Object classification in high-density 3D point clouds with applications in precision farming is a very challenging area due to high intra-class variances and high degrees of occlusions and overlaps due to self-similar...Object classification in high-density 3D point clouds with applications in precision farming is a very challenging area due to high intra-class variances and high degrees of occlusions and overlaps due to self-similarities and densely packed plant organs, especially in ripe growing stages. Due to these application specific challenges, this contribution gives an experimental evaluation of the performance of local shape descriptors (namely Point-Feature Histogram (PFH), Fast-Point-Feature Histogram (FPFH), Signature of Histograms of Orientations (SHOT), Rotational Projection Statistics (RoPS) and Spin Images) in the classification of 3D points into different types of plant organs. We achieve very good results on four representative scans of a leave, a grape bunch, a grape branch and a flower of between 94 and 99% accuracy in the case of supervised classification with an SVM and between 88 and 96% accuracy using a k-means clustering approach. Additionally, different distance measures and the influence of the number of cluster centres are examined.展开更多
Precision farming(PF)allows the efficient use of resources such as water,and fertilizers,among others;as well,it helps to analyze the behavior of insect pests,in order to increase production and decrease the cost of c...Precision farming(PF)allows the efficient use of resources such as water,and fertilizers,among others;as well,it helps to analyze the behavior of insect pests,in order to increase production and decrease the cost of crop management.This paper introduces an innovative approach to integrated cotton management,involving the implementation of an Autonomous Cycle of Data Analysis Tasks(ACODAT).The proposed autonomous cycle is composed of a classification task of the population of pests(boll weevil)(based on eXtreme Gradient Boosting-XGBoost),a diagnosis-prediction task of cotton yield(based on a fuzzy system),and a prescription task of strategies for the adequate management of the crop(based on genetic algorithms).The proposed system can evaluate several variables according to the conditions of the crop,and recommend the best strategy for increasing the cotton yield.In particular,the classification task has an accuracy of 88%,the diagnosis/prediction task obtained an accuracy of 98%,and the genetic algorithm recommends the best strategy for the context analyzed.Focused on integrated cotton management,our system offers flexibility and adaptability,which facilitates the incorporation of new tasks.展开更多
Precision management of animals using technology is one innovation in agriculture that has the potential to revolutionizewhole livestock industries including the poultry sector. Limited research in precision livestock...Precision management of animals using technology is one innovation in agriculture that has the potential to revolutionizewhole livestock industries including the poultry sector. Limited research in precision livestock farming (PLF) in the poultry productionhas been so far conducted and most of them are conducted within the past 5-10 years. The PLF collects real-time data from individual orgroup of animals or birds using sensor technology, and involves the multidisciplinary team approach to give it a reality. Poultry scientistsplay a central role in executing poultry PLF with collaboration from agri-engineers and computer scientists for the type of measurementsto be made on biological or environmental variables. A real-time collection of environmental, behavioral and health data from birdgrow-out facilities can be a strong tool for developing daily action plans for poultry management. Unlike other livestock farming, theattributes of poultry rearing such as a closed housing system and vertically integrated industry provides a greater opportunity for poultrysector to adopt technology-based farming for enhanced production output.展开更多
Precision Livestock Farming(PLF)emerges as a promising solution for revolutionising farming by enabling real-time automated monitoring of animals through smart technologies.PLF provides farmers with precise data to en...Precision Livestock Farming(PLF)emerges as a promising solution for revolutionising farming by enabling real-time automated monitoring of animals through smart technologies.PLF provides farmers with precise data to enhance farm management,increasing productivity and profitability.For instance,it allows for non-intrusive health assessments,contributing to maintaining a healthy herd while reducing stress associated with handling.In the poultry sector,image analysis can be utilised to monitor and analyse the behaviour of each hen in real time.Researchers have recently used machine learning algorithms to monitor the behaviour,health,and positioning of hens through computer vision techniques.Convolutional neural networks,a type of deep learning algorithm,have been utilised for image analysis to identify and categorise various hen behaviours and track specific activities like feeding and drinking.This research presents an automated system for analysing laying hen movement using video footage from surveillance cameras.With a customised implementation of object tracking,the system can efficiently process hundreds of hours of videos while maintaining high measurement precision.Its modular implementation adapts well to optimally exploit the GPU computing capabilities of the hardware platform it is running on.The use of this system is beneficial for both real-time monitoring and post-processing,contributing to improved monitoring capabilities in precision livestock farming.展开更多
