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
This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology,inspired by the needs of problem solving for biological,agricultural,a...This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology,inspired by the needs of problem solving for biological,agricultural,aquacultural and environmental systems.The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based,unified models that can also be applied for the effective coupling amongst the various(sophisticated field-specific,sensor data processing-based,upper level optimization-driven,etc.)models.The scientific problem addressed in this innovation is to develop Programmable Process Structures(PPS)by combining functional basis of systems theory,structural approach of net theory and computational principles of agent based modeling.PPS offers a novel framework for the automatic generation of easily extensible and connectible,unified models for the underlying complex systems.PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure.The models consist of unified state and transition elements.The local program containing prototype elements,derived also from the meta-prototypes,are responsible for the case-specific calculations.The integrity and consistency of PPS architecture are based on the meta-prototypes,prepared to distinguish between the conservation-laws-based measures and the signals.The simulation is based on data flows amongst the state and transition elements,as well as on the unification based data transfer between these elements and their calculating prototypes.This architecture and its AI language-based(Prolog)implementation support the integration of various field-and task-specific models,conveniently.The better understanding is helped by a simple example.The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications,focusing on the recently studied agricultural and aquacultural cases.展开更多
IoT based agriculture(Ag-IoT)is an emerging communication technology that is widely adopted by agricultural entrepreneurs and farmers to perform agricultural agro-chores in the farm to improve productivity,for better ...IoT based agriculture(Ag-IoT)is an emerging communication technology that is widely adopted by agricultural entrepreneurs and farmers to perform agricultural agro-chores in the farm to improve productivity,for better monitoring,and to reduce labor costs.However,the use of the Internet in Ag-IoT facilitates real-time functionality in an agriculture system,it can increase the risk of security breaches and cyber attacks that would cause the Ag-IoT system to malfunction and can affect its productivity.Ag-IoT is overlooked in cyber security parameters,which can have severe impacts on its trustworthiness and adoption by agricultural communities.To address this gap,this article presents a systematic study of the literature published between 2001 and 2023 that discusses advances in Ag-IoT technology.The subjects included in the study on Ag-IoT are emerging applications,different IoT architectures,suspected cyber attacks and cyber crimes,and challenges in incident response and digital forensics.The findings of this study encourage the reader to explore future potential research avenues related to the security risks and challenges of Ag-IoT,as well as the readiness for incident response and forensic investigation in the smart agricultural sector.The main conclusion of this study is that security must be ensured in Ag-IoT environments to offer uninterrupted services and also there is a need for forensic readiness for effective investigation in the event of unanticipated security incidents.展开更多
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
Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and downstream.This content can be used to monitor the progress of the oil palm fr...Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and downstream.This content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product profitability.Based on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm fruit.First,350 palm fruits with Tenera varieties(Elaeis guineensis Jacq.var.tenera),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content extraction.Then,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil content.Finally,a robust EMD-ANN model is generated by optimizing the lowest possible errors.Based on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±0.208.These results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally friendly.Therefore,the proposed technique has a promising potential to be applied in the oil palm industry.Measurements like this will lead to the effective and efficient management of oil palm production.展开更多
This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the...This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the information transmission layer and the sys-tem architecture.The system has employed multiple sensors terminal to real-time acqui-sition,including air and water temperatures,dissolved oxygen etc.LoRa protocol is suitable for sending small data and the 4G was employed to collect data and send to the cloud plat-form.Java is used to develop background applications,to access cloud platforms and local data processing.Based on the collection and processing of environmental data and cloud service platform,the mobile application program client and remote login desktop have been developed.It has been implemented and tested in Tongzhou,Beijing for 3 months in 2020.The results showed the proposed monitoring system stability for overall operation and accuracy data transmission,which can support the actual hydroponics and aquacul-ture production management.After analysis of monitoring data collected from the devel-oped monitoring system,indoor air and water temperature have the obvious correlation with atmospheric pressure(0.7 and 0.9)and outdoor temperature(1.0 and 0.9),respectively.展开更多
