Development of a RBFNN prediction model for carrot quality based on meteorological temperatures at vegetable stations

To evaluate and predict the quality of carrots during logistics process in North China under extreme temperature conditions,quality indicator changes of carrots were investigated,and temperature-coupled quality prediction models were developed.Seven temperatures were selected from meteorological temperature data by cluster analysis to simulate the changes in extreme temperatures during the short-term transportation of carrots.No carrots rotted during the 48h storage period.Under both isothermal and nonisothermal conditions,weight loss andΔE increased while the firmness and sensory evaluation(SE)decreased.The RBFNN performed better than the Arrhenius model in predicting weight loss andΔE,with R^(2)>0.97,MSE<0.009 and relative errors within±18%.The results of the predictive confidence level and standardized residual indicated the good performance of the RBFNN model.The temperature-coupled prediction models of RBFNN were promising candidates for predicting the quality of vegetable products and therefore reducing economic loss of vegetable industry.

Research and Experiment on Performance Stability of Facility Green-house Environmental Monitoring System

At present,the safety and stability of most facility greenhouse environment monitoring systems are seldom considered. In order to improve the stability of data transmission in environment and prevent the system failure caused by the fault of coordinator,a mechanism based on Zigbee coordinator to improve the stability of the whole system is proposed to ensure the security of wireless data transmission. Finally,the system is tested,and the results show that the system can effectively ensure the fault-free transmission of collected environmental data.

Developing improved measures of non-Gaussianity and Gaussianity for quantum states based on normalized Hilbert–Schmidt distance

Non-Gaussianity of quantum states is a very important source for quantum information technology and can be quantified by using the known squared Hilbert–Schmidt distance recently introduced by Genoni et al.(Phys. Rev. A 78 042327(2007)). It is, however, shown that such a measure has many imperfects such as the lack of the swapping symmetry and the ineffectiveness evaluation of even Schr?dinger-cat-like states with small amplitudes. To deal with these difficulties, we propose an improved measure of non-Gaussianity for quantum states and discuss its properties in detail. We then exploit this improved measure to evaluate the non-Gaussianities of some relevant single-mode non-Gaussian states and multi-mode non-Gaussian entangled states. These results show that our measure is reliable. We also introduce a modified measure for Gaussianity following Mandilara and Cerf(Phys. Rev. A 86 030102(R)(2012)) and establish a conservation relation of non-Gaussianity and Gaussianity of a quantum state.

Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

Multi-Task Timing Assignment Algorithm for Intelligent Production of Vegetables in Open Field

Vegetable production in the open field involves many tasks,such as soil preparation,ridging,and transplanting/sowing.Different tasks require agricultural machinery equipped with different agricultural tools to meet the needs of the operation.Aiming at the coupling multi-task in the intelligent production of vegetables in the open field,the task assignment method for multiple unmanned tractors based on consistency alliance is studied.Firstly,unmanned vegetable production in the open field is abstracted as a multi-task assignment model with constraints of task demand,task sequence,and the distance traveled by an unmanned tractor.The tight time constraints between associated tasks are transformed into time windows.Based on the driving distance of the unmanned tractor and the replacement cost of the tools,an expanded task cost function is innovatively established.The task assignment model of multiple unmanned tractors is optimized by the consensus based bundle algorithm(CBBA)with time windows.Experiments show that the method can effectively solve task conflict in unmanned production and optimize task allocation.A basic model is provided for the cooperative task of multiple unmanned tractors for vegetable production in the open field.