Precise information about the spatial variability of soil properties is essential in developing site-specific soil management, such as variable rate application of fertilizers. In this study the sampling grid of 100 m...Precise information about the spatial variability of soil properties is essential in developing site-specific soil management, such as variable rate application of fertilizers. In this study the sampling grid of 100 m × 100 m was established to collect 1 703 soil samples at the depth of 0-20 cm, and examine spatial patterns including 13 soil chemical properties （pH, OM, NH4^＋, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn） in a 1 760 ha rice field in Haifeng farm, China, from 6th to 22nd of April, 2006, before fertilizer application and planting. Soil analysis was performed by ASI （Agro Services International） and data were analyzed both statistically and geostatistically. Results showed that the contents of soil OM, NH4^＋, and Zn in Haifeng farm were very low for rice production and those of others were enough to meet the need for rice cultivation. The spatial distribution model and spatial dependence level for 13 soil chemical properties varied in the field. Soil Mg and B showed strong spatial variability on both descriptive statistics and geostatistics, and other properties showed moderate spatial variability. The maximum ranges for K, Ca, Mg, S, Cu and Mn were all - 3 990.6 m and the minimum ranges for soil pH, OM, NH4^＋, P, Fe, and Zn ranged from 516.7 to 1 166.2 m. Clear patchy distribution of N, P, K, Mg, S, B, Mn, and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.展开更多
Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic ...Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.展开更多
Spatial patterns of soil fertility parameters and other extrinsic factors need to be identified to develop farming practices that match agricultural inputs with local crop needs. Little is known about the spatial stru...Spatial patterns of soil fertility parameters and other extrinsic factors need to be identified to develop farming practices that match agricultural inputs with local crop needs. Little is known about the spatial structure of nutrition in Iran. The present study was conducted in a 132-ha field located in central Iran. Soil samples were collected at 0-30 cm depth and were then analyzed for total nitrogen （N）, available phosphorus （P）, available potassium （K）, available copper （Cu）, available zinc （Zn）, available iron （Fe） and available manganese （Mn）. The results showed that the contents of soil organic matter, Cu and Zn in Marvdasht＇s farms were low. The spatial distribution model and spatial dependence level for soil chemical properties varied in the field. N, K, carbonate calcium equivalent （CaCO3） and electrical conductivity （EC） data indicated the existence of moderate spatial dependence. The variograms for other variables revealed stronger spatial structure. The results showed a longer range value for available P （480 m）, followed by total N （429 m）. The value of other chemical properties values showed a shorter range （128 to 174 m）. Clear patchy distribution of N, P, K, Fe, Mn, Cu and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.展开更多
Excessive use of nitrogen fertilizer in China and its adverse effects on agricultural production have been a national and global concern.In addition to massive public initiatives to promote sustainable farm practices,...Excessive use of nitrogen fertilizer in China and its adverse effects on agricultural production have been a national and global concern.In addition to massive public initiatives to promote sustainable farm practices,grass-rooted innovations are emerging in the niche,many of which take the forms of information and communication technologies(ICT)and digital services.This study examines the effects of ICT-based extension services provided by an entrepreneurial startup on adopting sustainable farming practices.We found no significant reduction in N-fertilizer use for wheat production.But the ICT-based services promoted farmers to adapt N-fertilizer use towards site-specific management.The business model of the entrepreneurial venture faces great challenges in becoming participatory and financially sustainable.展开更多
One of the most critical objectives of precision farming is to assess the germination quality of seeds.Modern models contribute to thisfield primarily through the use of artificial intelligence techniques such as machin...One of the most critical objectives of precision farming is to assess the germination quality of seeds.Modern models contribute to thisfield primarily through the use of artificial intelligence techniques such as machine learning,which present difficulties in feature extraction and optimization,which are critical factors in predicting accuracy with few false alarms,and another significant dif-ficulty is assessing germination quality.Additionally,the majority of these contri-butions make use of benchmark classification methods that are either inept or too complex to train with the supplied features.This manuscript addressed these issues by introducing a novel ensemble classification strategy dubbed“Assessing Germination Quality of Seed Samples(AGQSS)by Adaptive Boosting Ensemble Classification”that learns from quantitative phase features as well as universal features in greyscale spectroscopic images.The experimental inquiry illustrates the significance of the proposed model,which outperformed the currently avail-able models when performance analysis was performed.展开更多
