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| 1 | Computer vision-based apple grading for golden delicious apples based on surface features显示文摘In this paper,a computer vision-based algorithm for golden delicious apple grading is proposed which works in six steps.Non-apple pixels as background are firstly removed from input images.Then,stem end is detected by combination of morphological methods and Mahalanobis distant classifier.Calyx region is also detected by applying K-means clustering on the Cb component in YCbCr color space.After that,defects segmentation is achieved using Multi-Layer Perceptron(MLP)neural network.In the next step,stem end and calyx regions are removed from defected regions to refine and improve apple grading process.Then,statistical,textural and geometric features from refined defected regions are extracted.Finally,for apple grading,a comparison between performance of Support Vector Machine(SVM),MLP and K-Nearest Neighbor(KNN)classifiers is done.Classification is done in two manners which in the first one,an input apple is classified into two categories of healthy and defected.In the second manner,the input apple is classified into three categories of first rank,second rank and rejected ones.In both grading steps,SVM classifier works as the best one with recognition rate of 92.5%and 89.2%for two categories(healthy and defected)and three quality categories(first rank,second rank and rejected ones),among 120 different golden delicious apple images,respectively,considering K-folding with K=5.Moreover,the accuracy of the proposed segmentation algorithms including stem end detection and calyx detection are evaluated for two different apple image databases. | Payman Moallem Alireza Serajoddin Hossein Pourghassem | 2017 | Information Processing in Agriculture2017，4，1： | 28 |
| 2 | Detection of plant leaf diseases using image segmentation and soft computing techniques显示文摘Agricultural productivity is something on which economy highly depends.This is the one of the reasons that disease detection in plants plays an important role in agriculture field,as having disease in plants are quite natural.If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality,quantity or productivity is affected.For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States.Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops,and at very early stage itself it detects the symptoms of diseases i.e.when they appear on plant leaves.This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases.It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection.Image segmentation,which is an important aspect for disease detection in plant leaf disease,is done by using genetic algorithm. | Vijai Singh A.K.Misra | 2017 | Information Processing in Agriculture2017，4，1： | 28 |
| 3 | Defining the effect of sweep tillage tool cutting edge geometry on tillage forces using 3D discrete element modelling显示文摘The energy required for tillage processes accounts for a significant proportion of total energy used in crop production.In many tillage processes decreasing the draft and upward vertical forces is often desired for reduced fuel use and improved penetration,respectively.Recent studies have proved that the discrete element modelling(DEM)can effectively be used to model the soil–tool interaction.In his study,Fielke(1994)[1]examined the effect of the various tool cutting edge geometries,namely;cutting edge height,length of underside rub,angle of underside clearance,on draft and vertical forces.In this paper the experimental parameters of Fielke(1994)[1]were simulated using 3D discrete element modelling techniques.In the simulations a hysteretic spring contact model integrated with a linear cohesion model that considers the plastic deformation behaviour of the soil hence provides better vertical force prediction was employed.DEM parameters were determined by comparing the experimental and simulation results of angle of repose and penetration tests.The results of the study showed that the simulation results of the soil-various tool cutting edge geometries agreed well with the experimental results of Fielke(1994)[1].The modelling was then used to simulate a further range of cutting edge geometries to better define the effect of sweep tool cutting edge geometry parameters on tillage forces.The extra simulations were able to show that by using a sharper cutting edge with zero vertical cutting edge height the draft and upward vertical force were further reduced indicating there is benefit from having a really sharp cutting edge.The extra simulations also confirmed that the interpolated trends for angle of underside clearance as suggested by Fielke(1994)[1]where correct with a linear reduction in draft and upward vertical force for angle of underside clearance between the ranges of-25 and-5°,and between-5 and 0°.The good correlations give confidence to recommend further investigation of the use of DEM to model the different types of tillage tools. | Mustafa Ucgul John M.Fielke Chris Saunders | 2015 | Information Processing in Agriculture2015，2，2： | 24 |
