyperspectral model of cold fresh pork established by using the whole wavelengths after the sample set was divided by using concentration gradient (CG) algorithm, and the spectral data was pretreated by using normalization combined with mean center(MC) had the best prediction abilities, with the determination coefficients $$ \text{R}_{{\text{cv}}}^{\text{2}} $$Rcv2 equaled to 0.768, $$ \text{R}_{\text{p}}^{\text{2}} $$Rp2 equaled to 0.694, RMSECV equaled to 0.1113, and RMSEP equaled to 0.1204. The results also indicated that the model established by using the characteristic wavelengths which were selected by using CARS algorithm had better prediction abilities, with $$ \text{R}_{{\text{cv}}}^{\text{2}} $$Rcv2 equaled to 0.8581, $$ \text{R}_{\text{p}}^{\text{2}} $$Rp2 equaled to 0.8668, RMSECV equaled to 0.0858, and RMSEP equaled to 0.0772. All the results showed that suitable data processing methods was advantageous to the prediction ability of the model, and that HSI technique can be utilized to measure the pH value of cold fresh pork in a rapid and non-destructive way.

As the proposition of the ‘Internet plus’ concept and speedy progress of new media technology, traditional business have been increasingly shared in the development fruits of the informatization and the networking. Proceeding from the real plant protection demands, the construction of a cloud-based video monitoring system that surveillances diseases and pests in apple orchards has been discussed, aiming to solve the lack of timeliness and comprehensiveness in the control of diseases and pests in apple orchards. The system can not only monitor the growth state of all apple trees in orchards, but also detect apple trees’ diseases and pests. The system featured a camera located under tree canopies that could precisely detect diseases and pests on the back of the leaves. On the cloud storage service side, plant protectors can get hold of the situation of apple trees’ diseases and pests by using the software installed in a smartphone or a computer. The system provided pinpoint surveillance data and determining criterions for the control of diseases and pests of fruit trees, which had a great realistic meaning to the development of agricultural and rural informatization.

Australia is one of the countries whose scientific level of using grassland resources and animal husbandry development level is very high and the way to coordinate Australia’s animal husbandry and ecological environment protection is worth using for reference. This study focuses on facilities about the Australian government’s controlling and protecting of ecological environment in the process of development of animal husbandry, and combines with the current problems in the process of China’s animal husbandry development, in order to explore a suitable path for implementing the animal husbandry sustainable development in China.

In order to provide references for heavy metals diagnosis of Vetiver grass, Raman spectroscopy technology was employed, the partial least squares (PLS) quantitative analysis model of heavy metal lead of Vetiver grass root was established, different processing methods were used to optimize the Raman spectra of Vetiver grass roots, Successive projection algorithm (SPA) was applied to screen the bands of Raman spectrum and enhance the model’s accuracy. The best Raman spectroscopy quantitative analysis model of lead content in Vetiver grass root were set up, 20 unknown samples were used to test the quality of optimized model. The prediction correlation coefficient and the root mean square error were 0.607 and 0.040 g/kg, respectively.

Dynamic monitoring of vegetation coverage changes, especially on a relatively large temporal scale, have important practical significance in urban planning and environmental protection. The objective of this study is to dynamically investigate the urban landscape patterns of vegetation coverage based on remote sensing techniques. Multi-temporal Landsat images of 1990, 2000 and 2013 were firstly used to produce three vegetation coverage maps of Hefei City, Anhui Province, China with five grades using the NDVI (Normalized Difference Vegetation Index) dimidiate pixel model. Subsequently, a total of eight landscape pattern indictors in FRAGSTATS 4.2 were selected to analyze the dynamic characteristics of area, quantity and density for the study area with different vegetation coverage grades. The results showed that (1) the dominant vegetation coverage of 1990, 2000 and 2013 were the high vegetation coverage, the moderate vegetation coverage and the moderate-to-high vegetation coverage, respectively. The acreage of non-vegetation coverage increased by 1.89 %, while the high vegetation coverage decreased by 10.48 % from 1990 to 2013; (2) the quantity and density of patches decreased by 33.42 % and 33.41 % during 1990–2013. Shannon’s diversity index and Shannon’s evenness index increased from 0.92 in 1990 to 0.97 in 2000, and then declined to 0.96 in 2013; and (3) the contagion index had an upward trend and conversely the aggregation index showed no significant changes, but both of them were close to 1 during 1990–2013. In comparison with natural influences, the primary driving forces causing the changes were ascribed to human factors including the rapid population growth and fast-growing urban areas.