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield.Biomass is also a key trait in increasing grain yield by crop breeding.The aims of this study were(i)to identify the best vegetation indices for estimating maize biomass,(ii)to investigate the relationship between biomass and leaf area index(LAI)at several growth stages,and(iii)to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network(DNN)algorithm.The results showed that biomass was associated with all vegetation indices.The three-band water index(TBWI)was the best vegetation index for estimating biomass and the corresponding R2,RMSE,and RRMSE were 0.76,2.84 t ha−1,and 38.22%respectively.LAI was highly correlated with biomass(R2=0.89,RMSE=2.27 t ha−1,and RRMSE=30.55%).Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm(R2=0.83,RMSE=1.96 t ha−1,and RRMSE=26.43%).Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices(R2=0.91,RMSE=1.49 t ha−1,and RRMSE=20.05%).Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass.Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices(R2=0.87,RMSE=1.84 t ha−1,and RRMSE=24.76%).The DNN algorithm was effective in improving the estimation accuracy of biomass.It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops,thereby performing yield prediction,but not for fruit tree species such as jujube tree(Zizyphus jujuba).The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter.The model was established using data collected from dedicated field experiments performed in 2016-2018.Simulated growth dynamics of dry weights of leaves,stems,fruits,total biomass and leaf area index(LAI) agreed well with measured values,showing root mean square error(RMSE) values of 0.143,0.333,0.366,0.624 t ha^-1 and 0.19,and R2 values of 0.947,0.976,0.985,0.986 and 0.95,respectively.Simulated phenological development stages for emergence,anthesis and maturity were 2,3 and 3 days earlier than the observed values,respectively.In addition,in order to predict the yields of trees with different ages,the weight of new organs(initial buds and roots) in each growing season was introduced as the initial total dry weight(TDWI),which was calculated as averaged,fitted and optimized values of trees with the same age.The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI.The modelling performance was significantly improved when it considered TDWI integrated with tree age,showing good global(R2≥0.856,RMSE≤0.68 t ha^-1) and local accuracies(mean R2≥0.43,RMSE≤0.70 t ha^-1).Furthermore,the optimized TDWI exhibited the highest precision,with globally validated R2 of 0.891 and RMSE of 0.591 t ha^-1,and local mean R2 of 0.57 and RMSE of 0.66 t ha^-1,respectively.The proposed model was not only verified with the confidence to accurately predict yields of jujube,but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

Assessment of Land Suitability Potentials for Selecting Winter Wheat Cultivation Areas in Beijing,China,Using RS and GIS

It is very important to provide reference basis for winter wheat quality regionalization of cultivation area. The aim of this article was based on factors affecting wheat quality and setting realistic spatial models in each part of the land for assessment of land suitability potentials in Beijing, China. The study employed artificial neural network （ANN） analysis to select factors and evaluate the relative importance of selected environment factors on wheat grain quality. The spatial models were developed and demonstrated their use in selecting the most suitable areas for the winter wheat cultivation. The strategy overcomes the non-accurate traditional statistical methods. Satellite images, toposheet, and ancillary data of the study area were used to find tillable land. These categories were formed by integrating the various layers with corresponding weights in geographical information system （GIS）. An integrated land suitability potential （LSP） index was computed considering the contribution of various parameters of land suitability. The study demonstrated that the tillable land could be categorized into spatially distributed agriculture potential zones based on soil nutrient and assembled weather factors using RS and GIS as not suitable, marginally suitable, moderately suitable, suitable, and highly suitable by adopting the logical criteria. The sort of land distribution map made by the factors with their weights showed more truthfulness.

Assimilation of Remote Sensing and Crop Model for LAI Estimation Based on Ensemble Kalman Filter

Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm （EnKF） assimilation by combing the crop growth model （CERES-Wheat） with remote sensing data, but also optimized and updated the key parameters （LAI） of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.

Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test

Sensitivity analysis （SA） is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application. The objectives were to （i） determine influential and non-influential parameters with respect to above ground biomass （AGB）, canopy cover （CC）, and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments （rainfall, normal irrigation, and over-irrigation）; and （ii） generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results. In this study, field experiments were conducted during the 2012-2013,2013-2014, and 2014-2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China. The extended Fourier amplitude sensitivity test （EFAST） method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013-2014 growing season were used to calibrate the AquaCrop model, and data from 2012-2013 and 2014-2015 growing seasons were val- idated. For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis. For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions （normal irrigation and over-irrigation）, there were more sensitive parameters under water stress （rainfall）, while root development parameters were more sensitive. For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress. Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters （growing degree-days （GDD） from sowing to the maximum rooting depth （root） and the maximum effective rooting depth （rtx）） under water stress than under no water stress. The results showed that there was higher accuracy under water stress than under no water stress. This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.

CNN intelligent early warning for apple skin lesion image acquired by infrared video sensors

Video sensors and agricultural IoT(internet of things) have been widely used in the informationalized orchards.In order to realize intelligent-unattended early warning for disease-pest,this paper presents convolutional neural network(CNN) early warning for apple skin lesion image,which is real-time acquired by infrared video sensor.More specifically,as to skin lesion image,a suite of processing methods is devised to simulate the disturbance of variable orientation and light condition which occurs in orchards.It designs a method to recognize apple pathologic images based on CNN,and formulates a self-adaptive momentum rule to update CNN parameters.For example,a series of experiments are carried out on the recognition of fruit lesion image of apple trees for early warning.The results demonstrate that compared with the shallow learning algorithms and other involved,wellknown deep learning methods,the recognition accuracy of the proposal is up to 96.08%,with a fairly quick convergence,and it also presents satisfying smoothness and stableness after convergence.In addition,statistics on different benchmark datasets prove that it is fairly effective to other image patterns concerned.

