Papaya(Carica papaya)is a tropical fruit having commercial importance because of its high nutritive and medicinal value.The packaging of papaya fruit as per its maturity status is an essential task in the fruit indust...Papaya(Carica papaya)is a tropical fruit having commercial importance because of its high nutritive and medicinal value.The packaging of papaya fruit as per its maturity status is an essential task in the fruit industry.The manual grading of papaya fruit based on human visual perception is time-consuming and destructive.The objective of this paper is to suggest a novel non-destructive maturity status classification of papaya fruits.The paper suggested two approaches based on machine learning and transfer learning for classification of papaya maturity status.Also,a comparative analysis is carried out with different methods of machine learning and transfer learning.The experimentation is carried out with 300 papaya fruit sample images which includes 100 of each three maturity stages.The machine learning approach includes three sets of features and three classifiers with their different kernel functions.The features and classifiers used in machine learning approaches are local binary pattern(LBP),histogram of oriented gradients(HOG),Gray Level Cooccurrence Matrix(GLCM)and k-nearest neighbour(KNN),support vector machine(SVM),Naı¨ve Bayes respectively.The transfer learning approach includes seven pretrained models such as ResNet101,ResNet50,ResNet18,VGG19,VGG16,GoogleNet and AlexNet.The weighted KNN with HOG feature outperforms other machine learningbased classification model with 100%of accuracy and 0.0995 s training time.Again,among the transfer learning approach based classification model VGG19 performs better with 100%accuracy and 1 min 52 s training time with consideration of early stop training.The proposed classification method for maturity classification of papaya fruits,i.e.VGG19 based on transfer learning approach achieved 100%accuracy which is 6%more than the existing method.展开更多
Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighin...Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring area.Usually,it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its wings.To solve these issues,we developed one simple and low-cost weighing system with high stability and accuracy.A validity value extraction method from dynamic weighing was proposed.Then,an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental interference.The BP neural networks model used daily weight gain,day-age,average velocity,and the weight data after filtering algorithm as the input layer.The weighing system was tested in a commercial Beijing Fatty Chickens house with Beijing Fatty Chickens.We tested thirteen groups of Beijing Fatty Chickens of different weights,from 500 g to 1800 g in intervals of 100 g,using the three different methods:no filtering algorithm or BP neural networks,only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural networks.The results showed that the hybrid algorithm had a better performance in minimizing the error,lowering from the original 6%down to 3%.The accurate weight data was transmitted to the remote service platform for further decision-making,such as activity analysis,feeding management,and health alerts.展开更多
Heat and light stress causes sunburn to the maturing apple fruits and results in crop production and quality losses.Typically,when the fruit surface temperature(FST)rises above critical limits for a prolonged duration...Heat and light stress causes sunburn to the maturing apple fruits and results in crop production and quality losses.Typically,when the fruit surface temperature(FST)rises above critical limits for a prolonged duration,the fruit may suffer several physiological disorders including sunburn.To manage apple sunburn,monitoring FST is critical and our group at Washington State University is developing a noncontact smart sensing system that integrates thermal infrared and visible imaging sensors for real time FST monitoring.Pertinent system needs to perform in-field imagery data analysis onboard a single board computer with processing unit that has limited computational resources.Therefore,key objective of this study was to develop a novel image processing algorithm optimized to use available resources of a single board computer.Algorithm logic flow includes color space transformation,k-means++classification and morphological operators prior to fruit segmentation and FST estimation.The developed algorithm demonstrated the segmentation accuracy of 57.78%(missing error=12.09%and segmentation error=0.13%).This aided successful apple FST estimation that was 10–18C warmer than ambient air temperature.Moreover,algorithm reduced the imagery data processing time cost of the smart sensing systemfrom 87 s to 44 s using image compression approach.展开更多
This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate.The experimental data used for the developed models included 15 process variables me...This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate.The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany.In this context,the concentration of volatile fatty acids,total solids,volatile solids acid detergent fibre,acid detergent lignin,neutral detergent fibre,ammonium nitrogen,hydraulic retention time,and organic loading rate were used.Artificial neural networks(ANN)were established to predict the biogas production rate.An ant colony optimization and genetic algorithms were implemented to perform the variable selection.They identified the significant process variables,reduced the model dimension and improved the prediction capacity of the ANN models.The best prediction of the biogas production rate was obtained with an error of prediction of 6.24%and a coefficient of determination of R2=0.9.展开更多
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...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.展开更多
In response to the challenges in providing real-time extraction of crop biophysical signatures,computer vision in computational crop phenotyping highlights the opportunities of computational intelligence solutions.Sha...In response to the challenges in providing real-time extraction of crop biophysical signatures,computer vision in computational crop phenotyping highlights the opportunities of computational intelligence solutions.Shadow and angular brightness due to the presence of photosynthetic light unevenly illuminate crop canopy.In this study,a novel vegetation index named artificial bee colony-optimized visible band oblique dipyramid greenness index(vODGIabc)was proposed to enhance vegetation pixels by correcting the saturation and brightness levels,and the ratio of visible RGB reflectance intensities.Consumer-grade smartphone was used to acquire indoor and outdoor aquaponic lettuce images daily for full 6-week crop life cycle.The introduced saturation rectification coeffi-cient(X),value rectification coefficient(m),green–red wavelength adjustment factor(a),and green–blue wavelength adjustment factor(b)on the original triangular greenness index resulted in 3D canopy reflectance spectrum with two oblique tetrahedrons formed by connecting the vertices of visible RGB band reflectance and maximum wavelength point map to corresponding saturation and value of lettuce-captured images.Hybrid neighborhood component analysis(NCA),minimum redundancy maximum relevance(MRMR),Pearson’s correlation coefficient(PCC),and analysis of variance(ANOVA)weighted most of the canopy area,energy,and homogeneity.Strong linear relationships were exhibited by using vODGIabc in estimating lettuce crop fresh weight,height,number of spanning leaves,leaf area index,and growth stage with R2 values of 0.9368 for InceptionV3,0.9574 for ResNet101,0.9612 for ResNet101,0.9999 for Gaussian processing regression,and accuracy of 88.89%for ResNet101,respectively.This low-cost approach on developing greenness index for biophysical signatures estimation proved to be more accurate than the previously established triangular greenness index(TGI)using RGB smartphone camera.展开更多