With the development of computer science and information technology, data mining technology in the field of agriculture in recent years has become a hot research. Corn planting process, rational fertilization can effectively promote the growth of corn, however, no basis and targeted fertilization may cause shortage of low fertility soil fertilization, high fertility soil fertilization overdose. To solve this problem, In this paper, cluster analysis OPTICS algorithm based on density of soil classification, and press the nutrient balance method to calculate the level of soil fertility for each corresponding amount of fertilizer, farmers can be targeted based on fertilizer fertilization. In the town of Yushu City, Jilin Province by Gongpeng for application, compared with the traditional fertilization, fertilizer input savings of 20.5 %, maize yield of about 10 %, not only to meet the needs of farmers, but also achieve a reduction in fertilizer inputs, increase production purposes.

Monitoring crops with Remote Sensing (RS) at the national scale is usually an operational work acted as a normal business for the government needs to the crop field conditions. The crop information is main content of agricultural condition. It mainly includes crop growth, crop areas and crop yields, which can be named 3 factors for crop monitoring with RS. Diversification is the general feature of crop monitoring with RS, which reflects in 3 parts of labor objects, labor materials and labor process. Monitoring the 3 factors with RS has similar process summarized as 3 periods which are data acquisition and transmission, model development and application, producing products. Monitoring crops with RS at the national scale needs to found an organizational and technical system, using the System Theory according to the 3 factors, the 3 parts and the 3 periods, mentioned above. The operational work of monitoring the 3 factors have a common goal, which is that the monitoring result is more accurate, the monitoring process is faster, more economic and more convenient. In China, Remote Sensing Application Centre (RSAC) has been working on monitoring the main crops as an operational task and a research project based on its system for several years. The monitoring methods to the 3 factors are presented in this paper along with the cases coming from the monitoring products produced by RSAC in 2014.

Time series smoothing functions have been frequently applied to fit multi-temporal vegetation index for better extraction of plant seasonal/growing parameters. Questions are raised that whether the smoothing is necessary for crop mapping. Four time series smoothing functions, namely, HANTS, Savitzky-Golay (S-G), double logistics and asymmetric Gaussian, were used to smooth 23 MODIS 16-days composite NDVI images in one year. The effectiveness were compared through visual check, correlation coefficient R, root mean square error (RMSE), and local signal noise ratio (SNR). The best smoothing time series NDVI images, along with the original time series images, were then used to map corn and soybeans by spectral angle mapper (SAM) method and their mapping accuracies were compared. Comparison of smoothing results showed that S-G fitted data got the strongest correlation coefficient R, the lowest RMSE and lower local SNR. Comparison of mapping results further showed that time smoothing function does not improve the classification accuracy obviously with the same training sample and same temporal bands. The whole analysis indicates that it is the mapping method that matters more than time series smoothing function for classification precision.

Equipment includes planter, field management equipment and harvester for controlled traffic farming system is elaborated in the paper. And the analysis results shows that main problems of equipment for controlled traffic farming system contains poor ability to navigate, uneven and bent wheel lanes. To solve the problem, the thought that precision agriculture is introduced into equipment for controlled traffic farming system is put forward. For this purpose, the key technology of precision agriculture is introduced, and the feasibility of combination of precision agriculture and controlled traffic farming system is discussed, which provides theoretical support for the development of equipment for controlled traffic farming system.

