Standard Framework Construction of Technology and Equipment for Big Data in Crop Phenomics

Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional geno-mics,digital breeding,and smart cultivation.Despite this advancement,the lack of standards for the cre-ation and usage of crop phenomics technology and equipment has become a bottleneck,limiting the industry’s high-quality development.This paper begins with an overview of the crop phenotyping indus-try and presents an industrial mapping of technology and equipment for big data in crop phenomics.It analyzes the necessity and current state of constructing a standard framework for crop phenotyping.Furthermore,this paper proposes the intended organizational structure and goals of the standard frame-work.It details the essentials of the standard framework in the research and development of hardware and equipment,data acquisition,and the storage and management of crop phenotyping data.Finally,it discusses promoting the construction and evaluation of the standard framework,aiming to provide ideas for developing a high-quality standard framework for crop phenotyping.

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.

Delineation and Scale Effect of Precision Agriculture Management Zones Using Yield Monitor Data Over Four Years

In this study, precision agriculture management zones were delineated using yield data over four years from the combine harvester equipped with yield monitor and DGPS receiver. Relative yields measured during each year were interpolated to 4 m2 grid size using ordinary kriging. The resultant interpolated yield maps were averaged across years to create a map of the mean relative yield, which was then used for cluster analysis. The mean yield map of post-classification was processed by applying majority filtering with window sizes that were equivalent to the grid sizes of 12, 20, 28, 36, 44, 52 and 60 m. The scale effect of management zones was evaluated using relative variance reduction, test of significant differences of the means of yield zones, spatial fragmentation, and spatial agreement. The results showed that the post-classification majority filtering （PCMF） eliminated lots of isolated cells or patches caused by random variation while preserving yield means, high variance reduction, general yield patterns, and high spatial agreement. The zoned result can be used as yield goal map for preplant or in-season fertilizer recommendation in precision agriculture.

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.

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.

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.

Relationship of 2 100-2 300 nm Spectral Characteristics of Wheat Canopy to Leaf Area Index and Leaf N as Affected by Leaf Water Content

The effects of leaf water status in a wheat canopy on the accuracy of estimating leaf area index (LAI) and N were determined in this study using extracted spectral characteristics in the 2 000-2 300 nm region of the short wave infrared (SWI) band. A newly defined spectral index, relative adsorptive index in the 2000-2300 nm region (RAI2000-2300), which can be calculated by RAI2000-2300 = (R2224 - R2054) (R2224 + R2054)-1 with R being the reflectance at 2224 or 2054 nm, was utilized. This spectral index, RAI2000-2300, was significantly correlated (P < 0.01) with green LAI and leaf N concentration and proved to be potentially valuable for monitoring plant green LAI and leaf N at the field canopy scale. Moreover, plant LAI could be monitored more easily and more successfully than plant leaf N. The study also showed that leaf water had a strong masking effect on the 2 000-2 300 nm spectral characteristics and both the coefficient between RAI2000-2300 and green LAI and that between RAI2000-2300 and leaf N content decreased as leaf water content increased.

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

The use of remote sensing to monitor nitrogen(N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden.In this study,we model the total leaf N concentration(TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution(VND).The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model.The results show that:(1) the vertical distribution law of LNC was distinct,presenting a quadratic polynomial tendency from the top layer to the bottom layer.(2) The effective layer for remote sensing detection varied at different growth stages.The entire canopy,the three upper layers,the three upper layers,and the top layer are the effective layers at the jointing stage,flag leaf stage,flowering stages,and filling stage,respectively.(3) The TLNC model considering the VND has high predicting accuracy and stability.For models based on the greenness index(GI),mND705(modified normalized difference 705),and normalized difference vegetation index(NDVI),the values for the determining coefficient(R2),and normalized root mean square error(nRMSE) are 0.61 and 8.84%,0.59 and 8.89%,and 0.53 and 9.37%,respectively.Therefore,the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.

Detection of Internal Leaf Structure Deterioration Using a New Spectral Ratio Index in the Near-Infrared Shoulder Region

Spectral reflectance in the near-infrared （NIR） shoulder （750-900 nm） region is affected by internal leaf structure, but it has rarely been investigated. In this study, a dehydration treatment and three paraquat herbicide applications were conducted to explore how spectral reflectance and shape in the NIR shoulder region responded to various stresses. A new spectral ratio index in the NIR shoulder region （NSRI）, defined by a simple ratio of reflectance at 890 nm to reflectance at 780 nm, was proposed for assessing leaf structure deterioration. Firstly, a wavelength-independent increase in spectral reflectance in the NIR shoulder region was observed from the mature leaves with slight dehydration. An increase in spectral slope in the NIR shoulder would be expected only when water stress developed sufficiently to cause severe leaf dehydration resulting in an alteration in cell structure. Secondly, the alteration of leaf cell structure caused by Paraquat herbicide applications resulted in a wavelength-dependent variation of spectral reflectance in the NIR shoulder region. The NSRI in the NIR shoulder region increased significantly under an herbicide application. Although the dehydration process also occurred with the herbicide injury, NSRI is more sensitive to herbicide injury than the water-related indices （water index and normalized difference water index） and normalized difference vegetation index. Finally, the sensitivity of NSRI to stripe rust in winter wheat was examined, yielding a determination coefficient of 0.61, which is more significant than normalized difference vegetation index （NDVI）, water index （WI） and normalized difference water index （NDWI）, with a determination coefficient of 0.45, 0.36 and 0.13, respectively. In this study, all experimental results demonstrated that NSRI will increase with internal leaf structure deterioration, and it is also a sensitive spectral index for herbicide injury or stripe rust in winter wheat.