Pine(Pinus ssp.)needle biomass(PNB)was pyrolyzed at 400℃ for 3 h and then subjected to hydrothermal treatment at the same temperature for 10 min,with and without the addition of potassium chloride(KCl).The suspension...Pine(Pinus ssp.)needle biomass(PNB)was pyrolyzed at 400℃ for 3 h and then subjected to hydrothermal treatment at the same temperature for 10 min,with and without the addition of potassium chloride(KCl).The suspensions of the materials treated hydrothermally were submitted to ultrasound for 5,10,20,30 and 60 min.Diffuse reflectance UV-Vis(DRUV)spectroscopy results for the materials with variations in sonication times were obtained and the band gap energy(E)was calculated.A culture medium containing Saccharomyces cerevisiae was monitored during 30 min of exposure to different materials for the calculation of the 10%(IC10),30%(IC30)and 50%(IC50)inhibitory concentrations.Of the samples that underwent ultrasonic treatment,the material pyrolyzed at 400℃ without the addition of potassium ions(PNB4003H60)presented the greatest inhibition of 10% of the Saccharomyces cerevisiae cultures.Of the materials without the addition of potassium,the material pyrolyzed and sonicated for 10 min(PNB4003H10)showed the best characteristics for use as a support for Saccharomyces cerevisiae organisms.展开更多
The quality of fruits can be reduced due to some damages and impacts which occur during harvesting.One of the most important mechanical damages that can reduce the quality of ripe fruit quality is abrasion damage.This...The quality of fruits can be reduced due to some damages and impacts which occur during harvesting.One of the most important mechanical damages that can reduce the quality of ripe fruit quality is abrasion damage.This study focused on the effect of dynamic loading based on customary harvesting method on mulberry fruit properties.To this end,different maturity stage and storage regimes were considered.Color quality parameters,firmness,total soluble solid(TSS),total anthocyanins content(TAC)and abrasion area were the measured factors.The results revealed that none of the surveyed factors were stable during the experiment.The lightness(L^*),redness(a^*),yellowness(b^*),C^* value,firmness,TSS and TAC of immature and mature mulberry decreased during storage.The value of a^*,b^* and C^* increased as dropping height increased.However,L*value,firmness,TSS and TAC of mulberry fruit decreased at both maturity stages(immature and mature mulberry).Moreover,abrasion area increased at immature and mature mulberries by increasing the dropping height and storage time.展开更多
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 ai...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.展开更多
The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf bl...The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease.This paper demonstrates a methodology for classification of three important foliar diseases in banana,using local texture features.The disease affected regions are identified using image enhancement and color segmentation.Segmented images are converted to transform domain using three image transforms(DWT,DTCWT and Ranklet transform).Feature vector is extracted from transform domain images using LBP and its variants(ELBP,MeanELBP and MedianELBP).These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure.Experimental results showed best classification performance for ELBP features extracted from DTCWT domain(accuracy 95.4%,precision 93.2%,sensitivity 93.0%,Fscore 93.0%and specificity 96.4%).Compared with traditional methods of feature extraction,this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.展开更多
Agriculture is the backbone of the Indian Economy.However,statistics show that the rural population and arable land per person is declining.This is an ominous development for a country with a population of more than o...Agriculture is the backbone of the Indian Economy.However,statistics show that the rural population and arable land per person is declining.This is an ominous development for a country with a population of more than one billion,with over sixty-six percent living in rural areas.This paper aims to review current studies and research in agriculture,employing the recent practice of Big Data analysis,to address various problems in this sector.To execute this review,this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain.Another goal of this review is to gain insight into state-of-the-art Big Data applications in agriculture and to use a structural approach to identify challenges to be addressed in this area.This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.展开更多
Feed formulation is essential in the dairy production chain from economic,nutritional,and environmental perspectives.Optimizing the feed formulation across those three domains–given uncertainty of input prices,input ...Feed formulation is essential in the dairy production chain from economic,nutritional,and environmental perspectives.Optimizing the feed formulation across those three domains–given uncertainty of input prices,input availability,and regional climatic conditions–is a challenge for those in the industry.The diet formulation method that is widely used by trading firms and feed production facilities employs a static linear programming(LP)approach.This approach does not allow for intertemporal feed formulations and switches between dietary feed commodities under feed availability conditions,which result in foregone economic gains for feed producers.The current study develops a multi-period LP feed model that uses historical data to capture ration switch opportunities between available feed resources for dairy cows and demonstrates the potential use of the method in different commodity feed availability situations.We apply 14 diet formulations,each covering 150 months,representing a total of 2100 diets.The diet formulation considers a specific milk production level for a“model cow”,alternative feed formulations available,and volatility in feed prices.The results demonstrate that there is an opportunity for efficiency gains in the dairy industry with respect to feed formulation.Based on dietary feed inclusion and price spreads,barley can be an important dairy feed grain which