| 4 | Recent developments and trends in thermal blanching-A comprehensive review显示文摘Thermal blanching is an essential operation for many fruits and vegetables processing.It not only contributes to the inactivation of polyphenol oxidase(PPO),peroxidase(POD),but also affects other quality attributes of products.Herein we review the current status of thermal blanching.Firstly,the purposes of blanching,which include inactivating enzymes,enhancing drying rate and product quality,removing pesticide residues and toxic constituents,expelling air in plant tissues,decreasing microbial load,are examined.Then,the reason to why indicators such as POD and PPO,ascorbic acid,color,and texture are frequently used to evaluate blanching process is summarized.After that,the principles,applications and limitations of current thermal blanching methods,which include conventional hot water blanching,steam blanching,microwave blanching,ohmic blanching,and infrared blanching are outlined.Finally,future trends are identified and discussed. | Hong-Wei Xiao Zhongli Pan Li-Zhen Deng Hamed M.El-Mashad Xu-Hai Yang Arun S.Mujumdar Zhen-Jiang Gao Qian Zhang | 2017 | Information Processing in Agriculture2017，4，2： | 23 |
| 5 | Recent advances in image processing techniques for automated leaf pest and disease recognition – A review显示文摘Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way.Traditionally,human experts have been relied upon to diagnose anomalies in plants caused by diseases,pests,nutritional deficiencies or extreme weather.However,this is expensive,time consuming and in some cases impractical.To counter these challenges,research into the use of image processing techniques for plant disease recognition has become a hot research topic.In this paper,we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques.We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques.In particular,we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras.We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using handcrafted features.Researchers have reported high recognition accuracies on particular datasets but in many cases,the performance of those systems deteriorated significantly when tested on different datasets or in field conditions.Nevertheless,progress made so far has been encouraging.Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy,recall,precision,specificity,F1-score,training duration and storage requirements are also presented.Subsequently,recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments.We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions. | Lawrence C.Ngugi Moataz Abelwahab Mohammed Abo-Zahhad | 2021 | Information Processing in Agriculture2021，8，1： | 13 |
| 6 | Design, development and field assessment of a controlled seed metering unit to be used in grain drills for direct seeding of wheat显示文摘A newcontrolled seed metering unit was designed and mounted on a common grain drill for direct seeding of wheat(DSW).It comprised the following main parts:(a)a variable-rate controlled direct current motor(DCM)as seed metering shaft driver,(b)two digital encoders for sensing the rotational speed of supplemental ground wheel(SGW)and seed metering shaft and(c)a control box to handle and process the data of the unit.According to the considered closed-loop control system,the designed control box regularly checked the revolution per minute(RPM)of seed metering shaft,as operation feedback,using its digital encoder output.The seeding ratewas determined based on the calculated error signal and output signal of the digital encoder of the SGW.A field with four different levels of wheat stubble coverage(10%,30%,40%and 50%)was selected for evaluation of the fabricated seed metering unit(FSMU).The dynamic tests were conducted to compare the performance of installed FSMU on the grain drill and equipped grain drill with common seed metering unit(CSMU)at three forward speeds of 4,6 and 8(Km/h)for DSW.Results of the FSMU assessment demonstrated that an increase in forwardspeed of grain drill(FSGD)and stubble coverage did not significantly affect the seeding rate in the grain drill forDSW.Using theFSMU reduced the coefficient of variation(CV)by approximately 50%.Consequently,applying the FSMU on the common grain drill led to a desirable seeding rate at different forward speeds of the grain drill and stubble existence. | S.Kamgar F.Noei-Khodabadi S.M.Shafaei | 2015 | Information Processing in Agriculture2015，2，3： | 12 |
| 7 | Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs显示文摘Energy is regarded as one of the most important elements in agricultural sector.During the last decades energy consumption in agriculture has increased,so finding the relationship between energy consumption and crop yields in agricultural production can help to achieve sustainable agriculture.In this study several adaptive neuro-fuzzy inference system(ANFIS)models were evaluated to predict wheat grain yield on the basis of energy inputs.Moreover,artificial neural networks(ANNs)were developed and the obtained results were compared with ANFIS models.For the best ANFIS structure gained in this study,R,RMSE and MAPE were calculated as 0.976,0.046 and 0.4,respectively.The developed ANN was a multilayer perceptron(MLP)with eleven neurons in the input layer,two hidden layers with 32 and 10 neurons and one neuron(wheat grain yield)in the output layer.For the best ANN model,R,RMSE and MAPE were computed as 0.92,0.9 and 0.1,respectively.The results illustrated that ANFIS model can predict the yield more precisely than ANN. | Benyamin Khoshnevisan Shahin Rafiee Mahmoud Omid Hossein Mousazadeh | 2014 | Information Processing in Agriculture2014，1，1： | 12 |