The North-East China is nation commercial grain base of China.It provides important grain supply for other areas of the country every year.The nation and modern farmers are looking for advanced technological solutions...The North-East China is nation commercial grain base of China.It provides important grain supply for other areas of the country every year.The nation and modern farmers are looking for advanced technological solutions to increase production and preserve environment.Considering of this aim,this paper introduce a new planning that using 3S technology to develop precision farming,explaining its technology frame,operation steps and advantages.On the other hand,this paper also introduce the concept of precision farming and discusses the role of 3S technology as a data collection,management and analysis tool.展开更多
This paper summarized the application of computer technology in fruit science, including crop modelling, expert system, decision support system (DSS), computer vision (CV), the Internet, 3 “S”technology, etc. The ex...This paper summarized the application of computer technology in fruit science, including crop modelling, expert system, decision support system (DSS), computer vision (CV), the Internet, 3 “S”technology, etc. The existing problems and prospects are also discussed in the paper.展开更多
The increasing global population has led to a higher demand for food production, while a decrease in rural labor and a rise in production costs present complex challenges for the food industry. Smart agriculture is a ...The increasing global population has led to a higher demand for food production, while a decrease in rural labor and a rise in production costs present complex challenges for the food industry. Smart agriculture is a farm management concept that considers the deployment of Internet of Things (IoT) to address current food production challenges. In this regard, the agricultural sector is becoming increasingly data-focused, and requires data and technologies that are more precise, advanced, and cutting-edge than in the past. IoT enables agriculture to become data-driven, resulting in timely and more cost-effective farm intervention while reducing environmental impact. This review provides an analytical survey of the current and potential applications of IoT in smart agriculture to overcome challenges posed by spatio-temporal variability under varying environments and task diversity. This review also discusses the challenges that may arise from IoT deployment and presents an overview of the existing applications and those that may be developed in the future.展开更多
In order to be able to produce safe,uniform,cheap,environmentally-and welfare-friendly food products and market these products in an increasingly complex international agricultural market,livestock producers must have...In order to be able to produce safe,uniform,cheap,environmentally-and welfare-friendly food products and market these products in an increasingly complex international agricultural market,livestock producers must have access to timely production related information.Especially the information related to feeding/nutritional issues is important,as feeding related costs are always significant part of variables costs for all types of livestock production.Therefore,automating the collection,analysis and use of production related information on livestock farms will be essential for improving livestock productivity in the future.Electronically-controlled livestock production systems with an information and communication technology(ICT)focus are required to ensure that information is collected in a cost effective and timely manner and readily acted upon on farms.New electronic and ICT related technologies introduced on farms as part of Precision Livestock Farming(PLF)systems will facilitate livestock management methods that are more responsive to market signals.The PLF technologies encompass methods for electronically measuring the critical components of the production system that indicate the efficiency of resource use,interpreting the information captured and controlling processes to ensure optimum efficiency of both resource use and livestock productivity.These envisaged real-time monitoring and control systems could dramatically improve production efficiency of livestock enterprises.However,further research and development is required,as some of the components of PLF systems are in different stages of development.In addition,an overall strategy for the adoption and commercial exploitation of PLF systems needs to be developed in collaboration with private companies.This article outlines the potential role PLF can play in ensuring that the best possible management processes are implemented on farms to improve farm profitability,quality of products,welfare of livestock and sustainability of the farm environment,especially as it related to intensive livestock species.展开更多