completely replaces wheat,corn,and sorghum at price spreads of less than 94%,less than 78%,and less than 67%,respectively.Grain-based feed scenarios represent the lowest nutrient variation while multiple meal feeds had the lowest costs.Furthermore,and on average,multiple meal feed scenarios provided 10%higher dietary crude protein contents compared to grain based feed scenarios(i.e.163 vs 179 g/kg DM formulated feed).Meanwhile,multiple meal feeding cost was 11%lower than that in the grain based feeding scenarios.Additionally,the use of multiple meals reduces alfalfa dietary inclusion by 7%on dry matter basis.Our analysis shows a strong reduction in feed cost associated with dietary crude protein reduction equivalent to 7.6 USD/tonne per 1%reduction in dietary crude protein level.The modeling approach allows for the interaction between feed components over time taking into consideration volatile global feed prices,thereby improving feed availability and feed formulation.Overall,the model provides a decision making tool to improve the use of feed resources in the dairy sector.展开更多
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...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.展开更多
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 re...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.展开更多
The advent of remote livestock monitoring systems provides numerous possibilities for improving on-farm productivity,efficiency,and welfare.One potential application for these systems is for the detection of calving e...The advent of remote livestock monitoring systems provides numerous possibilities for improving on-farm productivity,efficiency,and welfare.One potential application for these systems is for the detection of calving events.This study describes the integration of data from multiple sensor sources,including accelerometers,global navigation satellite systems(GNsS),an accelerometer-derived rumination algorithm,a walk-over-weigh unit,and a weather station for parturition detection using a support vector machine approach.The best performing model utilised data from GNsS,the ruminating algorithm,and weather stations to achieve 98.6%accuracy,with 88.5%sensitivity and 100%specificity.The topranking features of this model were primarily GNSS derived.This study provides an overview as to how various sensor systems could be integrated on-farm to maximise calving detection for improved production and welfare outcomes.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
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This research paper defines the theoretical foundations and computational implementation of a non-conventional modeling and simulation methodology,inspired by the needs of problem solving for biological,agricultural,aquacultural and environmental systems.The challenging practical problem is to develop a framework for automatic generation of causally right and balance-based,unified models that can also be applied for the effective coupling amongst the various(sophisticated field-specific,sensor data processing-based,upper level optimization-driven,etc.)models.The scientific problem addressed in this innovation is to develop Programmable Process Structures(PPS)by combining functional basis of systems theory,structural approach of net theory and computational principles of agent based modeling.PPS offers a novel framework for the automatic generation of easily extensible and connectible,unified models for the underlying complex systems.PPS models can be generated from one state and one transition meta-prototypes and from the transition oriented description of process structure.The models consist of unified state and transition elements.The local program containing prototype elements,derived also from the meta-prototypes,are responsible for the case-specific calculations.The integrity and consistency of PPS architecture are based on the meta-prototypes,prepared to distinguish between the conservation-laws-based measures and the signals.The simulation is based on data flows amongst the state and transition elements,as well as on the unification based data transfer between these elements and their calculating prototypes.This architecture and its AI language-based(Prolog)implementation support the integration of various field-and task-specific models,conveniently.The better understanding is helped by a simple example.The capabilities of the recently consolidated general methodology are discussed on the basis of some preliminary applications,focusing on the recently studied agricultural and aquacultural cases.
文摘IoT based agriculture(Ag-IoT)is an emerging communication technology that is widely adopted by agricultural entrepreneurs and farmers to perform agricultural agro-chores in the farm to improve productivity,for better monitoring,and to reduce labor costs.However,the use of the Internet in Ag-IoT facilitates real-time functionality in an agriculture system,it can increase the risk of security breaches and cyber attacks that would cause the Ag-IoT system to malfunction and can affect its productivity.Ag-IoT is overlooked in cyber security parameters,which can have severe impacts on its trustworthiness and adoption by agricultural communities.To address this gap,this article presents a systematic study of the literature published between 2001 and 2023 that discusses advances in Ag-IoT technology.The subjects included in the study on Ag-IoT are emerging applications,different IoT architectures,suspected cyber attacks and cyber crimes,and challenges in incident response and digital forensics.The findings of this study encourage the reader to explore future potential research avenues related to the security risks and challenges of Ag-IoT,as well as the readiness for incident response and forensic investigation in the smart agricultural sector.The main conclusion of this study is that security must be ensured in Ag-IoT environments to offer uninterrupted services and also there is a need for forensic readiness for effective investigation in the event of unanticipated security incidents.