| 8 | Analyzing drying characteristics and modeling of thin layers of peppermint leaves under hot-air and infrared treatments显示文摘The drying kinetics of peppermint leaves was studied to determine the best drying method for them.Two drying methods include hot-air and infrared techniques,were employed.Three different temperatures(30,40,50℃)and air velocities(0.5,1,1.5 m/s)were selected for the hot-air drying process.Three levels of infrared intensity(1500,3000,4500 W/m^2),emitter-sample distance(10,15,20 cm)and air speed(0.5,1,1.5 m/s)were used for the infrared drying technique.According to the results,drying had a falling rate over time.Drying kinetics of peppermint leaves was explained and compared using three mathematical models.To determine coefficients of these models,non-linear regression analysis was used.The models were evaluated in terms of reduced chi-square(χ^2),root mean square error(RMSE)and coefficient of determination(R^2)values of experimental and predicted moisture ratios.Statistical analyses indicated that the model with the best fitness in explaining the drying behavior of peppermint samples was the Logarithmic model for hot-air drying and Midilli model for infrared drying.Moisture transfer in peppermint leaves was also described using Fick’s diffusion model.The lowest effective moisture diffusivity(1.096×10^-11m^2/s)occurred during hot-air drying at 30℃ using 0.5 m/s,whereas its highest value(5.928×10^-11m^2/s)belonged to infrared drying using 4500 W/m^2 infrared intensity,0.5 m/s airflow velocity and 10 cm emitter-sample distance.The activation energy for infrared and hot-air drying were ranged from 0.206 to 0.439 W/g,and from 21.476 to 27.784 kJ/mol,respectively. | Seyed-Hassan Miraei Ashtiani Alireza Salarikia Mahmood Reza Golzarian | 2017 | Information Processing in Agriculture2017，4，2： | 12 |
| 9 | Agricultural information dissemination using ICTs:A review and analysis of information dissemination models in China显示文摘Over the last three decades,China’s agriculture sector has been transformed from the traditional to modern practice through the effective deployment of Information and Communication Technologies(ICTs).Information processing and dissemination have played a critical role in this transformation process.Many studies in relation to agriculture information services have been conducted in China,but few of them have attempted to provide a comprehensive review and analysis of different information dissemination models and their applications.This paper aims to review and identify the ICT based information dissemination models in China and to share the knowledge and experience in applying emerging ICTs in disseminating agriculture information to farmers and farm communities to improve productivity and economic,social and environmental sustainability.The paper reviews and analyzes the development stages of China’s agricultural information dissemination systems and different mechanisms for agricultural information service development and operations.Seven ICT-based information dissemination models are identified and discussed.Success cases are presented.The findings provide a useful direction for researchers and practitioners in developing future ICT based information dissemination systems.It is hoped that this paper will also help other developing countries to learn from China’s experience and best practice in their endeavor of applying emerging ICTs in agriculture information dissemination and knowledge transfer. | Yun Zhang Lei Wang Yanqing Duan | 2016 | Information Processing in Agriculture2016，3，1： | 12 |
| 10 | Modeling and experimental validation of heat transfer and energy consumption in an innovative greenhouse structure显示文摘The commercial greenhouse is one of the most effective cultivation methods with a yield per cultivated area up to 10 times more than free land cultivation but the use of fossil fuels in this production field is very high.The objectives of this paper are to modeling and experimental evaluation of heat and mass transfer functions in an innovative solar greenhouse with thermal screen.For this propose,a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province(38100N and 46180E with elevation of 1364 m above the sea level).The inside environment factors include inside air temperature below screen(Ta),inside air temperature above screen(Tas),crop temperature(Tc),inside soil temperature(Ts),cover temperature(Tri)and thermal screen temperature(Tsc)were collected as the experimental data samples.The dynamic heat and mass transfer model used to estimate the