The precision livestock farming(PLF)has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production.Among the PLF technique...The precision livestock farming(PLF)has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production.Among the PLF techniques,the personalised management of each individual animal based on sensors systems,represents a viable option.It is worth noting that the implementation of an effective PLF approach can be still expensive,especially for small and medium-sized farms;for this reason,to guarantee the sustainability of a customized livestock management system and encourage its use,plug and play and cost-effective systems are needed.Within this context,we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera.By leveraging the current state-of-the-art methods for real-time object detection,(i.e.,YOLOv3)cattle's face areas,we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker.The cow IDs are read by an Optical Character Recognition(OCR)algorithm for which,an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs.Thanks to the detection of the tag position,the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed.Activity times for the areas are outputted as cattle activity recognition results.Evaluation results demonstrate the effectiveness of our proposed method,showing a mAP@0.50 of 89%.展开更多
A tool was developed to assist the cooling systems designer in designing and installing the microsprinklers and fan cooling system. The tool was developed by integrating a mathematical model into an electronic spark m...A tool was developed to assist the cooling systems designer in designing and installing the microsprinklers and fan cooling system. The tool was developed by integrating a mathematical model into an electronic spark map in order to use the mathematical model practically. The mathematical model was developed using the designs, parameters, variables, and constant values of the microsprinklers and fans cooling system. Subsequently, an electronic spark map （decision tree） was developed, and then the mathematical model was integrated into the electronic spark map. Afterwards, C# （C Sharp） programming language was used to develop a computer system via the electronic spark map, and to make the user interface. The developed computer system assists the designer in making decisions to specify and to calculate the required discharge of cooling system pump, length and diameter of cooling system pipelines, number of cooling fans, and number of microsprinklers. Moreover, this tool calculates the capital investment and the fixed, variable, and total costs of the cooling system. However, the mathematical model of the spark map requires some input data such as： pressure and discharge of microsprinklers, and some other engineering parameters. Data of 4 cooling systems were used to carry out the model validation. The differences between actual and calculated values were determined, and the standard deviations were calculated. The coefficients of variation were between 2.25% and 4.13%.展开更多
Background Various blood metabolites are known to be useful indicators of health status in dairy cattle,but their routine assessment is time-consuming,expensive,and stressful for the cows at the herd level.Thus,we eva...Background Various blood metabolites are known to be useful indicators of health status in dairy cattle,but their routine assessment is time-consuming,expensive,and stressful for the cows at the herd level.Thus,we evaluated the effectiveness of combining in-line near infrared(NIR)milk spectra with on-farm(days in milk[DIM]and parity)and genetic markers for predicting blood metabolites in Holstein cattle.Data were obtained from 388 Holstein cows from a farm with an AfiLab system.NIR spectra,on-farm information,and single nucleotide polymorphisms(SNP)markers were blended to develop calibration equations for blood metabolites using the elastic net(ENet)approach,considering 3 mod els:(1)Model 1(M1)including only NIR information,(2)Model 2(M2)with both NIR and on-farm information,and(3)Model 3(M3)combining NIR,on-farm and genomic information.Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study(GWAS)results.Results Results indicate that M2 improved the predictive ability by an average of 19%for energy-related metabolites(glucose,cholesterol,NEFA,B H B,urea,and c reatinin e),20%for liver functio n/hepatic damage,7%for inflammation/innate immunity.24%for oxidative stress metabolites,and 23%for minerals compared to M1,Meanwhile,M3 further enhanced the predictive ability by 34%for energy-related metabolites,32%for liver function/hepatic damage,22%for inflammation/innate immunity,42.1%for oxidative stress metabolites,and 41%for mineralse compared to M1.We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of>2.0by 5%for energy-related metabolites,9%for liver function/hepatic damage,8%for inflammation/innate immunity,22%for oxidative stress metabolites,and 9%for minerals.Slight redu ctions were observed fo r phosphorus(2%),ferricreducing antioxidant power(1%),and glucose(3%).Furthermore,it was found that prediction accuracies are influenced by using more restrictive thresholds(-log_(10)^(P-value)>2.5 and 3.0),with a lower increase in the predictive ability.Conclusion Our results highlighted the potential of combining several sources of information,such as genetic markers,on-farm information,and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle,representing an effective strategy for large-scale in-line health monitoring in commercial herds.展开更多