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the Research and Community Services Institution,IPB University(project no.10225/IT3.S3/KS/2020)。
文摘Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and downstream.This content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product profitability.Based on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm fruit.First,350 palm fruits with Tenera varieties(Elaeis guineensis Jacq.var.tenera),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content extraction.Then,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil content.Finally,a robust EMD-ANN model is generated by optimizing the lowest possible errors.Based on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±0.208.These results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally friendly.Therefore,the proposed technique has a promising potential to be applied in the oil palm industry.Measurements like this will lead to the effective and efficient management of oil palm production.
基金This research was financially supported by Research on Industrialization of Real-time Measurement and Control Technology for Efficient Fish-vegetable Symbiosis System(Project No.:KJ2019CX099).
文摘This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the information transmission layer and the sys-tem architecture.The system has employed multiple sensors terminal to real-time acqui-sition,including air and water temperatures,dissolved oxygen etc.LoRa protocol is suitable for sending small data and the 4G was employed to collect data and send to the cloud plat-form.Java is used to develop background applications,to access cloud platforms and local data processing.Based on the collection and processing of environmental data and cloud service platform,the mobile application program client and remote login desktop have been developed.It has been implemented and tested in Tongzhou,Beijing for 3 months in 2020.The results showed the proposed monitoring system stability for overall operation and accuracy data transmission,which can support the actual hydroponics and aquacul-ture production management.After analysis of monitoring data collected from the devel-oped monitoring system,indoor air and water temperature have the obvious correlation with atmospheric pressure(0.7 and 0.9)and outdoor temperature(1.0 and 0.9),respectively.
文摘Pine(Pinus ssp.)needle biomass(PNB)was pyrolyzed at 400℃ for 3 h and then subjected to hydrothermal treatment at the same temperature for 10 min,with and without the addition of potassium chloride(KCl).The suspensions of the materials treated hydrothermally were submitted to ultrasound for 5,10,20,30 and 60 min.Diffuse reflectance UV-Vis(DRUV)spectroscopy results for the materials with variations in sonication times were obtained and the band gap energy(E)was calculated.A culture medium containing Saccharomyces cerevisiae was monitored during 30 min of exposure to different materials for the calculation of the 10%(IC10),30%(IC30)and 50%(IC50)inhibitory concentrations.Of the samples that underwent ultrasonic treatment,the material pyrolyzed at 400℃ without the addition of potassium ions(PNB4003H60)presented the greatest inhibition of 10% of the Saccharomyces cerevisiae cultures.Of the materials without the addition of potassium,the material pyrolyzed and sonicated for 10 min(PNB4003H10)showed the best characteristics for use as a support for Saccharomyces cerevisiae organisms.
文摘The quality of fruits can be reduced due to some damages and impacts which occur during harvesting.One of the most important mechanical damages that can reduce the quality of ripe fruit quality is abrasion damage.This study focused on the effect of dynamic loading based on customary harvesting method on mulberry fruit properties.To this end,different maturity stage and storage regimes were considered.Color quality parameters,firmness,total soluble solid(TSS),total anthocyanins content(TAC)and abrasion area were the measured factors.The results revealed that none of the surveyed factors were stable during the experiment.The lightness(L^*),redness(a^*),yellowness(b^*),C^* value,firmness,TSS and TAC of immature and mature mulberry decreased during storage.The value of a^*,b^* and C^* increased as dropping height increased.However,L*value,firmness,TSS and TAC of mulberry fruit decreased at both maturity stages(immature and mature mulberry).Moreover,abrasion area increased at immature and mature mulberries by increasing the dropping height and storage time.
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.
基金supported by Center for Engineering Research and Development,Government of Kerala,India,vide Grant No.KTU/Research/2743/2017.