Biomass is an important phenotypic trait in plant growth analysis. In this study, we established and compared 8 models for measuring aboveground biomass of 402 rice varieties. Partial least squares (PLS) regression and all subsets regression (ASR) were carried out to determine the effective predictors. Then, 6 models were developed based on support vector regression (SVR). The kernel function used in this study was radial basis function (RBF). Three different optimization methods, Genetic Algorithm (GA) K-fold Cross Validation (K-CV), and Particle Swarm Optimization (PSO), were applied to optimize the penalty error C and RBF $$ \upgamma $$γ. We also compared SVR models with models based on PLS regression and ASR. The result showed the model in combination of ASR, GA optimization and SVR outperformed other models with coefficient of determination (R2) of 0.85 for the 268 varieties in the training set and 0.79 for the 134 varieties in the testing set, respectively. This paper extends the application of SVR and intelligent algorithm in measurement of cereal biomass and has the potential of promoting the accuracy of biomass measurement for different varieties.

Comprehensive, reliable and timely information of Brazil’s soybean area is necessary for China to make decisions on agricultural related problems. Spatial sampling method which combined remote sensing and sampling survey is widely used. Due to limitations of width and revisit cycle of medium resolution satellite, This study designed a typical investigation method about Brazil soybean area based on average samples change rate of two years and official statistics of a year before, typical samples were selected to survey, sampling frame was constructed on soybean planting state, the sampling unit was designed as 40 km × 40 km, the sampling proportion was 2 %, average samples change rate of two years were 2013 and 2014. Estimated area was compared with Brazil official harvested area in 2014 (published on 2015 April by Brazilian Institute of Geography and Statistics), the relative error is 2.37 %.

Intervention of online public opinion, especially agricultural rumors, has increasingly become an important issue that is related to the online society safety and reflecting the managers’ social management capabilities. In this paper, meta-synthetic methodology was used to investigate the intervention mechanism of online public opinion system. General characteristics of online public opinion system were characterized qualitatively, and an opinion supernetwork model was built to analyze the intervention mechanism quantitatively. We put forward a rumor intervention mechanism including intervention timing, intervention mode, and intervention intensity. Based on system modeling and simulation, an agricultural rumor case was used to verify the practical effects of online agricultural rumor intervention. The results show that rumor intervention based on meta-synthetic methodology produces good results. This investigation will help to provide a reliable basis for the development of more sophisticated and effective rumor intervention strategies.

Rice is the major food of approximately half world population and thousands of rice varieties are planted in the world. The identification of rice varieties is of great significance, especially to the breeders. In this study, a feasible method for rapid identification of rice varieties was developed. For each rice variety, rice grains per plant were imaged and analyzed to acquire grain shape features and a weighing device was used to obtain the yield-related parameters. Then, a Support Vector Machine (SVM) classifier was employed to discriminate the rice varieties by these features. The average accuracy for the grain traits extraction is 98.41 %, and the average accuracy for the SVM classifier is 79.74 % by using cross validation. The results demonstrated that this method could yield an accurate identification of rice varieties and could be integrated into new knowledge in developing computer vision systems used in automated rice-evaluated system.

How to obtain the spatial coordinates of kiwi fruit has been one of the key techniques for kiwi fruit harvesting robot. In this paper, the writer proposes a unique way to obtain the spatial coordinates of the features of kiwi fruit from the bottom of the target fruit based on the growth characteristics and scaffolding cultivation pattern characteristics of kiwi fruit, plus the help of Microsoft camera and Kinect sensor. Also included in this paper is the coordinate conversion between the images come from Microsoft camera and the images of the Kinect sensor, which is followed by an analysis of the precision of the spatial coordinates of Kiwi fruit captured by the Microsoft camera and Kinect sensor. The process is like this: first, capture images of the target fruit from the bottom of the fruit with Microsoft camera, and then extract coordinates of the target fruits’ feature points to determine the corresponding target fruit feature point coordinates in the Kinect sensor; second, analyze the correspondence between the Microsoft camera image coordinate system and the Kinect sensor image coordinate system so as to establish a mathematical model for the image coordinate conversion; finally, capture target feature points’ spatial coordinates with Kinect sensor and conduct tests. The results show that the precision of coordinate conversion mode and Kiwifruit spatial coordinates can meet the requirements of the harvesting robots.