Lodging occurs when the crop canopy is too heavy for the strength of the stem and it fallsover onto the ground. This decreases crop yield and quality, and it makes harvest difficult.A research experiment was set up in...Lodging occurs when the crop canopy is too heavy for the strength of the stem and it fallsover onto the ground. This decreases crop yield and quality, and it makes harvest difficult.A research experiment was set up in a spearmint field on a center pivot with mid elevationspray application (MESA) overhead sprinklers, where the water was applied from a “midelevation” of 2 m above the ground level (AGL), and low elevation precision application(LEPA) sprinklers, where the water was emitted directly onto the soil surface through draghoses without wetting the crop canopy. Every-other span of this full-size center pivot wasconfigured with MESA and LEPA sprinklers alternatively. In 2018, imagery was collectedwith an unmanned aerial vehicle (UAV) from a cross section of this field. In 2019, a crosssection was again collected, but in addition UAV imagery was collected from marked lodgedand un-lodged areas of the field to validate the lodging detection method. These UAV-basedimagery data were captured with a ground sample distance (GSD) of 0.03 m. This researchintroduces using the texture feature, which is based on image entropy, was used to evaluate the degree of lodging. The results from 2018 showed that the average entropy of thegrayscale image from LEPA (5.5 (mean) ± 0.27 (standard deviation)) was significantly(P < 0.0001) greater than the average entropy (5.0 ± 0.25) of MESA. Also, the entropy valueextracted from the images in 2019 from the marked un-lodged locations were significantlyhigher compared to that of the lodged areas. Overall, the LEPA irrigation treatment was significantly less lodged compared to MESA. Moreover, the entropy value, or texture feature, isa viable method for estimating lodging using low altitude RGB imagery.展开更多
The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product costs.To solve this problem,shallow geothermal energy as a local source o...The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product costs.To solve this problem,shallow geothermal energy as a local source of energy could be applied.In this study,a measurement model,the distribution profiles of temperature,and a preliminary assessment of the geothermal potential in the shallow zone at depths of 0.1 m to 3.6 m in Shouguang City,Shandong Province,eastern China were presented.The measurement results showed that the annual average temperature at depths of 0.1–3.6 m ranged from 13.1℃ to 17.6℃.Preliminary assessment results of the geothermal potential showed that the daily average temperature difference between the air and at depths of 1.5–3.6 m was mainly from 10℃ to 25℃ during the winter months and between-15℃ and-5℃ during the summer months.Therefore,the heating systems could operate during January,February,November,and December.In May,June,and July,the cooling systems could be applied.Moreover,the measurement model gave good stability results,and it could be used in combination with the monitoring of the groundwater table,a survey of the thermal conductivity of the soil,climate change studies,which helps reduce unnecessary time and costs.展开更多
Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental ...Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental and biological factors(e.g.soil compaction)the weight and size of the machinery cannot be further physically optimized.Thus,only marginal improvements are possible to increase equipment effectiveness.On the contrary,late technological advances in ICT provide the ground for significant improvements in agriproduction efficiency.In this work,the V-Agrifleet tool is presented and demonstrated.VAgrifleet is developed to provide a “hands-free”interface for information exchange and an “Olympic view”to all coordinated users,giving them the ability for decentralized decision-making.The proposed tool can be used by the end-users(e.g.farmers,contractors,farm associations,agri-products storage and processing facilities,etc.)order to optimize task and time management.The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations.Its vendorindependent architecture,voice-driven interaction,context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system.展开更多
As a complement to traditional estimates of stem dimensions from numerical models,terrestrial light detection and ranging(Lidar)provides direct stem diameter and volume values using cylindrical models constructed from...As a complement to traditional estimates of stem dimensions from numerical models,terrestrial light detection and ranging(Lidar)provides direct stem diameter and volume values using cylindrical models constructed from point clouds.This study used two approaches to estimate total stem volume using Lidar and compared them with two empirical equations,one used by the Forest Inventory Analysis in the Pacific Northwest(FIA-PNW)and one based on a taper equation.We fitted point clouds of 10 Douglas-fir with three sets of cylinder models that are distinguished by their segment length(i.e.0.5 m,1 m,and 2 m),then developed three taper equations based on the point-cloud-based diameter estimated previously.We estimated the total stem volume of the tree with eight models:six-point cloudbased(i.e.three taper and three cylinders)and two empirical.Finally,we used simulations to extrapolate the volume estimations of various methods for different diameters at breast height(DBH)classes.We found that all the point-cloud-based taper equations were similar in their performance(R2¼0:94,RMSE=4.6 cm)and produced mean volume estimates greater than mean estimates of the existing models.The cylinder models produced 11–16%greater mean volume estimates than the FIA-PNW estimate,with the 0.5 m segment length producing the greatest values,followed by the 1 m and 2 m segment length.The simulated data suggested that the mean volume estimates of a given DBH class are different when using different computation methods.ANOVA revealed a combined effect of the computation methods and the DBH class on the mean volume estimates.We conclude that the point-cloud-based taper equations,after being symmetrically calibrated,would be consistent with the regional stem volume estimates,whereas the cylinder models would be better in estimating individual stem volume.When constructing Lidar-based cylinder models in future applications,cylinder segment length would need to be adjusted to the length and DBH of the stem,as well as to the objectives of the research.展开更多
The rural population of Iran has a serious need for ICT application to launch e-marketing in order to increase employment.The important point is that most villagers are small holders,and an e-marketing model should be...The rural population of Iran has a serious need for ICT application to launch e-marketing in order to increase employment.The important point is that most villagers are small holders,and an e-marketing model should be proposed which is adopted and can be operationalized.Therefore,the purpose of the present study was modeling villagers’intention to adopt e-marketing and performing rural provincial clustering.Data were collected from approximately 1000 villages with ICToffices in all provinces.The research model was designed in a way that the Theory of Planned Behavior(TPB)was developed,and Rural Economy Geography was added as background.Therefore,the Geographic Model of Planned Behavior(GeoTPB)was proposed.The innovation of the current research is in using a combined model(behavioral and geographical)to adopt rural technology,which has presented a better understanding of adoption.Additionally,a two-stage structural equation modeling approach was used,which means that since the model was large and complicated,in the first stage,the non-significant paths were removed,and in the second stage,the model was simplified and proposed with the significant paths.This model predicted 76%of villagers’intention to adopt e-marketing.Also,the K-means clustering showed that based on economic and behavioral factors,the rural districts of Iran constituted 6 clusters.The interesting point was that the southern and southeastern provinces of Iran,which have been reported by the statistics center to be the less developed areas,were found to be the superior cluster in e-marketing adoption in the results of the current research.Therefore,the villages of Sistan and Baluchestan and Hormozgan provinces will be suitable as pilot provinces for e-marketing implementation.Considering the results of the GeoTPB model,priority would better be given to villages with appropriate access and a population with the potential to use technology.Additionally,regarding the government’s aim to develop rural businesses,it is suggested that the Transaction Services Model be launched as the national business and the related facilities be provided.展开更多