Nitrous oxide(N2O)emissions make up a significant part of agricultural greenhouse gas emissions.There is an urgent need to identify new approaches to the mitigation of these emissions with emerging technology.In this ...Nitrous oxide(N2O)emissions make up a significant part of agricultural greenhouse gas emissions.There is an urgent need to identify new approaches to the mitigation of these emissions with emerging technology.In this short review four approaches to precision managements of agricultural systems are described based on examples of work being undertaken in the UK and New Zealand.They offer the opportunity for N2 O mitigation without any reduction in productivity.These approaches depend upon new sensor technology,modeling and spatial information with which to make management decisions and interventions that can both improve agricultural productivity and environmental protection.展开更多
Cattle behavioral monitoring is an integral component of the modern infrastructure of the livestock industry.Ensuring cattle well-being requires precise observation,typically using wearable devices or surveillance cam...Cattle behavioral monitoring is an integral component of the modern infrastructure of the livestock industry.Ensuring cattle well-being requires precise observation,typically using wearable devices or surveillance cameras.Integrating deep learning into these systems enhances the monitoring of cattle behavior.However,challenges remain,such as occlusions,pose variations,and limited camera viewpoints,which hinder accurate detection and location mapping of individual cattle.To address these challenges,this paper proposes a multi-viewpoint surveillance system for indoor cattle barns,using footage from four cameras and deep learning-based models including action detection and pose estimation for behavior monitoring.The system accurately detects hierarchical behaviors across camera viewpoints.These results are fed into a Bird's Eye View(BEV)algorithm,producing precise cattle position maps in the barn.Despite complexities like overlapping and non-overlapping camera regions,our system,implemented on a real farm,ensures accurate cattle detection and BEV-based projections in real-time.Detailed experiments validate the system's efficiency,offering an end-to-end methodology for accurate behavior detection and location mapping of individual cattle using multi-camera data.展开更多
The integration of Artificial Intelligence(AI)into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care.This comprehensive review explores the development,...The integration of Artificial Intelligence(AI)into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care.This comprehensive review explores the development,implementation,and challenges associated with AI-powered biometric facial identification in dairy agriculture.It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows,thereby facilitating thorough tracking of their health,behaviors,and productivity levels.Derived from facial recognition technologies originally designed for humans,this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations.The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks(CNNs)and deep learning frameworks signifies a transition toward data-driven agriculture.This analysis addresses notable challenges such as environmental variability,data collection difficulties,ethical considerations,and technological limitations.Furthermore,it compares various AI frameworks,highlighting their unique advantages and suitability in the dairy farming context.Despite these obstacles,facial recognition technology holds promise for enhancing farm efficiency,improving animal welfare,and promoting sustainable practices,underscoring the need for ongoing research and innovation.We advocate for future investigations focused on enhancing adaptability to diverse environments,ensuring ethical AI deployment,fostering compatibility across different breeds,and integrating with complementary agricultural technologies.Ultimately,this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices.展开更多
This research introduces a new inclination correction method with increased accuracy applied to the guidance system of an agricultural vehicle.The method considers the geometry of a robot tractor and uses an Inertial ...This research introduces a new inclination correction method with increased accuracy applied to the guidance system of an agricultural vehicle.The method considers the geometry of a robot tractor and uses an Inertial Measurement Unit(IMU)to correct the lateral error of the RTK-GPS antenna measurements raised by the tractor's inclinations.A parameters optimization experiment and an automatic guidance experiment under real working conditions were used to compare the accuracy of a traditional correction method with the new correction method,by calculating the RMSE of the lateral error and the error reduction percentage.An additional tuned correction method was found by using a simple analytical method to find the optimal variables that reduced the lateral error to a minimum.The results indicate that the new correction method and the tuned correction method display a significant error reduction percentage compared to the traditional correction method.The methods could correct the GPS lateral error caused by the roll inclinations in real-time.The resulting lateral deviation caused by the tractor's inclinations could be reduced up to 23%for typical travelling speeds.展开更多
基金the project“Automated Evaluation and Comparison of Grapevine Genotypes by means of Grape Cluster Architecture”which is supported by the Deutsche Forschungsgemeinschaft(funding code:STE 806/2-1).