文摘The fungal diseases in banana cause major yield losses for millions of farmers around the globe.Early detection of these diseases helps the farmers to devise successful management strategies.The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease.This paper demonstrates a methodology for classification of three important foliar diseases in banana,using local texture features.The disease affected regions are identified using image enhancement and color segmentation.Segmented images are converted to transform domain using three image transforms(DWT,DTCWT and Ranklet transform).Feature vector is extracted from transform domain images using LBP and its variants(ELBP,MeanELBP and MedianELBP).These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure.Experimental results showed best classification performance for ELBP features extracted from DTCWT domain(accuracy 95.4%,precision 93.2%,sensitivity 93.0%,Fscore 93.0%and specificity 96.4%).Compared with traditional methods of feature extraction,this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.
文摘Agriculture is the backbone of the Indian Economy.However,statistics show that the rural population and arable land per person is declining.This is an ominous development for a country with a population of more than one billion,with over sixty-six percent living in rural areas.This paper aims to review current studies and research in agriculture,employing the recent practice of Big Data analysis,to address various problems in this sector.To execute this review,this article outline a framework for Big Data analytics in agriculture and present ways in which they can be applied to solve problems in the present agricultural domain.Another goal of this review is to gain insight into state-of-the-art Big Data applications in agriculture and to use a structural approach to identify challenges to be addressed in this area.This review of Big Data applications in the agricultural sector has also revealed several collection and analytics tools that may have implications for the power relationships between farmers and large corporations.
文摘Feed formulation is essential in the dairy production chain from economic,nutritional,and environmental perspectives.Optimizing the feed formulation across those three domains–given uncertainty of input prices,input availability,and regional climatic conditions–is a challenge for those in the industry.The diet formulation method that is widely used by trading firms and feed production facilities employs a static linear programming(LP)approach.This approach does not allow for intertemporal feed formulations and switches between dietary feed commodities under feed availability conditions,which result in foregone economic gains for feed producers.The current study develops a multi-period LP feed model that uses historical data to capture ration switch opportunities between available feed resources for dairy cows and demonstrates the potential use of the method in different commodity feed availability situations.We apply 14 diet formulations,each covering 150 months,representing a total of 2100 diets.The diet formulation considers a specific milk production level for a“model cow”,alternative feed formulations available,and volatility in feed prices.The results demonstrate that there is an opportunity for efficiency gains in the dairy industry with respect to feed formulation.Based on dietary feed inclusion and price spreads,barley can be an important dairy feed grain which completely replaces wheat,corn,and sorghum at price spreads of less than 94%,less than 78%,and less than 67%,respectively.Grain-based feed scenarios represent the lowest nutrient variation while multiple meal feeds had the lowest costs.Furthermore,and on average,multiple meal feed scenarios provided 10%higher dietary crude protein contents compared to grain based feed scenarios(i.e.163 vs 179 g/kg DM formulated feed).Meanwhile,multiple meal feeding cost was 11%lower than that in the grain based feeding scenarios.Additionally,the use of multiple meals reduces alfalfa dietary inclusion by 7%on dry matter basis.Our analysis shows a strong reduction in feed cost associated with dietary crude protein reduction equivalent to 7.6 USD/tonne per 1%reduction in dietary crude protein level.The modeling approach allows for the interaction between feed components over time taking into consideration volatile global feed prices,thereby improving feed availability and feed formulation.Overall,the model provides a decision making tool to improve the use of feed resources in the dairy sector.
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.
基金The authors acknowledge the support of the University of South Australia–Australia for granting of a post graduate scholarship to Mustafa Ucgulthe Australian Grains Research and Development Corporation(GRDC)project USA00005 for funding the computer and software.
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.
基金the Central Queensland University Animal Ethics Committee(application ID:0000021144).
文摘The advent of remote livestock monitoring systems provides numerous possibilities for improving on-farm productivity,efficiency,and welfare.One potential application for these systems is for the detection of calving events.This study describes the integration of data from multiple sensor sources,including accelerometers,global navigation satellite systems(GNsS),an accelerometer-derived rumination algorithm,a walk-over-weigh unit,and a weather station for parturition detection using a support vector machine approach.The best performing model utilised data from GNsS,the ruminating algorithm,and weather stations to achieve 98.6%accuracy,with 88.5%sensitivity and 100%specificity.The topranking features of this model were primarily GNSS derived.This study provides an overview as to how various sensor systems could be integrated on-farm to maximise calving detection for improved production and welfare outcomes.
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.