Asymptotic Analysis of Transmission Capacities for Overlaid Spread-Spectrum Wireless Networks

We study the transmission capacities of two coexisting spread-spectrum wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. We defi ne transmission capacity as the product among the density of transmissions, the transmission rate, and the successful transmission probability. The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, while the SR network limits its interference to the PR network by carefully controlling the density ofits transmitters. Considering two types of spread-spectrum transmission schemes (FH-CDMA and DS-CDMA) and the channel inversion power control mechanism, we quantify the transmission capacities for these two networks based on asymptotic analysis. Our results show that if the PR network permits a small increase ofits outage probability, the sum transmission capacities of the two networks (i.e., the overall spectrumefficiency per unit area) will be boosted significantly over that of a single network.

Layout Optimization for Greenhouse WSN Based on Path Loss Analysis

When wireless sensor networks (WSN) are deployed in the vegetablegreenhouse with dynamic connectivity and interference environment, it is necessary to increase the node transmit power to ensure the communication quality,which leads to serious network interference. To offset the negative impact, thetransmit power of other nodes must also be increased. The result is that the network becomes worse and worse, and node energy is wasted a lot. Taking intoaccount the irregular connection range in the cucumber greenhouse WSN, wemeasured the transmission characteristics of wireless signals under the 2.4 Ghzoperating frequency. For improving network layout in the greenhouse, a semiempirical prediction model of signal loss is then studied based on the measureddata. Compared with other models, the average relative error of this semi-empiricalsignal loss model is only 2.3%. Finally, by combining the improved networktopology algorithm and tabu search, this paper studies a greenhouse WSN layoutthat can reduce path loss, save energy, and ensure communication quality. Giventhe limitation of node-degree constraint in traditional network layout algorithms,the improved algorithm applies the forwarding constraint to balance network energyconsumption and constructs asymmetric network communication links. Experimentalresults show that this research can realize the energy consumption optimization ofWSN layout in the greenhouse.

Visualization of Chlorophyll Content Distribution in Apple Leaves Based on Hyperspectral Imaging Technology

We took distribution visualization of chlorophyll content in apple leaves to estimate the nutrient content and growth levels of apple leaves. 130 mature and non-destructive apple leaves were collected, and imaging spectroscopy data were collected by SOC710VP hyperspectral imager. The chlorophyll content of the leaves was determined on the spectral information of the leaves. After pre-processing, we took linear wavelength stepwise regression method to choose the sensitive wavelength of chlorophyll content. And then we established partial least squares, principal component analysis and stepwise regression model. Finally, the chlorophyll content distribution visualization was realized. The results showed that the sensitive wavelengths of the chlorophyll content were 712.50 nm, 509.95 nm, 561.22 nm, 840.62 nm, 696.67 nm and 987.91 nm. The R2, RMSE, RE of the optical chlorophyll content estimation model, and the principal component analysis regression model, were 0.800, 0.319 and 26.4%. The chlorophyll content of each pixel on the hyperspectral image of apple leaves was calculated by the best estimation model and we completed the visualization distribution of chlorophyll content, which provided a technical support for the rapid detection of nutrient distribution.

Evaluation and Exploitation of Retrieval Algorithms for Estimating Biophysical Crop Variables Using Sentinel-2,Venus,and PRISMA Satellite Data

This paper is devoted to the development and testing of the optimal procedures for retrieving biophysical crop variables by exploiting the spectral information of current multispectral optical satellite Sentinel-2 and Venus and in view of the advent of the new Sino-EU hyperspectral satellite(e.g.,PRISMA,EnMAP,and GF-5).Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index(LAI)and Leaf chlorophyll content(Cab)were evaluated:non-kernel-based and kernel-based Machine Learning Regression Algorithms(MLRA);Sentinel-2 and Venus data comparison for the analysis of the durum wheat-growing season.Results show that for Sentinel-2 data,Gaussian Process Regression(GPR)was the best performing algorithm for both LAI(R 2=0.89 and RMSE=0.59)and Cab(R 2=0.70 and RMSE=8.31).Whereas,for PRISMA simulated data the Kernel Ridge Regression(KRR)was the best performing algorithm among all the other MLRA(R 2=0.91 and RMSE=0.51)for LAI and(R 2=0.83 and RMSE=6.09)for Cab,respectively.Results of Sentinel-2 and Venus data for durum wheat-growing season were consistent with ground truth data and confirm also that SWIR bands,which are used as tie-points in the PROSAIL inversion,are extremely useful for an accurate retrieving of crop biophysical parameters.