temperature in six different points of the semi-solar greenhouse with initial values and consider the crop evapotranspiration.The results showed that dynamic model can predict the inside temperatures in four different points(Ta,Tc,Tri,Ts)with MAPE,RMSE and EF about 5-7%,1-2℃ and 80-91%for greenhouse without thermal screen and about 3-7%,0.6-1.8C and 89-96%for six different points of greenhouse with thermal screen(Ta,Tc,Tri,Ts,Tas,Tsc),respectively.The results of using thermal screen at night(12 h)in autumn showed that this method can decrease the use of fossil fuels up to 58%and so decrease the final cost and air pollution.This movable insulation caused about 15℃ difference between outside and inside air temperature and also made about 6℃ difference between Ta and Tas.The experimental results showed that inside thermal screen can decrease the crop temperature fluctuation at night. | Morteza Taki Yahya Ajabshirchi Seyed Faramarz Ranjbar Abbas Rohani Mansour Matloobi | 2016 | Information Processing in Agriculture2016，3，3： | 10 |
| 11 | A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features显示文摘To increase prediction accuracy of dissolved oxygen(DO)in aquaculture,a hybrid model based on multi-scale features using ensemble empirical mode decomposition(EEMD)is proposed.Firstly,original DO datasets are decomposed by EEMD and we get several components.Secondly,these components are used to reconstruct four terms including high frequency term,intermediate frequency term,low frequency term and trend term.Thirdly,according to the characteristics of high and intermediate frequency terms,which fluctuate violently,the least squares support vector machine(LSSVR)is used to predict the two terms.The fluctuation of low frequency term is gentle and periodic,so it can be modeled by BP neural network with an optimal mind evolutionary computation(MEC-BP).Then,the trend term is predicted using grey model(GM)because it is nearly linear.Finally,the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms.The experimental results demonstrate that our hybrid model outperforms EEMD-ELM(extreme learning machine based on EEMD),EEMD-BP and MEC-BP models based on the mean absolute error(MAE),mean absolute percentage error(MAPE),mean square error(MSE)and root mean square error(RMSE).Our hybrid model is proven to be an effective approach to predict aquaculture DO. | Chen Li Zhenbo Li Jing Wu Ling Zhu Jun Yue | 2018 | Information Processing in Agriculture2018，5，1： | 10 |
| 12 | Underwater image quality enhancement of sea cucumbers based on improved histogram equalization and wavelet transform显示文摘Sea cucumbers usually live in an environment where lighting and visibility are generally not controllable,which cause the underwater image of sea cucumbers to be distorted,blurred,and severely attenuated.Therefore,the valuable information from such an image cannot be fully extracted for further processing.To solve the problems mentioned above and improve the quality of the underwater images of sea cucumbers,pre-processing of a sea cucumber image is attracting increasing interest.This paper presents a newmethod based on contrast limited adaptive histogram equalization and wavelet transform(CLAHE-WT)to enhance the sea cucumber image quality.CLAHE was used to process the underwater image for increasing contrast based on the Rayleigh distribution,and WTwas used for de-noising based on a soft threshold.Qualitative analysis indicated that the proposed method exhibited better performance in enhancing the quality and retaining the image details.For quantitative analysis,the test with 120 underwater images showed that for the proposed method,the mean square error(MSE),peak signal to noise ratio(PSNR),and entropy were 49.2098,13.3909,and 6.6815,respectively.The proposed method outperformed three established methods in enhancing the visual quality of sea cucumber underwater gray image. | Xi Qiao Jianhua Bao Hang Zhang Lihua Zeng Daoliang Li | 2017 | Information Processing in Agriculture2017，4，3： | 9 |
| 13 | A review of neural networks in plant disease detection using hyperspectral data显示文摘This paper reviews advanced Neural Network(NN)techniques available to process hyperspectral data,with a special emphasis on plant disease detection.Firstly,we provide a review on NN mechanism,types,models,and classifiers that use different algorithms to process hyperspectral data.Then we highlight the current state of imaging and nonimaging hyperspectral data for early disease detection.The hybridization of NNhyperspectral approach has emerged as a powerful tool for disease detection and diagnosis.Spectral Disease Index(SDI)is the ratio of different spectral bands of pure disease spectra.Subsequently,we introduce NN techniques for rapid development of SDI.We also highlight current challenges and future trends of hyperspectral data. | Kamlesh Golhani Siva K.Balasundram Ganesan Vadamalai Biswajeet Pradhan | 2018 | Information Processing in Agriculture2018，5，3： | 9 |