文摘Object classification in high-density 3D point clouds with applications in precision farming is a very challenging area due to high intra-class variances and high degrees of occlusions and overlaps due to self-similarities and densely packed plant organs, especially in ripe growing stages. Due to these application specific challenges, this contribution gives an experimental evaluation of the performance of local shape descriptors (namely Point-Feature Histogram (PFH), Fast-Point-Feature Histogram (FPFH), Signature of Histograms of Orientations (SHOT), Rotational Projection Statistics (RoPS) and Spin Images) in the classification of 3D points into different types of plant organs. We achieve very good results on four representative scans of a leave, a grape bunch, a grape branch and a flower of between 94 and 99% accuracy in the case of supervised classification with an SVM and between 88 and 96% accuracy using a k-means clustering approach. Additionally, different distance measures and the influence of the number of cluster centres are examined.
基金supported by the Colombian Science,Technology,and Innovation Fund(FCTeI)of the General Royalty System(SGR)Universidad EAFITand Universidad de Córdoba.
文摘Precision farming(PF)allows the efficient use of resources such as water,and fertilizers,among others;as well,it helps to analyze the behavior of insect pests,in order to increase production and decrease the cost of crop management.This paper introduces an innovative approach to integrated cotton management,involving the implementation of an Autonomous Cycle of Data Analysis Tasks(ACODAT).The proposed autonomous cycle is composed of a classification task of the population of pests(boll weevil)(based on eXtreme Gradient Boosting-XGBoost),a diagnosis-prediction task of cotton yield(based on a fuzzy system),and a prescription task of strategies for the adequate management of the crop(based on genetic algorithms).The proposed system can evaluate several variables according to the conditions of the crop,and recommend the best strategy for increasing the cotton yield.In particular,the classification task has an accuracy of 88%,the diagnosis/prediction task obtained an accuracy of 98%,and the genetic algorithm recommends the best strategy for the context analyzed.Focused on integrated cotton management,our system offers flexibility and adaptability,which facilitates the incorporation of new tasks.
文摘Precision management of animals using technology is one innovation in agriculture that has the potential to revolutionizewhole livestock industries including the poultry sector. Limited research in precision livestock farming (PLF) in the poultry productionhas been so far conducted and most of them are conducted within the past 5-10 years. The PLF collects real-time data from individual orgroup of animals or birds using sensor technology, and involves the multidisciplinary team approach to give it a reality. Poultry scientistsplay a central role in executing poultry PLF with collaboration from agri-engineers and computer scientists for the type of measurementsto be made on biological or environmental variables. A real-time collection of environmental, behavioral and health data from birdgrow-out facilities can be a strong tool for developing daily action plans for poultry management. Unlike other livestock farming, theattributes of poultry rearing such as a closed housing system and vertically integrated industry provides a greater opportunity for poultrysector to adopt technology-based farming for enhanced production output.
基金National Research Centre and received funding from the European Union Next-GenerationEU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)—MISSIONE 4 COMPONENTE 2,INVESTIMENTO 1.4—D.D.103217/06/2022,CN00000022).
文摘Precision Livestock Farming(PLF)emerges as a promising solution for revolutionising farming by enabling real-time automated monitoring of animals through smart technologies.PLF provides farmers with precise data to enhance farm management,increasing productivity and profitability.For instance,it allows for non-intrusive health assessments,contributing to maintaining a healthy herd while reducing stress associated with handling.In the poultry sector,image analysis can be utilised to monitor and analyse the behaviour of each hen in real time.Researchers have recently used machine learning algorithms to monitor the behaviour,health,and positioning of hens through computer vision techniques.Convolutional neural networks,a type of deep learning algorithm,have been utilised for image analysis to identify and categorise various hen behaviours and track specific activities like feeding and drinking.This research presents an automated system for analysing laying hen movement using video footage from surveillance cameras.With a customised implementation of object tracking,the system can efficiently process hundreds of hours of videos while maintaining high measurement precision.Its modular implementation adapts well to optimally exploit the GPU computing capabilities of the hardware platform it is running on.The use of this system is beneficial for both real-time monitoring and post-processing,contributing to improved monitoring capabilities in precision livestock farming.