An entirely new approach based on remote sensing data to calculate the nitrogen nutrition index of winter wheat

The nitrogen nutrition index(NNI)is a reliable indicator for diagnosing crop nitrogen(N)status.However,there is currently no specific vegetation index for the NNI inversion across multiple growth periods.To overcome the limitations of the traditional direct NNI inversion method(NNI_(T1))of the vegetation index and traditional indirect NNI inversion method(NNI_(T2))by inverting intermediate variables including the aboveground dry biomass(AGB)and plant N concentration(PNC),this study proposed a new NNI remote sensing index(NNI_(RS)).A remote-sensing-based critical N dilution curve(Nc_(_RS))was set up directly from two vegetation indices and then used to calculate NNI_(RS).Field data including AGB,PNC,and canopy hyperspectral data were collected over four growing seasons(2012–2013(Exp.1),2013–2014(Exp.2),2014–2015(Exp.3),2015–2016(Exp.4))in Beijing,China.All experimental datasets were cross-validated to each of the NNI models(NNI_(T1),NNI_(T2)and NNI_(RS)).The results showed that:(1)the NNI_(RS)models were represented by the standardized leaf area index determining index(sLAIDI)and the red-edge chlorophyll index(CI_(red edge))in the form of NNI_(RS)=CI_(red edge)/(a×sLAIDI~b),where"a"equals 2.06,2.10,2.08 and 2.02 and"b"equals 0.66,0.73,0.67 and 0.62 when the modeling set data came from Exp.1/2/4,Exp.1/2/3,Exp.1/3/4,and Exp.2/3/4,respectively;(2)the NNI_(RS)models achieved better performance than the other two NNI revised methods,and the ranges of R2 and RMSE were 0.50–0.82 and 0.12–0.14,respectively;(3)when the remaining data were used for verification,the NNI_(RS)models also showed good stability,with RMSE values of 0.09,0.18,0.13 and 0.10,respectively.Therefore,it is concluded that the NNI_(RS)method is promising for the remote assessment of crop N status.

Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC （grain protein content） estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.

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.

Aging effect on the mobility and bioavailability of copper in soil

Aging effect on the mobility and bioavallability of copper （Cu） was investigated using a spiked soil with different incubation periods from 3 to 56 d. Wheat was planted and earthworms were cultured separately in the incubated soils. The mobility of Cu in soil was evaluated by a chemical fractionation scheme and the toxicity and bioavailability were assessed by measuring the biomass and Cu concentration in tissues. Results showed that aging had a significant effect on Cu fraction distribution, of which Cu tended to incorporate from the exchangeable into more stable fractions such as the reducible and oxidisable fractions. However, aging had little effect on Cu bioavailability to wheat and earthworm. Comparing the soil being incubated for 3 d and 56 d, Cu concentration in wheat roots decreased from 14.5 to 12.8 mg/kg, and no significant changes in Cu concentration were observed in both wheat shoots and earthworms. The Cu concentration was around 2.0 and 50 mg/kg for wheat shoots and earthworms, respectively, irrespective of soil incubation time. The CaC12-extractable Cu had a linear relationship with Cu concentration in wheat roots （R2 = 0.65, P 〈 0.05）, but no linear relationship can be found for wheat shoots and earthworms. Biological control may be more crucial for Cu accumulation in organism than the changes in soil Cu fraction caused by aging.

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.

Planning Evaluation on Spatial Layout of Livestock and Poultry Farms

The problems of spatial layout in livestock and poultry farms were discussed, and the development status of the planning evaluation on the spatial layout in recent years was systematically reviewed. The research progress in planning evaluation systems and methods was mainly intro- duced. And some opinions were proposed to solve these problems.

Spectral Target-Detecting System Using Sine-Wave Modulation

Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection systems previously in China.It was difficult to adjust the output power,and the anti-interference ability was weak in these systems.In order to resolve these problems,the target detection method based on analog sine-wave modulation was studied.The spectral detecting system was set up in the aspects of working principle,electric circuit,and optical path.Lab testing was performed.The results showed that the reflected signal from the target varied inversely with detection distances.It indicated that it was feasible to establish the target detection system using analog sine-wave modulation technology.Furthermore,quantitative measurement of the reflected optical signal for near-infrared and visible light could be achieved by using this system.The research laid the foundation for the future development of the corresponding instrument.

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.

Identification of Wheat Canopy Structure Using Hyperspectral Data

Some winter wheat varieties were selected in this experiment. The results were as follows： 1） Leaf orientation value （LOV） and leaf area index （LAI） of wheat had different contributions to canopy spectral reflectance （CSR）. For example, LOV affected greatly canopy spectra more than LAI did in jointing stage, but LAI had a greater effect on CSR than LOV did after the ground was near to be covered completely. 2） Twenty treatments including different varieties and densities were arranged in this experiment, and the result of cluster analysis showed that all these treatments can be parted into four clusters according to LAI and LOV： varieties with erect leaves and low LAI （denoted as A）, varieties with erect leaves and high LAI （denoted as B）, varieties with horizontal leaves and low LAI （denoted as C）, varieties with horizontal leaves and high LAI （denoted as D）. Their CSR had difference in 400-700 nm and 700-1 150 nm at jointing stage, especially in different plant types. 3） There was obvious distribution difference among different clusters in scatter plot （X=△R890, Y=R890）, △R890 was the reflectance increment from jointing to booting stage. It was seen from the Y-axis direction that R890 of horizontal varieties were higher than the erect ones, and seen from the X-axis direction that the greater △R890 was, the lower LAI one within the same plant type varieties, which indicted that the combination of plant-type and the population magnitude can be initially identified by this method.