For breaking the limitation of the GIS platform and realizing the soil nutrients spatial interpolation algorithm for any points in the monitoring area to transplant to the mobile platforms, this paper established the spatial index of the soil nutrient sampling points utilizing the K-D Tree as the space splitting algorithm of the soil nutrient sampling points. On this basis, the K nearest neighbor search of the soil nutrient sampling points was also implemented employing KNN algorithm. Finally, the soil nutrient spatial interpolation was realized based on KNN and IDW algorithm. Meanwhile, the accuracy of the algorithm and the influence to the different soil nutrient elements affected by the K value in KNN algorithm were also verified. The results show that the soil nutrient spatial interpolation algorithm was viable to predict the element contents of soil nutrient during the running time was less than 3 s. To reach the best accuracy, the values of the proximal point K for predicting the PH, organic matter, rapid available phosphorus and rapid available potassium should be set as 85, 15, the largest sample space and 65 respectively. The optimal average absolute error of the pH, organic matter, rapid available phosphorus and rapid available potassium was 0.0405, 0.3870, 0.0015 respectively.

Crop leaf segmentation was one important research content in agricultural machine vision applications. In order to study and solve the segmentation problem of occlusive leaves, an improved watershed algorithm was proposed in this paper. Firstly, the color threshold component (G−R)/(G+R) was used to extract the green component of the cotton leaf image and remove the shadow and invalid background. Then the lifting wavelet algorithm and Canny operator were applied to extract the edge of the pre-processed image to extract cotton leaf region and enhance the leaf edge. Finally, the image of the leaf was labeled with morphological methods to improve the traditional watershed algorithm. By comparing the cotton leaf area segmented using the proposed algorithm with the manually extracted cotton leaf area, successful rates for all the images were higher than 97 %. The results not only demonstrated the effectiveness of the algorithm, but also laid the foundation for the construction of cotton growth monitoring system.

With the development of agricultural research and production, agricultural information and data processing technology have become increasingly demanding. In recent years, organizing and expressing semantic knowledge based on the form of ontology has become the focus of research in artificial intelligence field. And the construction of ontology-based knowledge base is the basic condition. HNC theory has great advantages in processing of Chinese character. However, complexity of the system has restricted its development. This paper summarized the existing research results, took rice production (cultivation) and processing as an example, proposed an idea and methods for the knowledge base construction of agricultural ontology based on HNC theory.

Molecular breeding is considered an important way to improve the breeding efficiency. But due to the deletion of data, method and instrument, molecular design breeding is basically at the concept stage, without operational technology process and breeding practice. On the basis of breeding data from Beijing Kings Nower Seed S&T CO, LTD, this paper explored a set of methods for maize design breeding based on molecular detection and phenotypic testing information. Firstly, the parents of the combinations were obtained from the inbred lines with high homozygosity through SSR detecting and good comprehensive traits through field testing; secondly, the heterosis rate of the parents was got by calculating both the genetic differences and phenotypic differences of the parents according to the SSR detection results and field testing results respectively to express the special combining ability of their own; finally, in this paper it constructed the hybridization group model by using the comprehensive characters, special combining ability, orthogonal anti value, calculated the comprehensive characters, advantages, disadvantages of the hybridizations, and screened hybrid combinations to the next round field breeding, it also developed a software system of hybrid combination to support the technology route. Applying the software, 37 hybrid combinations resistance to Ralstonia solanacearum were got based on 179 inbred lines with molecular and phenotypic data. Thus, the method and software preliminary provides technical support for our country to carry out and perfect the molecular design breeding.