Dielectric spectroscopy has been employed as a simple,low cost and a non-destructive way for prediction of some physicochemical indices of kiwifruit during storage.A parallel-plate capacitor was developed and supplied...Dielectric spectroscopy has been employed as a simple,low cost and a non-destructive way for prediction of some physicochemical indices of kiwifruit during storage.A parallel-plate capacitor was developed and supplied with sinusoidal voltage waves within a frequency range of 40 kHz–20 MHz.Dielectric properties of samples were measured by the dielectric sensor.Additionally,changes associated with fruit ripening properties,including firmness,total soluble solid(TSS)and pH were determined as a function of time at 2C.The results showed that storage time significantly affected the quality characteristics of kiwifruit.Artificial neural networks(ANNs)were employed to develop models for prediction of quality indices from dielectric properties at the swept frequencies.Dielectric property features were selected as inputs while the quality indices including firmness,TSS and pH were chosen as output for the ANNs.The obtained models were able to predict the firmness,soluble solids content,and pH of kiwifruit non-destructively.Among predictive models,an ANN with a topology of 20-19-1 gave a perfect capability to predict the kiwifruit firmness with R2 value of 0.92.Results of this research show that this technique can be used as an efficient and non-destructive method for kiwifruit quality evaluation and monitoring the ripening.展开更多
The spent mushroom substrate(SMS)is a byproduct of cultivation of oyster mushroom(Pleurotus spp.)and represents the composted substrate that remains after completion the harvested crop.This study mentioned the role of...The spent mushroom substrate(SMS)is a byproduct of cultivation of oyster mushroom(Pleurotus spp.)and represents the composted substrate that remains after completion the harvested crop.This study mentioned the role of some effective date palm wastes in improving spent mushroom substrate properties which containing fibers of date palm Phoenix dactylifera L.(Fibrillum),mixed with white sawdust and wheat straw in three formulas.These mixtures of SMS namely,SMS1(wheat straw),SMS2(wheat straw 70%,sawdust 20%and date palm fiber 10%)and SMS3(wheat straw 50%,sawdust 30%and date palm fiber 20%)were obtained from locally mushroom farm in western Iraq and sent to determine some properties such as moisture content,dry matter,EC,pH,ash,carbon,nitrogen,protein contents and C:N ratio.Generally,determinations of Hydrogen ion concentration(pH)for SMS extracts had acidic value at average 5.06.The higher EC was 3.30 ms/cm for SMS1-P.ostreatus(white),while the lower value reached to 1.13 ms/cm for SMS3 of same species.The higher nitrogen content was 9.98 g/kg for SMS3-P.ostreatus(white),SMS1 of Pleurotus salmoneostramineus and SMS2-P.ostreatus(white),while,SMS3-P.salmoneostramineus had lower nitrogen content(6.65 g/kg).The higher C:N ratio was reported with SMS3 of P.salmoneostramineus at value 35.36,while SMS2-P.ostreatus(grey)had ratio 22.03,significantly(p<0.05).Overall,these SMS was suitable as a natural fertilizer and soil amender in agriculture and horticulture fields.展开更多
The aim of this study was to develop near infrared spectroscopy(NIRS)calibrations to predict quality parameters,dry matter(DM,g kg1)and crude protein(CP,g kg1 DM),in fresh un-dried grass.Knowledge of these parameters ...The aim of this study was to develop near infrared spectroscopy(NIRS)calibrations to predict quality parameters,dry matter(DM,g kg1)and crude protein(CP,g kg1 DM),in fresh un-dried grass.Knowledge of these parameters would enable more precise allocation of quality herbage to grazing livestock.Perennial ryegrass samples(n=1615)were collected over the 2017 and 2018 grazing seasons at Teagasc Moorepark to develop a NIRS calibration dataset.Additional samples were collected for an independent validation dataset(n=197)during the 2019 grazing season.Samples were scanned using a FOSS 6500 spectrometer at 2 nm intervals in the range of 1100~2500 nm and absorption was recorded as log 1/Reflectance.Reference wet chemistry analysis was carried out for both parameters and the resultant data were calibrated against spectral data by means of modified partial least squares regression.A range of mathematical spectral treatments were examined for each calibration,which were ranked in order of standard error of prediction(SEP)and ratio of percent deviation(RPD).Best performing calibrations achieved high predictive precision for DM(R2=0.86 SEP=9.46 g kg1,RPD=2.60)and moderate precision for CP(R2=0.84 SEP=20.38 g kg1 DM,RPD=2.37).These calibrations will aid the optimisation of grassland management and the development of precision agricultural technologies.展开更多
The present study aims at identifying and also prioritizing information technology barriers in supply chain of sugarcane in Khuzestan province.The statistical population of the study consisted of all senior managers o...The present study aims at identifying and also prioritizing information technology barriers in supply chain of sugarcane in Khuzestan province.The statistical population of the study consisted of all senior managers of sugarcane industries in the province.Of four large rel-evant companies in the field,34 senior managers were selected.The required data was col-lected through two stages hiring questionnaires.In order to evaluate information technology barriers at the supply chain level,six main dimensions were considered includ-ing technological,supply chain management,strategic,organizational,individual and cus-tomer barriers.DEMATEL technique was also used to identify the relationship between indicators,and the ANP method was also employed to prioritize the before mentioned bar-riers.The results of DEMATEL showed that strategic barrier indicator acts as the most influ-ential,and the supply chain management barrier indicator as the most impotent one.The results of ANP method showed that of all research indicators,customer barrier indicator(weight 0.00127)was the most important indicator.Next,organizational and supply chain management barrier indicators are ranked second and third,respectively.Also,among the 28 research components(sub-criteria),the“lack of financial support”component with the weight of 0.04821 ranked first,followed by“poor outsourcing management”and“high investment and installation costs”as second and third respectively,which also had the most impact on the sugarcane supply chain.展开更多
The Agriculture business domain,as a vital part of the overall supply chain,is expected to highly evolve in the upcoming years via the developments,which are taking place on the side of the Future Internet.This paper ...The Agriculture business domain,as a vital part of the overall supply chain,is expected to highly evolve in the upcoming years via the developments,which are taking place on the side of the Future Internet.This paper presents a novel Business-to-Business collaboration platform from the agri-food sector perspective,which aims to facilitate the collaboration of numerous stakeholders belonging to associated business domains,in an effective and flexible manner.The contemporary B2B collaboration schemes already place the requirements for swift deployment of cloud applications,capable of both integrating diverse legacy systems,as well as developing in a rapid way new services and systems,which will be able to instantly communicate and provide complete,"farm-to-fork"solutions for farmers,agrifood and logistics service providers,ICT companies,end-product producers,etc.To this end,this conceptual paper describes how these requirements are addressed via the FIspace B2B platform,focusing on the Greenhouse Management&Control scenarios.展开更多