A utility-optimal backoff algorithm for wireless sensor networks

A novel backoff algorithm in CSMA/CA-based medium access control (MAC) protocols for clustered sensor networks was proposed. The algorithm requires that all sensor nodes have the same value of contention window (CW) in a cluster, which is revealed by formulating resource allocation as a network utility maximization problem. Then, by maximizing the total network utility with constrains of minimizing collision probability, the optimal value of CW (Wopt) can be computed according to the number of sensor nodes. The new backoff algorithm uses the common optimal value Wopt and leads to fewer collisions than binary exponential backoff algorithm. The simulation results show that the proposed algorithm outperforms standard 802.11 DCF and S-MAC in average collision times, packet delay, total energy consumption, and system throughput.

Developments and Applications of Data Deidentification Technology under Big Data

In this age characterized by rapid growth in the volume of data,data deidentification technologies have become crucial in facilitating the analysis of sensitive information.For instance,healthcare information must be processed through deidentification procedures before being passed to data analysis agencies in order to prevent any exposure of personal details that would violate privacy.As such,privacy protection issues associated with the release of data and data mining have become a popular field of study in the domain of big data.As a strict and verifiable definition of privacy,differential privacy has attracted noteworthy attention and widespread research in recent years.In this study,we analyze the advantages of differential privacy protection mechanisms in comparison to traditional deidentification data protection methods.Furthermore,we examine and analyze the basic theories of differential privacy and relevant studies regarding data release and data mining.

Research advance in phenotype detection robots for agriculture and forestry

The continuous development of robot technology has made phenotype detection robots a key for extracting and analyzing phenotyping data in agriculture and forestry.The different applications of agricultural robots and phenotype detection robots were discussed in this article.Further,the structural characteristics and information interaction modes of the current phenotype detection robots were summarized from the viewpoint of agriculture and forestry.The publications with keywords related to clustering distribution were analyzed and the currently available phenotype robots were classified.Additionally,a conclusion on the design criteria and evaluation system of plant phenotype detection robots was summarized and obtained,and the challenges and future development direction were proposed,which can provide a reference for the design and applications of agriculture and forestry robots.

Transcriptomics and metabolomics analyses provide insights into postharvest ripening and senescence of tomato fruit under low temperature

Tomato is one of the most important vegetable crops in the world and is a model plant used to study the ripening of climacteric fleshy fruit.During the ripening process of tomato fruit,flavor and aroma metabolites,color,texture and plant hormones undergo significant changes.However,low temperatures delayed the ripening process of tomato fruit,inhibiting flavor compounds and ethylene production.Metabolomics and transcriptomics analyses of tomato fruit stored under low temperature(LT,5°C)and room temperature(RT,25°C)were carried out to investigate the effects of storage temperature on the physiological changes in tomato fruit after harvest.The results of transcriptomics changes revealed that the differentially expressed genes(DEGs)involved in tomato fruit ripening,including several kinds of transcription factors(TFs)(TCP,WRKY,MYB and bZIP),enzymes involved in cell wall metabolism[beta-galactosidase(β-GAL),pectinesterase(PE)and pectate lyase(PL),cellulose and cellulose synthase(CESA)],enzymes associated with fruit flavor and aroma[acetyltransferase(AT),malic enzyme(ME),lipoxygenase(LOX),aldehyde dehydrogenase(ALDH),alcohol dehydrogenase(ADH)and hexokinase(HK)],genes associated with heat stress protein 70 and genes involved in the production of plant hormones such as Ethylene responsive factor 1(ERF1),Auxin/indoleacetic acids protein(AUX/IAA),gibberellin regulated protein.Based on the above results,we constructed a regulatory network model of the effects of different temperatures during the fruit ripening process.According to the analysis of the metabolomics results,it was found that the contents of many metabolites in tomato fruit were greatly affected by storage temperature,including,organic acids(L-tartaric acid,a-hydroxyisobutyric acid and 4-acetamidobutyric acid),sugars(melezitose,beta-Dlactose,D-sedoheptulose 7-phosphate,2-deoxyribose 1-phosphate and raffinose)and phenols(coniferin,curcumin and feruloylputrescine).This study revealed the effects of storage temperature on postharvest tomato fruit and provided a basis for further understanding of the molecular biology and biochemistry of fruit ripening.