Leaf is one of the most important photosynthetic organs of rapeseed (Brassica napus L.). To quantify relationships between the leaf curve and the corresponding leaf biomass for rapeseed on main stem, this paper presents a biomass-based leaf curvilinear model for rapeseed. Various model variables, including leaf length, bowstring length, tangential angle, and bowstring angle, were parameterized based on data derived from the field experiments with varieties, fertilizer, and transplanting densities during 2011 to 2012, and 2012 to 2013 growing seasons. And then we analysed the biological significance of curvilinear equation for straight leaves, constructed the straight leaf probabilistic model on main stem, quantified the relationship between leaf curvature and the corresponding leaf biomass, and constructed the leaf curvilinear model based on the assumption and verification of the curvilinear equation form for curving leaf. The probability of straight leaf can be quantified with piecewise function according to the different trend in the normalized leaf ranks ((0, 0.4], and (0.4, 1]). The leaf curvature decreased with the increasing of leaf biomass, and can be described with reciprocal function. The curve of straight leaf and the curving leaf can be simulated by linear equation and the quadratic function, respectively. Our models were validated with the independent dataset from the field experiment, and the results indicated that the model could effectively predict the straight leaf probability and leaf curvature, which would be useful for linking the rapeseed growth model with the rapeseed morphological model, and set the stage for the development of functional-structural rapeseed models.

In this paper, a series of paddy drying experiments were conducted on a deep fixed-bed and we investigated the effect rules of five influencing parameters on drying time of paddy. By using the quadratic orthogonal rotation combination design, the nonlinear function between the drying time and the five influencing parameters are built up. Then a detailed study of the qualitative and quantitative effect rules of each influencing factor is elaborated. The results of this paper conclude the rules of how drying parameters influencing the dry time of paddy. And also, it reveals how to reduce paddy drying time and improve the productivity, which have significance in practical productions.

Recently the picking technology of high value crops has become a new research hot spot, and the image segmentation and recognition are still the key link of fruit picking robot. In order to realize the lotus image recognition, this paper proposes a new feature extraction method combined with shape and color, and uses the K-Means clustering algorithm to get lotus recognition model. Before the feature extraction, the existing pulse coupled neural network segmentation algorithm, combined with morphological operation, is used to achieve nice segmentation image, including lotus, lotus flower, lotus leaf and stems. Then in the feature extraction processing, the chromatic aberration method and the moment invariant algorithm are selected to extract the color and shape features of the segmented images, in which principal component analysis algorithm is selected to reduce the dimension of the color and shape features to achieve principal components of lotus, lotus flower, lotus leaf and stems. In the experiment, K-Means clustering algorithm is used to get lotus recognition model and four clustering centers according to above principal components of training samples about lotus, lotus flower, lotus leaf and stems; then the testing experiment is applied to validate the recognition model. Experimental results shows that the correct recognition rate is 90.57 % about 53 testing samples of lotus, and the average recognition time is 0.0473 s, which further indicates that the feature extraction algorithm is applicable to lotus feature extraction, and K-Means algorithm is simple, reliable and feasible, providing a theoretical basis for positioning and picking of lotus harvest robot.

Primary ramification morphogenesis has a significant influence on the yield of rapeseed. In order to quantify the relationship between rapeseed architecture indices and the organ biomass, a rapeseed primary ramification structural model based on biomass were presented. Intended to explain effects of cultivars and environmental conditions on rapeseed PR morphogenesis. The outdoor experiment with cultivars: Ningyou 18 (V1, conventional), Ningyou 16 (V2, conventional) and Ningza 19 (V3, hybrid), and designed treatment of cultivar-fertilizer, cultivar-fertilizer-density, and cultivar tests in 2011–2012 and 2012–2013. The experimental result showing that the leaf blade length of PR, leaf blade width of PR, leaf blade bowstring length of PR, PR length, and PR diameter from 2011 to 2012 were goodness, and their d _a values and RMSE values were −1.900 cm, 5.033 cm (n = 125); −0.055 cm, 3.233 cm (n = 117); 0.274 cm, 2.810 cm (n = 87); −0.720 cm, 3.272 cm (n = 90); 0.374 cm, 0.778 cm (n = 514); 0.137 cm, 1.193 cm (n = 514); 0.806 cm, 8.990 cm (n = 145); and −0.025 cm, 0.102 cm (n = 153), respectively. The correlations between observation and simulation in the morphological indices were significant at P < 0.001, but the d _ap values were <5 % for the second leaves length and the third leaves length, leaf blade bowstring length, PR length, and PR diameter, which indicated that the model’s accuracy was high. The models established in this paper had definite mechanism and interpretation, and the impact factors of N, the ratio of the leaf length to leaf dry weight of primary ramification (PRRLW), and the partitioning coefficient of leaf blade dry weight of primary ramification (PRCPLB) were presented, enabled to develop a link between the plant biomass and its morphogenesis. Thus, the rapeseed growth model and the rapeseed morphological model can be combined through organ biomass, which set a reference for the establishment of FSPMs of rapeseed.