Greenness identification from crop images captured outdoors is the important step for crop growth monitoring.The commonly used methods for greenness identification are based on visible spectral-index,such as the exces...Greenness identification from crop images captured outdoors is the important step for crop growth monitoring.The commonly used methods for greenness identification are based on visible spectral-index,such as the excess green index,the excess green minus excess red index,the vegetative index,the color index of vegetation extraction,the combined index.All these visible spectral-index based methods are working on the assumption that plants display a clear high degree of greenness,and soil is the only background element.In fact,the brightness and contrast of an image coming from outdoor environments are seriously affected by the weather conditions and the capture time.The color of the plant varies from dark green to bright green.The back ground elements may contain crop straw,straw ash besides soil.These environmental factors always make the visible spectral-index based methods unable to work correctly.In this paper,an HSV decision tree based method for greenness identification from maize seedling images captured outdoors is proposed.Firstly,the image was converted from RGB color space to HSV color space to avoid influence of illumination.Secondly,most of the background pixels were removed according to their hue values compared with the ones of green plants.Thirdly,the pixels of wheat straws whose hue values were intersected with tender green leaves were eliminated subject to their hues,saturations and values.At last,thresholding was employed to get the green plants.The results indicate that the proposed method can recognize greenness pixels correctly from the crop images captured outdoors.展开更多
In this study,the sensitivity of a novel dehumidification requirement model(DehumReq)is analyzed to evaluate the effect of the predominant factors on the dehumidification needs of the greenhouses.The hourly dehumidifi...In this study,the sensitivity of a novel dehumidification requirement model(DehumReq)is analyzed to evaluate the effect of the predominant factors on the dehumidification needs of the greenhouses.The hourly dehumidification demand and sensitivity coefficient(SC)are estimated for three different seasons:warm(July),mild(May),and cold(November),by using the local sensitivity analysis method.Based on SC values,the solar radiation,air exchange,leaf area index(LAI),and indoor setpoints(temperature,relative humidity(RH),and water vapor partial pressure(WVPP))have significant impact on the dehumidifi-cation needs,and the impact varies from season to season.Most parameters have a higher SC in summer,whereas solar radiation and LAI have a higher SC in mild season.The dehumidification load increases 4 times of its base value with increasing solar radiation by 200 W/m^(2),and the highest LAI(10)caused 5 times increment of the load.The changing of WVPP from its base value(1.5 kPa)to maximum(2.9 kPa)reduces the load 70%in summer.Air exchange was found to be the most crucial parameter because it is the main dehumidification approach that has a large range and is easily adjustable for any greenhouses.Sufficient air exchange by ventilation or infiltration will reduce the dehumidification load to zero in May and November and minimizes it to only nighttime load in July.For the other parameters,higher ambient air RH and indoor air speed will result in higher the dehumidification load;whereas higher inner surface condensation will result in lower dehumidifi-cation load.The result of this study will assist in the selection of the most efficient moisture control strategies and techniques for greenhouse humidity control.展开更多
The current work aims to explore the suitable drying technique for peanut pods which can be used for seeds or edible peanuts.Four drying methods,namely naturally-open sun drying as the control check(CK),hot air drying...The current work aims to explore the suitable drying technique for peanut pods which can be used for seeds or edible peanuts.Four drying methods,namely naturally-open sun drying as the control check(CK),hot air drying(HAD),pulsed vacuum drying(PVD),and radio frequency combined hot air drying(RF-HAD),were employed to dry peanut pods,and their effects on the nutritional quality attributes in terms of protein,fat,fatty acid contents,etc.,germination characteristics,microstructure,color,texture,acid value and peroxide value of peanuts were explored.Mathematical models of peanuts drying with four drying methods were also established.According to the statistical parameters including the determination coefficient(R^(2))、root mean square error(RMSE)and chi-square value(v^(2)),theWeibull model was best for predicting the moisture ratio change kinetics of peanuts during its four drying processes.There were significant differences in physicochemical indexes of peanut by different drying methods(p<0.05).Fat and oleic acid contents under RF-HAD were significantly higher than those by the other three drying methods.Compared with the naturally-open sun drying,RF-HAD reduced drying time by 76.70%and the microstructure of RF-HAD peanuts produced larger and more cavities.The RF-HAD kept better comprehensive nutritional quality,but the germination rate was reduced by 27.80%.PVD could maintain good nutritional quality and germination rate among these mechanical drying technologies.However,PVD had a longer drying time of 9.5 h than RF-HAD and HAD,and the microstructure of pulsed vacuum dried peanuts showed dense structure and less cavity.Hot air-dried peanut kernel held the highest protein(28.75%),fatty acids contents(26.11%)and germination rate(88.00%).However,peanut kernel dried by HAD showed poor qualities,such as high acid value,peroxide value and large color changes.These findings indicated RF-HAD was a promising drying technique for edible peanuts regarding the higher drying rate and better-quality preservation,while HAD was suitable for peanut seeds drying as it could well protect the germination rate.展开更多
Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work propose...Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work proposes an automated system for classifying Tuna spe-cies based on their images.An ensemble of region-based deep neural networks is used.A sub region contrast stretching operation is applied to enhance the images.Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks(CNN).After fine-tuning the models,the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor(G2DLBP).Statistical features from the descriptor are applied to different classifiers,and the best clas-sifier for each image region model is identified.Different ensemble methods are subse-quently used to combine the three CNN-G2DLBP models.Among the ensemble techniques,super learner ensemble method with random forest(RF)classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%.The perfor-mance of different ensemble methods is analyzed in terms of accuracy,precision,recall,and f-score.The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset.An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.展开更多
基金the support the research grant under“Collaborative and Innovation Scheme”of TEQIP-Ⅲ with project title“Development of Novel Approaches for Recognition and Grading of Fruits using Image processing and Computer Intelligence”,with reference letter No.VSSUT/TEQIP/113/2020.