| 14 | Quality control of the agricultural products supply chain based on'Internet+'显示文摘This paper describes a quality-control supply-chain model using the'Internet+'paradigm.The model is based on principal-agent theory,which considers the reputational loss due to inferior products and external responsibility identification.After model analysis and simulation verification,the results show that the optimal quality-control level and market price of agricultural products can be achieved in the agricultural supply chain based on'Internet+'if and only if the information platform’s claim to the agricultural producer is less than the agricultural producer’s claim to the delivery service provider.Also,a rise in consumers’claims or the agricultural producer’s reputational loss due to inferior products will motivate the quality control of an agricultural procedure.Meanwhile,the market price of agricultural products will also increase with enhanced quality control procedures.The quality-control level of a delivery service provider is inversely proportional to the information platform or its own reputational loss.Thus,the key to promoting quality control along the supply chain is to strengthen the responsibility confirmation of an inferior product between the agricultural producer and the delivery service provider. | Qiang Shen Jian Zhang Yun-xian Hou Jia-hui Yu Jin-you Hu | 2018 | Information Processing in Agriculture2018，5，3： | 8 |
| 15 | An intelligent system for egg quality classification based on visible-infrared transmittance spectroscopy显示文摘The potential of the visible infrared(Vis–IR)(400–1100 nm)transmittance method to assess the internal quality(freshness)of intact chicken egg during storage at a temperature of 30±7C and 25±4%relative humidity was investigated.Two hundred chicken egg samples were used for measuring freshness and spectra collection during egg storage(up to 25 days).Two correlation models,firstly between Haugh unit(HU)and storage time,and secondly between the yolk coefficient(YC)and storage time,were developed and yielded correlation coefficients(R^2)of 0.86 and 0.96,respectively.These models spanned the period for which egg quality decreased dramatically and are statistically significant(P<0.05).In addition,to reduce the dimensionality of the spectra and extract effective wavelengths,two methods were developed based on principal component analysis(PCA)and a genetic algorithm(GA).The output of PCA and GA were also used comparatively to design an egg quality intelligent system.The result of the analyses indicated that identification ratio of GAwith fast Fourier transform(FFT)preprocessing was superior to other methods,and that the quality classification rates of this method for one-day-old eggs are 100%.This study shows that identification of an egg’s freshness using NIR spectroscopy with GA and artificial neural network(ANN)is reliable. | Saman Abdanan Mehdizadeh Saeid Minaei Nigel H.Hancock Mohamad Amir Karimi Torshizi | 2014 | Information Processing in Agriculture2014，1，2： | 8 |
| 16 | Applied machine learning in greenhouse simulation;new application and analysis显示文摘Prediction the inside environment variables in greenhouses is very important because they play a vital role in greenhouse cultivation and energy lost especially in cold and hot regions.The greenhouse environment is an uncertain nonlinear system which classical modeling methods have some problems to solve it.So the main goal of this study is to select the best method between Artificial Neural Network(ANN)and Support Vector Machine(SVM)to estimate three different variables include inside air,soil and plant temperatures(Ta,Ts,Tp)and also energy exchange in a polyethylene greenhouse in Shahreza city,Isfahan province,Iran.The environmental factors which influencing all the inside temperatures such as outside air temperature,wind speed and outside solar radiation were collected as data samples.In this research,13 different training algorithms were used for ANN models(MLPRBF).Based on K-fold cross validation and Randomized Complete Block(RCB)methodology,the best model was selected.The results showed that the type of training algorithm and kernel function are very important factors in ANN(RBF and MLP)and SVM models performance,respectively.Comparing RBF,MLP and SVM models showed that the performance of RBF to predict Ta,Tp and Ts variables is better according to small values of RMSE and MAPE and large value of R2 indices.The range of RMSE and MAPE factors for RBF model to predict Ta,Tp and Ts were between 0.07 and 0.12C and 0.28-0.50%,respectively.Generalizability and stability of the RBF model with 5-fold cross validation analysis showed that this method can use with small size of data groups.The performance of best model(RBF)to estimate the energy lost and exchange in the greenhouse with heat transfer models showed that this method can estimate the real data in greenhouse and then predict the energy lost and exchange with high accuracy. | Morteza Taki Saman Abdanan Mehdizadeh Abbas Rohani Majid Rahnama Mostafa Rahmati-Joneidabad | 2018 | Information Processing in Agriculture2018，5，2： | 8 |