This research was conducted to investigate the application of detecting plastic films in cotton using visible and near-infrared hyperspectral imaging. A line-scan hyperspectral imaging system (326–1100 nm) was used to detect plastic films mixed with cotton which was an important quality issue. Hyperspectral reflectance images were acquired and difference spectra of cotton and plastic films were extracted and analyzed to determine the dominant wavelengths. Also, as one of the most commonly used methods for dimensionality reduction, principal component analysis (PCA) was chosen to process the hyperspectral images. Afterwards, effective wavelengths were selected by analyzing the first three principal components (PCs) and six single-band images at 473.24 nm, 497.29 nm, 530.6 nm, 670.81 nm, 674.71 nm, and 955.68 nm were extracted respectively. Finally, the selected wavelengths were validated to prove the effectiveness. The results indicated that the selected wavelengths could be able to detect plastic films in cotton instead of the whole wavelengths.

A precision seeding control system was developed based on GPS, which could achieve real-time adjustment of seeding speed to ensure seed distribution uniformity. Parameters including seeding rate, row spacing and hole number of seed metering device were entered by users which were transferred by CAN bus to the controller. Its electronic speed control system could work out rotational speed of sowing axis matching with operating speed of tractor automatically. Seed metering device was driven by the hydraulic motor. The field experiment results demonstrated that rate of spacing of normally sown seeds and variable coefficient of this system comparing to traditional land wheel driven planter was relatively 94.1 %, 24.6 % and 89.1 %, 33.1 % while operating speed was 5.9 km/h. If the variation range of seeding spacing shrinks 40 % according the standard request, rate of spacing of normally sown seeds of precision seeding control system was 89.7 % exceeded the traditional land wheel 23 %.

Predicting rice yield plays a significant role in preparing production plan and relevant decision-making for following year. The paper adopts ARIMA time series algorithm taking rice yield per hectare collected between 1991 and 2010 from different prefecture-level cities of Heilongjiang Province as the object of time series processing, to analyze the rice yield recorded between 1991 and 2009 of different prefecture-level cities and to build prediction model of rice yield. By using the model, the rice yield of different prefecture-level cities in 2010 is predicted. The predicted and actual space distribution of rice yield is obtained using ArcGIS software. And by conducting spatial data analysis, the space distribution diagram is mapped out. It is analyzed that the accuracy of predicted model on average attains over 95 %, featuring good prediction effect.

Longicorn beetle is an important wood-boring insect. In order to probe into the effective eco-friendly method controlling longicorn beetle, this document recommends a new dual optical digital machine for killing the larva of longicorn beetle with microwave based on the arduino. This machine uses the arduino with temperature controller as the core, touch switch, and a temperature sensor as important parts. By using arduino and sensor measurement technology, a kind of economical close-loop grinding control system is formed to control the temperature with the control of duty cycle and frequency of pulses. The results of the operation showed that the temperature controlling, microwave power is adjustable, man - machine dialogue is woven and seized. It is reliable to be easy to operate and high in automation.