文摘Papaya(Carica papaya)is a tropical fruit having commercial importance because of its high nutritive and medicinal value.The packaging of papaya fruit as per its maturity status is an essential task in the fruit industry.The manual grading of papaya fruit based on human visual perception is time-consuming and destructive.The objective of this paper is to suggest a novel non-destructive maturity status classification of papaya fruits.The paper suggested two approaches based on machine learning and transfer learning for classification of papaya maturity status.Also,a comparative analysis is carried out with different methods of machine learning and transfer learning.The experimentation is carried out with 300 papaya fruit sample images which includes 100 of each three maturity stages.The machine learning approach includes three sets of features and three classifiers with their different kernel functions.The features and classifiers used in machine learning approaches are local binary pattern(LBP),histogram of oriented gradients(HOG),Gray Level Cooccurrence Matrix(GLCM)and k-nearest neighbour(KNN),support vector machine(SVM),Naı¨ve Bayes respectively.The transfer learning approach includes seven pretrained models such as ResNet101,ResNet50,ResNet18,VGG19,VGG16,GoogleNet and AlexNet.The weighted KNN with HOG feature outperforms other machine learningbased classification model with 100%of accuracy and 0.0995 s training time.Again,among the transfer learning approach based classification model VGG19 performs better with 100%accuracy and 1 min 52 s training time with consideration of early stop training.The proposed classification method for maturity classification of papaya fruits,i.e.VGG19 based on transfer learning approach achieved 100%accuracy which is 6%more than the existing method.
基金supported by Key Technologies Research and Development Program(CN),funding number,2018YFE0108500the International Cooperation Fund Project of Beijing Academy of Agriculture and Forestry Sciences,funding number 2019HP002Beijing Science and Technology Planning,funding number Z191100004019007。
文摘Broiler chickens are traditionally weighed by steelyard or platform scale,which is timeconsuming and labor-intensive.Broiler chickens usually exhibit stress-related behavior during weighing.The 3D camera-based weighing system for broiler chickens can only weigh the broiler chicken in the monitoring area.Usually,it makes poor weight prediction due to poor segmentation especially when the broiler chicken is flapping its wings.To solve these issues,we developed one simple and low-cost weighing system with high stability and accuracy.A validity value extraction method from dynamic weighing was proposed.Then,an improved amplitude-limiting filtering algorithm and a BP neural networks model were developed to avoid accidental interference.The BP neural networks model used daily weight gain,day-age,average velocity,and the weight data after filtering algorithm as the input layer.The weighing system was tested in a commercial Beijing Fatty Chickens house with Beijing Fatty Chickens.We tested thirteen groups of Beijing Fatty Chickens of different weights,from 500 g to 1800 g in intervals of 100 g,using the three different methods:no filtering algorithm or BP neural networks,only the improved amplitude-limiting filtering algorithm and a hybrid of the improved amplitude-limiting filtering algorithm and BP neural networks.The results showed that the hybrid algorithm had a better performance in minimizing the error,lowering from the original 6%down to 3%.The accurate weight data was transmitted to the remote service platform for further decision-making,such as activity analysis,feeding management,and health alerts.
基金This project was funded in part by NSF/USDA-NIFA Cyber Physical Systems and USDA-NIFA WNP0745 projects.The author extends their gratitude to Dr.Sindhuja Sankaran and Mr.Chongyuan Zhang of Washington State University for their assistance in completion of this study.
文摘Heat and light stress causes sunburn to the maturing apple fruits and results in crop production and quality losses.Typically,when the fruit surface temperature(FST)rises above critical limits for a prolonged duration,the fruit may suffer several physiological disorders including sunburn.To manage apple sunburn,monitoring FST is critical and our group at Washington State University is developing a noncontact smart sensing system that integrates thermal infrared and visible imaging sensors for real time FST monitoring.Pertinent system needs to perform in-field imagery data analysis onboard a single board computer with processing unit that has limited computational resources.Therefore,key objective of this study was to develop a novel image processing algorithm optimized to use available resources of a single board computer.Algorithm logic flow includes color space transformation,k-means++classification and morphological operators prior to fruit segmentation and FST estimation.The developed algorithm demonstrated the segmentation accuracy of 57.78%(missing error=12.09%and segmentation error=0.13%).This aided successful apple FST estimation that was 10–18C warmer than ambient air temperature.Moreover,algorithm reduced the imagery data processing time cost of the smart sensing systemfrom 87 s to 44 s using image compression approach.
基金This work was part of the joint projects BIOGAS-ENZYME and BIOGAS-BIOCOENOSIS supported by the German Federal Ministry of Food and Agriculture(BMEL),grant nos.22027707,22010711 and 22028911[27].
文摘This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate.The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany.In this context,the concentration of volatile fatty acids,total solids,volatile solids acid detergent fibre,acid detergent lignin,neutral detergent fibre,ammonium nitrogen,hydraulic retention time,and organic loading rate were used.Artificial neural networks(ANN)were established to predict the biogas production rate.An ant colony optimization and genetic algorithms were implemented to perform the variable selection.They identified the significant process variables,reduced the model dimension and improved the prediction capacity of the ANN models.The best prediction of the biogas production rate was obtained with an error of prediction of 6.24%and a coefficient of determination of R2=0.9.
基金supported by the International Science&Technology Cooperation Program of China(2015DFA00090)Special Fund for Agro-scientific Research in the Public Interest(201203017).
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.
文摘In response to the challenges in providing real-time extraction of crop biophysical signatures,computer vision in computational crop phenotyping highlights the opportunities of computational intelligence solutions.Shadow and angular brightness due to the presence of photosynthetic light unevenly illuminate crop canopy.In this study,a novel vegetation index named artificial bee colony-optimized visible band oblique dipyramid greenness index(vODGIabc)was proposed to enhance vegetation pixels by correcting the saturation and brightness levels,and the ratio of visible RGB reflectance intensities.Consumer-grade smartphone was used to acquire indoor and outdoor aquaponic lettuce images daily for full 6-week crop life cycle.The introduced saturation rectification coeffi-cient(X),value rectification coefficient(m),green–red wavelength adjustment factor(a),and green–blue wavelength adjustment factor(b)on the original triangular greenness index resulted in 3D canopy reflectance spectrum with two oblique tetrahedrons formed by connecting the vertices of visible RGB band reflectance and maximum wavelength point map to corresponding saturation and value of lettuce-captured images.Hybrid neighborhood component analysis(NCA),minimum redundancy maximum relevance(MRMR),Pearson’s correlation coefficient(PCC),and analysis of variance(ANOVA)weighted most of the canopy area,energy,and homogeneity.Strong linear relationships were exhibited by using vODGIabc in estimating lettuce crop fresh weight,height,number of spanning leaves,leaf area index,and growth stage with R2 values of 0.9368 for InceptionV3,0.9574 for ResNet101,0.9612 for ResNet101,0.9999 for Gaussian processing regression,and accuracy of 88.89%for ResNet101,respectively.This low-cost approach on developing greenness index for biophysical signatures estimation proved to be more accurate than the previously established triangular greenness index(TGI)using RGB smartphone camera.