| 17 | A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms显示文摘Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly available.After segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image processing.Among them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of kNN.The average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of kNN.Thus,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing factories.The main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms. | Sajad Sabzi Yousef Abbaspour-Gilandeh Ginés García-Mateos | 2018 | Information Processing in Agriculture2018，5，1： | 8 |
| 18 | An improved random forest classifier for multi-class classification显示文摘The paper presents an improved-RFC(Random Forest Classifier)approach for multi-class disease classification problem.It consists of a combination of Random Forest machine learning algorithm,an attribute evaluator method and an instance filter method.It intends to improve the performance of Random Forest algorithm.The performance results confirm that the proposed improved-RFC approach performs better than Random Forest algorithm with increase in disease classification accuracy up to 97.80%for multi-class groundnut disease dataset.The performance of improved-RFC approach is tested for its efficiency on five benchmark datasets.It shows superior performance on all these datasets. | Archana Chaudhary Savita Kolhe Raj Kamal | 2016 | Information Processing in Agriculture2016，3，4： | 8 |
| 19 | Computer vision technology in agricultural automation--A review显示文摘Computer vision is a field that involves making a machine “see”.This technology uses a camera and computer instead of the human eye to identify,track and measure targets for further image processing.With the development of computer vision,such technology has been widely used in the field of agricultural automation and plays a key role in its development.This review systematically summarizes and analyzes the technologies and challenges over the past three years and explores future opportunities and prospects to form the latest reference for researchers.Through the analyses,it is found that the existing technology can help the development of agricultural automation for small field farming to achieve the advantages of low cost,high efficiency and high precision.However,there are still major challenges.First,the technology will continue to expand into new application areas in the future,and there will be more technological issues that need to be overcome.It is essential to build large-scale data sets.Second,with the rapid development of agricultural automation,the demand for professionals will continue to grow.Finally,the robust performance of related technologies in various complex environments will also face challenges.Through analysis and discussion,we believe that in the future,computer vision technology will be combined with intelligent technology such as deep learning technology,be applied to every aspect of agricultural production management based on large-scale datasets,be more widely used to solve the current agricultural problems,and better improve the economic,general and robust performance of agricultural automation systems,thus promoting the development of agricultural automation equipment and systems in a more intelligent direction. | Hongkun Tian Tianhai Wang Yadong Liu Xi Qiao Yanzhou Li | 2020 | Information Processing in Agriculture2020，7，1： | 8 |
| 20 | Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina显示文摘In countries like Argentina,whose economy depends heavily on crop production,the estimation of harvests is an elementary requirement.Besides providing objectivity,the use of remote sensing allows estimating yield in advance.Since the time of maximum leaf area in wheat corresponds with the critical period of the crop,a good relationship is expected between the Normalized Difference Vegetation Index(NDVI)and yield.The present study was carried out in the North of Buenos Aires province,Argentina.Based on the type of soil,the study area can be divided into two homogeneous subzones:a subzone with lower clay content in the southwestand a subzone with higher clay content in the northeast.Nine growing seasons(2003–2011)were studied.In the first five years,an empirical model was calibrated and validated with field-observed wheat yields and MOD13q1 product-NDVI data,whereas in the other four years,the calibrated model was applied by means of yield maps and by comparing with official yields.The MOD13q1 image corresponding to Julian day 289 showed the best fit between NDVI and yield to estimate wheat yield early.Through yield maps,better weather conditions showedhigher yields and higher soil productivity presented a greater proportion of the area occupied by higher yields.At department level,an R2 value of 0.75 was found after relating the estimation of the calibrated empirical model with official yields.The method used allows predicting wheat yield 30 days before harvest.Through yield maps,the NDVI perceived the temporal and spatial variability in the study area. | Mariano F.Lopresti Carlos M.Di Bella Américo J.Degioanni | 2015 | Information Processing in Agriculture2015，2，2： | 8 |
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