In view of the problems existing in the development and utilization of water resources in Sanjiang Plain, this paper made a study on the risk assessment of water resources shortage in Sanjiang Plain. From the perspective of water resources shortage risk, analytic hierarchy process was applied to establish assessment index system and evaluation criteria, and then make a comprehensive assessment and regional difference analysis of water resources shortage risk in Sanjiang Plain. Research results showed that Shuangyashan City belonged to high degree of water resources shortage risk, while Jixi, Hegang, Jiamusi, Qitaihe, Muling and Yilan belonged to extremely high water resources shortage risk, which provided a theoretical principle for sustainable and efficient utilization of regional water resources.

The soil nutrition is an important indicator of soil fertility. The method K-means and FCM are always used to evaluating the soil fertility, but the cluster number need to be set, and the outlier couldn’t be eliminated accurately, and there is the deviation between the real result and the soil fertility. So the paper applied the FUMF to analysis the soil nutrient data of Nong An county for eight years, 2005–2012. The result show that the low fertility soils gradually decreased from 2005 to 2012 by precision fertilization, and the moderate and high fertility soil was rising, the overall soil fertility of Nong An had improved significantly. The analysis result was consistent with the actual situation, The FUMF algorithm is proved that was an effective evaluate method of the soil fertility evaluation. It has the practical significance to analyze the large number of soil fertility of high complexity and interactive, it also provided the technical support for precision fertilization decision-making.

The effect of agronomic factors including seedling age, transplanting density and net nitrogen ratio on the yield and profit of a single-cropping hybrid late rice (Oryza sativa) cultivar ‘Yongyou 12’ was investigated. The orthogonally rotational combination experimental design was used in setting field parameter test. Regression models assuming yield or profit as the objective function were constructed, and the impact from each individual or combinations of the three agronomic factors on the yield and profit of the crop was estimated. A computer simulation was performed to select the optimized agronomic scheme of growing rice ‘Yongyou 12’ under the field conditions used in this study.

Milk somatic cell image segmentation has a very important effect on milk quality analysis. In this paper, a new segmentation method is proposed for color milk somatic cell image. Aiming at the milk somatic cell image, a new method for image segmentation is proposed. First of all, selecting the appropriate color components by reducing the dimension of RGB color image; Then using the k-means algorithm to segment the low dimension image; Finally, the segmented images are fused by the region splitting and merging process. The experimental results show that the proposed method is better than the original three-dimensional (3D) color space segmentation method. The method improves in the performance and running time, the correct segmentation rate reached 98.4 %. Therefore, this method has certain feasibility.

The detection of pesticide residued in fruit is an important concern for consumers. Surface enhanced Raman spectroscopy (SERS) coupled with gold colloid was applied to analyze two kinds of pesticides (phosmet, chlorpyrifos) which were mainly used on the navel orange. The concentration of the phosmet samples of range from 3 to 33 mg/L and chlorpyrifos samples of range from 4 to 34 mg/L. Using Partial least squares (PLS) regression and the different preprocessing method for the spectral data analyses, and different pretreatment methods such as the Savitzky-Golay were compared. The optimal model of phosmet pesticide and chlorpyrifos pesticide were set up. The prediction correlation coefficient (R) and the root mean square error of prediction (RMSEP) of phosmet pesticide were 0.924 and 4.293 mg/L; The R and RMSEP of chlorpyrifos pesticide were 0.715 and 6.646 mg/L. It indicated that SERS technology is a effective method in the field of pesticide residue detection in fruit.

In the traditional information service for freshwater fish, there existed several crucial problems involving in the information classification and content organization, which was not clear and specific to serve market and consultancy etc., information refreshing not timely, and information sharing difficult to take, and so on. These problems seriously restricted the development of the freshwater fish and the other relative industries. The “Internet + Strategy”, which has been advocated and carried out in China since 2015, has promoted the formulation of new ideas for information service model to construct and enhance modern production and circulation of the freshwater fish. Based on the analysis of the core and characteristics of information service in the production and circulation for the modern freshwater fish, this study constructed a new information service model consisting of production, sale, transaction and circulation. In the development of the freshwater fish industry for the future, it is necessary and beneficial to accelerate the construction of the information service platform, integrate the information service as well as the improvement of information resources with the merchandise production and circulation, and strengthen the brand building of enterprises, especially the leading enterprises.