文摘Lodging occurs when the crop canopy is too heavy for the strength of the stem and it fallsover onto the ground. This decreases crop yield and quality, and it makes harvest difficult.A research experiment was set up in a spearmint field on a center pivot with mid elevationspray application (MESA) overhead sprinklers, where the water was applied from a “midelevation” of 2 m above the ground level (AGL), and low elevation precision application(LEPA) sprinklers, where the water was emitted directly onto the soil surface through draghoses without wetting the crop canopy. Every-other span of this full-size center pivot wasconfigured with MESA and LEPA sprinklers alternatively. In 2018, imagery was collectedwith an unmanned aerial vehicle (UAV) from a cross section of this field. In 2019, a crosssection was again collected, but in addition UAV imagery was collected from marked lodgedand un-lodged areas of the field to validate the lodging detection method. These UAV-basedimagery data were captured with a ground sample distance (GSD) of 0.03 m. This researchintroduces using the texture feature, which is based on image entropy, was used to evaluate the degree of lodging. The results from 2018 showed that the average entropy of thegrayscale image from LEPA (5.5 (mean) ± 0.27 (standard deviation)) was significantly(P < 0.0001) greater than the average entropy (5.0 ± 0.25) of MESA. Also, the entropy valueextracted from the images in 2019 from the marked un-lodged locations were significantlyhigher compared to that of the lodged areas. Overall, the LEPA irrigation treatment was significantly less lodged compared to MESA. Moreover, the entropy value, or texture feature, isa viable method for estimating lodging using low altitude RGB imagery.
基金financially supported by The International Technology Cooperation of China(2015DFA00090)Key Laboratory of Agricultural Information Acquisition Technology,Thousand Youth Talents Plan from the Organization Department of CCP Central Committee(China Agricultural University,China,China Grant No.62339001)Fundamental Research Funds for the Central Universities in China,China(Grant No.2018QC174)。
文摘The use of electrical energy for heating and cooling systems to control the temperature in greenhouses will lead to high production and product costs.To solve this problem,shallow geothermal energy as a local source of energy could be applied.In this study,a measurement model,the distribution profiles of temperature,and a preliminary assessment of the geothermal potential in the shallow zone at depths of 0.1 m to 3.6 m in Shouguang City,Shandong Province,eastern China were presented.The measurement results showed that the annual average temperature at depths of 0.1–3.6 m ranged from 13.1℃ to 17.6℃.Preliminary assessment results of the geothermal potential showed that the daily average temperature difference between the air and at depths of 1.5–3.6 m was mainly from 10℃ to 25℃ during the winter months and between-15℃ and-5℃ during the summer months.Therefore,the heating systems could operate during January,February,November,and December.In May,June,and July,the cooling systems could be applied.Moreover,the measurement model gave good stability results,and it could be used in combination with the monitoring of the groundwater table,a survey of the thermal conductivity of the soil,climate change studies,which helps reduce unnecessary time and costs.
基金The authors wish to acknowledge financial support provided by the Special Account for Research Funds of the Technological Education Institute of Central Macedonia,Greece,under grant SMF/LG/060219–23/3/19.
文摘Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental and biological factors(e.g.soil compaction)the weight and size of the machinery cannot be further physically optimized.Thus,only marginal improvements are possible to increase equipment effectiveness.On the contrary,late technological advances in ICT provide the ground for significant improvements in agriproduction efficiency.In this work,the V-Agrifleet tool is presented and demonstrated.VAgrifleet is developed to provide a “hands-free”interface for information exchange and an “Olympic view”to all coordinated users,giving them the ability for decentralized decision-making.The proposed tool can be used by the end-users(e.g.farmers,contractors,farm associations,agri-products storage and processing facilities,etc.)order to optimize task and time management.The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations.Its vendorindependent architecture,voice-driven interaction,context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system.
文摘As a complement to traditional estimates of stem dimensions from numerical models,terrestrial light detection and ranging(Lidar)provides direct stem diameter and volume values using cylindrical models constructed from point clouds.This study used two approaches to estimate total stem volume using Lidar and compared them with two empirical equations,one used by the Forest Inventory Analysis in the Pacific Northwest(FIA-PNW)and one based on a taper equation.We fitted point clouds of 10 Douglas-fir with three sets of cylinder models that are distinguished by their segment length(i.e.0.5 m,1 m,and 2 m),then developed three taper equations based on the point-cloud-based diameter estimated previously.We estimated the total stem volume of the tree with eight models:six-point cloudbased(i.e.three taper and three cylinders)and two empirical.Finally,we used simulations to extrapolate the volume estimations of various methods for different diameters at breast height(DBH)classes.We found that all the point-cloud-based taper equations were similar in their performance(R2¼0:94,RMSE=4.6 cm)and produced mean volume estimates greater than mean estimates of the existing models.The cylinder models produced 11–16%greater mean volume estimates than the FIA-PNW estimate,with the 0.5 m segment length producing the greatest values,followed by the 1 m and 2 m segment length.The simulated data suggested that the mean volume estimates of a given DBH class are different when using different computation methods.ANOVA revealed a combined effect of the computation methods and the DBH class on the mean volume estimates.We conclude that the point-cloud-based taper equations,after being symmetrically calibrated,would be consistent with the regional stem volume estimates,whereas the cylinder models would be better in estimating individual stem volume.When constructing Lidar-based cylinder models in future applications,cylinder segment length would need to be adjusted to the length and DBH of the stem,as well as to the objectives of the research.
基金This paper was supported by the“Iran National Science Foundation”(INSF)Grant No.96001269.