Pruning treatment is one of the important management practices of perennial fruit tree cultivation. Combining fruit tree growth theory with mathematics model and software development is an effective way in agricultural research. Virtual apple tree is built as a case to study pruning treatment in this paper. First, architectural development of apple tree was analyzed and stored in xml file. Then, the simulation software of apple tree pruning is build based on the Qt framework and OpenGL graphics library. The interactive pruning and automatic pruning with setting conditions are realized in the software. Reaction laws of apple tree pruning are extracted from the analysis on the experiment data. Stochastic 3D apple tree architectures after being pruned also can be simulated. Result indicates that simulation is efficient, accurate and timely judgment of pruning reaction is possible. This work is the foundation of future research, which will simulate the apple tree development of architecture and biomass after pruning treatment over time.

Village and town are the basic unit of our society, there vigorously development in social management and innovative plays an important role in promoting economic growth, maintaining social stability, improving people’s living environment and other aspects, but the traditional government management mode can not meet the claim of the social development of the villages. Based on the idea of grid management model, this paper presents the principles and method of grid cell division in the process of the gird management in the village community, and to analyze the division method so as to provide evidence for the scientific, rational division of community grid unit.

Water quality monitoring technology based on optical method is the trend for modern water environmental monitoring. Compared with the traditional monitoring methods, Spectroscopy is a more simple, a small amount of reagent consumption, good repeatability, high accuracy and rapid detection of significant advantages, which is very suitable for rapid and on-line monitoring determination of environment water samples COD. This paper summarized the status and research progress of optical methods for monitoring of COD in water. The basic principle of traditional analysis methods and optical methods for measuring COD in water were brief described, and compared to the characteristic of different waveband of the detection of COD. The principles and applications of spectroscopic methods commonly used spectral preprocessing methods and calibration methods were listed, and also introduced the progress of optical sensors. Finally, the future research focus and direction of spectroscopic methods were prospected.

The non-destructive, rapid and accurate monitoring diagnosis of rapeseed diseases is of significance for sustainable development of rapeseed production and environment protection. The spectrum data of rapeseed leaf leukoplakia were collected in the experimental Farm of Jiangsu Academy of Agricultural Science in 2013 and 2014. Firstly, the common distinctive bands of the disease and the health were found by comparing reflectance spectrum of leaves in the field and under black background. The results showed that with the progress of the growth period, reflectance of disease leaves decreased earlier than healthy leaves. It was the best period to identify rapeseed leukoplakia from 11 days after early anthesis to 9 days after finish flowering to identify rapeseed leukoplakia in the field due to during this period the reflectance of healthy leaves remained at 35 % while the disease diseased to 30 %. The sensitive band was in the range of 760–1080 nm. The correlation among disease index (DI), agronomic parameters, and the reflectance of the disease samples were analysed, and the results showed that there were high correlations between DI, and agronomic parameters and reflectance, e.g., the correlation between the leaf moisture content and the reflectance in 460 nm, 550 nm, 650 nm, 710 nm, 760 nm, 1480 nm, and 1600 nm, between the leaf nitrogen content and the reflectance in 810 nm, 870 nm, 1080 nm, 1280 nm, 1320 nm, 1540 nm, 1600 nm, 1650 nm, and 1700 nm, and between the SPAD value and the reflectance in 1200 nm, 1280 nm, and 1540 nm had significance with p < 0.01. The quantitative models of agronomic parameters based on reflectance were developed by stepwise regression, principal component analysis, and curve fitting. The data of rapeseed leukoplakia in 2013 and rapeseed virus in 2014 were used to test. The results showed that in the same disease test, the quantitative models of moisture content based on reflectance were fit well. In the different disease test, the quantitative models were fit badly except the model of moisture content. The model on moisture content performed reasonably well, though performance of precision could probably be improved by further analysis, and the paper would provide a basis for spectrum-based identifying of rapeseed leaf leukoplakia.