文摘The rural population of Iran has a serious need for ICT application to launch e-marketing in order to increase employment.The important point is that most villagers are small holders,and an e-marketing model should be proposed which is adopted and can be operationalized.Therefore,the purpose of the present study was modeling villagers’intention to adopt e-marketing and performing rural provincial clustering.Data were collected from approximately 1000 villages with ICToffices in all provinces.The research model was designed in a way that the Theory of Planned Behavior(TPB)was developed,and Rural Economy Geography was added as background.Therefore,the Geographic Model of Planned Behavior(GeoTPB)was proposed.The innovation of the current research is in using a combined model(behavioral and geographical)to adopt rural technology,which has presented a better understanding of adoption.Additionally,a two-stage structural equation modeling approach was used,which means that since the model was large and complicated,in the first stage,the non-significant paths were removed,and in the second stage,the model was simplified and proposed with the significant paths.This model predicted 76%of villagers’intention to adopt e-marketing.Also,the K-means clustering showed that based on economic and behavioral factors,the rural districts of Iran constituted 6 clusters.The interesting point was that the southern and southeastern provinces of Iran,which have been reported by the statistics center to be the less developed areas,were found to be the superior cluster in e-marketing adoption in the results of the current research.Therefore,the villages of Sistan and Baluchestan and Hormozgan provinces will be suitable as pilot provinces for e-marketing implementation.Considering the results of the GeoTPB model,priority would better be given to villages with appropriate access and a population with the potential to use technology.Additionally,regarding the government’s aim to develop rural businesses,it is suggested that the Transaction Services Model be launched as the national business and the related facilities be provided.
文摘Dielectric spectroscopy has been employed as a simple,low cost and a non-destructive way for prediction of some physicochemical indices of kiwifruit during storage.A parallel-plate capacitor was developed and supplied with sinusoidal voltage waves within a frequency range of 40 kHz–20 MHz.Dielectric properties of samples were measured by the dielectric sensor.Additionally,changes associated with fruit ripening properties,including firmness,total soluble solid(TSS)and pH were determined as a function of time at 2C.The results showed that storage time significantly affected the quality characteristics of kiwifruit.Artificial neural networks(ANNs)were employed to develop models for prediction of quality indices from dielectric properties at the swept frequencies.Dielectric property features were selected as inputs while the quality indices including firmness,TSS and pH were chosen as output for the ANNs.The obtained models were able to predict the firmness,soluble solids content,and pH of kiwifruit non-destructively.Among predictive models,an ANN with a topology of 20-19-1 gave a perfect capability to predict the kiwifruit firmness with R2 value of 0.92.Results of this research show that this technique can be used as an efficient and non-destructive method for kiwifruit quality evaluation and monitoring the ripening.
文摘The spent mushroom substrate(SMS)is a byproduct of cultivation of oyster mushroom(Pleurotus spp.)and represents the composted substrate that remains after completion the harvested crop.This study mentioned the role of some effective date palm wastes in improving spent mushroom substrate properties which containing fibers of date palm Phoenix dactylifera L.(Fibrillum),mixed with white sawdust and wheat straw in three formulas.These mixtures of SMS namely,SMS1(wheat straw),SMS2(wheat straw 70%,sawdust 20%and date palm fiber 10%)and SMS3(wheat straw 50%,sawdust 30%and date palm fiber 20%)were obtained from locally mushroom farm in western Iraq and sent to determine some properties such as moisture content,dry matter,EC,pH,ash,carbon,nitrogen,protein contents and C:N ratio.Generally,determinations of Hydrogen ion concentration(pH)for SMS extracts had acidic value at average 5.06.The higher EC was 3.30 ms/cm for SMS1-P.ostreatus(white),while the lower value reached to 1.13 ms/cm for SMS3 of same species.The higher nitrogen content was 9.98 g/kg for SMS3-P.ostreatus(white),SMS1 of Pleurotus salmoneostramineus and SMS2-P.ostreatus(white),while,SMS3-P.salmoneostramineus had lower nitrogen content(6.65 g/kg).The higher C:N ratio was reported with SMS3 of P.salmoneostramineus at value 35.36,while SMS2-P.ostreatus(grey)had ratio 22.03,significantly(p<0.05).Overall,these SMS was suitable as a natural fertilizer and soil amender in agriculture and horticulture fields.
文摘The aim of this study was to develop near infrared spectroscopy(NIRS)calibrations to predict quality parameters,dry matter(DM,g kg1)and crude protein(CP,g kg1 DM),in fresh un-dried grass.Knowledge of these parameters would enable more precise allocation of quality herbage to grazing livestock.Perennial ryegrass samples(n=1615)were collected over the 2017 and 2018 grazing seasons at Teagasc Moorepark to develop a NIRS calibration dataset.Additional samples were collected for an independent validation dataset(n=197)during the 2019 grazing season.Samples were scanned using a FOSS 6500 spectrometer at 2 nm intervals in the range of 1100~2500 nm and absorption was recorded as log 1/Reflectance.Reference wet chemistry analysis was carried out for both parameters and the resultant data were calibrated against spectral data by means of modified partial least squares regression.A range of mathematical spectral treatments were examined for each calibration,which were ranked in order of standard error of prediction(SEP)and ratio of percent deviation(RPD).Best performing calibrations achieved high predictive precision for DM(R2=0.86 SEP=9.46 g kg1,RPD=2.60)and moderate precision for CP(R2=0.84 SEP=20.38 g kg1 DM,RPD=2.37).These calibrations will aid the optimisation of grassland management and the development of precision agricultural technologies.
文摘The present study aims at identifying and also prioritizing information technology barriers in supply chain of sugarcane in Khuzestan province.The statistical population of the study consisted of all senior managers of sugarcane industries in the province.Of four large rel-evant companies in the field,34 senior managers were selected.The required data was col-lected through two stages hiring questionnaires.In order to evaluate information technology barriers at the supply chain level,six main dimensions were considered includ-ing technological,supply chain management,strategic,organizational,individual and cus-tomer barriers.DEMATEL technique was also used to identify the relationship between indicators,and the ANP method was also employed to prioritize the before mentioned bar-riers.The results of DEMATEL showed that strategic barrier indicator acts as the most influ-ential,and the supply chain management barrier indicator as the most impotent one.The results of ANP method showed that of all research indicators,customer barrier indicator(weight 0.00127)was the most important indicator.Next,organizational and supply chain management barrier indicators are ranked second and third,respectively.Also,among the 28 research components(sub-criteria),the“lack of financial support”component with the weight of 0.04821 ranked first,followed by“poor outsourcing management”and“high investment and installation costs”as second and third respectively,which also had the most impact on the sugarcane supply chain.
基金The research,leading to these results,has received funding from the European Commission’s Seventh Framework program FP7-ICT-2012 under grant agreement N°604123 also referred to as FIspace(Future Internet Business Collaboration Networks in Agri-Food,Transport and Logistics).
文摘The Agriculture business domain,as a vital part of the overall supply chain,is expected to highly evolve in the upcoming years via the developments,which are taking place on the side of the Future Internet.This paper presents a novel Business-to-Business collaboration platform from the agri-food sector perspective,which aims to facilitate the collaboration of numerous stakeholders belonging to associated business domains,in an effective and flexible manner.The contemporary B2B collaboration schemes already place the requirements for swift deployment of cloud applications,capable of both integrating diverse legacy systems,as well as developing in a rapid way new services and systems,which will be able to instantly communicate and provide complete,"farm-to-fork"solutions for farmers,agrifood and logistics service providers,ICT companies,end-product producers,etc.To this end,this conceptual paper describes how these requirements are addressed via the FIspace B2B platform,focusing on the Greenhouse Management&Control scenarios.
基金The authors thank The Ministry of Science and Technology of the People’s Republic of China(2013DFA11320)Hebei Natural Science Foundation(F2015201033),for financial support.
文摘Greenness identification from crop images captured outdoors is the important step for crop growth monitoring.The commonly used methods for greenness identification are based on visible spectral-index,such as the excess green index,the excess green minus excess red index,the vegetative index,the color index of vegetation extraction,the combined index.All these visible spectral-index based methods are working on the assumption that plants display a clear high degree of greenness,and soil is the only background element.In fact,the brightness and contrast of an image coming from outdoor environments are seriously affected by the weather conditions and the capture time.The color of the plant varies from dark green to bright green.The back ground elements may contain crop straw,straw ash besides soil.These environmental factors always make the visible spectral-index based methods unable to work correctly.In this paper,an HSV decision tree based method for greenness identification from maize seedling images captured outdoors is proposed.Firstly,the image was converted from RGB color space to HSV color space to avoid influence of illumination.Secondly,most of the background pixels were removed according to their hue values compared with the ones of green plants.Thirdly,the pixels of wheat straws whose hue values were intersected with tender green leaves were eliminated subject to their hues,saturations and values.At last,thresholding was employed to get the green plants.The results indicate that the proposed method can recognize greenness pixels correctly from the crop images captured outdoors.
文摘In this study,the sensitivity of a novel dehumidification requirement model(DehumReq)is analyzed to evaluate the effect of the predominant factors on the dehumidification needs of the greenhouses.The hourly dehumidification demand and sensitivity coefficient(SC)are estimated for three different seasons:warm(July),mild(May),and cold(November),by using the local sensitivity analysis method.Based on SC values,the solar radiation,air exchange,leaf area index(LAI),and indoor setpoints(temperature,relative humidity(RH),and water vapor partial pressure(WVPP))have significant impact on the dehumidifi-cation needs,and the impact varies from season to season.Most parameters have a higher SC in summer,whereas solar radiation and LAI have a higher SC in mild season.The dehumidification load increases 4 times of its base value with increasing solar radiation by 200 W/m^(2),and the highest LAI(10)caused 5 times increment of the load.The changing of WVPP from its base value(1.5 kPa)to maximum(2.9 kPa)reduces the load 70%in summer.Air exchange was found to be the most crucial parameter because it is the main dehumidification approach that has a large range and is easily adjustable for any greenhouses.Sufficient air exchange by ventilation or infiltration will reduce the dehumidification load to zero in May and November and minimizes it to only nighttime load in July.For the other parameters,higher ambient air RH and indoor air speed will result in higher the dehumidification load;whereas higher inner surface condensation will result in lower dehumidifi-cation load.The result of this study will assist in the selection of the most efficient moisture control strategies and techniques for greenhouse humidity control.
基金supported by key research and development and promotion projects of Henan Province(212102110232)the innovation and creativity project of Henan academy of agricultural sciences(2020CX15)+1 种基金independent innovation fund of Henan academy of agricultural Sciences(2021ZC66)the open fund of institute of ocean research,Bohai University(BDHYYJY2020003).
文摘The current work aims to explore the suitable drying technique for peanut pods which can be used for seeds or edible peanuts.Four drying methods,namely naturally-open sun drying as the control check(CK),hot air drying(HAD),pulsed vacuum drying(PVD),and radio frequency combined hot air drying(RF-HAD),were employed to dry peanut pods,and their effects on the nutritional quality attributes in terms of protein,fat,fatty acid contents,etc.,germination characteristics,microstructure,color,texture,acid value and peroxide value of peanuts were explored.Mathematical models of peanuts drying with four drying methods were also established.According to the statistical parameters including the determination coefficient(R^(2))、root mean square error(RMSE)and chi-square value(v^(2)),theWeibull model was best for predicting the moisture ratio change kinetics of peanuts during its four drying processes.There were significant differences in physicochemical indexes of peanut by different drying methods(p<0.05).Fat and oleic acid contents under RF-HAD were significantly higher than those by the other three drying methods.Compared with the naturally-open sun drying,RF-HAD reduced drying time by 76.70%and the microstructure of RF-HAD peanuts produced larger and more cavities.The RF-HAD kept better comprehensive nutritional quality,but the germination rate was reduced by 27.80%.PVD could maintain good nutritional quality and germination rate among these mechanical drying technologies.However,PVD had a longer drying time of 9.5 h than RF-HAD and HAD,and the microstructure of pulsed vacuum dried peanuts showed dense structure and less cavity.Hot air-dried peanut kernel held the highest protein(28.75%),fatty acids contents(26.11%)and germination rate(88.00%).However,peanut kernel dried by HAD showed poor qualities,such as high acid value,peroxide value and large color changes.These findings indicated RF-HAD was a promising drying technique for edible peanuts regarding the higher drying rate and better-quality preservation,while HAD was suitable for peanut seeds drying as it could well protect the germination rate.
文摘Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work proposes an automated system for classifying Tuna spe-cies based on their images.An ensemble of region-based deep neural networks is used.A sub region contrast stretching operation is applied to enhance the images.Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks(CNN).After fine-tuning the models,the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor(G2DLBP).Statistical features from the descriptor are applied to different classifiers,and the best clas-sifier for each image region model is identified.Different ensemble methods are subse-quently used to combine the three CNN-G2DLBP models.Among the ensemble techniques,super learner ensemble method with random forest(RF)classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%.The perfor-mance of different ensemble methods is analyzed in terms of accuracy,precision,recall,and f-score.The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset.An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.