基金funded by thestarting project of scientific research for high-level tal-ents introduced by North China University of Water Conservancy and Electric Power (200723)Shang-hai Municipal Key Task Projects of Prospering Agri-culture by the Science and Technology Plan, China(NGZ 1-10)
文摘Precise information about the spatial variability of soil properties is essential in developing site-specific soil management, such as variable rate application of fertilizers. In this study the sampling grid of 100 m × 100 m was established to collect 1 703 soil samples at the depth of 0-20 cm, and examine spatial patterns including 13 soil chemical properties （pH, OM, NH4^＋, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn） in a 1 760 ha rice field in Haifeng farm, China, from 6th to 22nd of April, 2006, before fertilizer application and planting. Soil analysis was performed by ASI （Agro Services International） and data were analyzed both statistically and geostatistically. Results showed that the contents of soil OM, NH4^＋, and Zn in Haifeng farm were very low for rice production and those of others were enough to meet the need for rice cultivation. The spatial distribution model and spatial dependence level for 13 soil chemical properties varied in the field. Soil Mg and B showed strong spatial variability on both descriptive statistics and geostatistics, and other properties showed moderate spatial variability. The maximum ranges for K, Ca, Mg, S, Cu and Mn were all - 3 990.6 m and the minimum ranges for soil pH, OM, NH4^＋, P, Fe, and Zn ranged from 516.7 to 1 166.2 m. Clear patchy distribution of N, P, K, Mg, S, B, Mn, and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.
基金Project supported by the National Natural Science Foundation of China (No. 30270773), and the Teaching and Research Award Pro-gram for Outstanding Young Teachers in Higher Education Institu-tions & the Specialized Research Fund for the Doctoral Program o
文摘Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.
基金the Soil Science Lab in the Department of Soil Sciences, Ramin Universitysupported by funds from Ramin University
文摘Spatial patterns of soil fertility parameters and other extrinsic factors need to be identified to develop farming practices that match agricultural inputs with local crop needs. Little is known about the spatial structure of nutrition in Iran. The present study was conducted in a 132-ha field located in central Iran. Soil samples were collected at 0-30 cm depth and were then analyzed for total nitrogen （N）, available phosphorus （P）, available potassium （K）, available copper （Cu）, available zinc （Zn）, available iron （Fe） and available manganese （Mn）. The results showed that the contents of soil organic matter, Cu and Zn in Marvdasht＇s farms were low. The spatial distribution model and spatial dependence level for soil chemical properties varied in the field. N, K, carbonate calcium equivalent （CaCO3） and electrical conductivity （EC） data indicated the existence of moderate spatial dependence. The variograms for other variables revealed stronger spatial structure. The results showed a longer range value for available P （480 m）, followed by total N （429 m）. The value of other chemical properties values showed a shorter range （128 to 174 m）. Clear patchy distribution of N, P, K, Fe, Mn, Cu and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.
基金financial support from the National Natural Science Foundation of China (72003148)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAASASTIP–2016-AII)+1 种基金the Chinese Universities Scientific Fund (2452020072)the National Key Research and Development Program of China (2016YFD0201303)
文摘Excessive use of nitrogen fertilizer in China and its adverse effects on agricultural production have been a national and global concern.In addition to massive public initiatives to promote sustainable farm practices,grass-rooted innovations are emerging in the niche,many of which take the forms of information and communication technologies(ICT)and digital services.This study examines the effects of ICT-based extension services provided by an entrepreneurial startup on adopting sustainable farming practices.We found no significant reduction in N-fertilizer use for wheat production.But the ICT-based services promoted farmers to adapt N-fertilizer use towards site-specific management.The business model of the entrepreneurial venture faces great challenges in becoming participatory and financially sustainable.
文摘One of the most critical objectives of precision farming is to assess the germination quality of seeds.Modern models contribute to thisfield primarily through the use of artificial intelligence techniques such as machine learning,which present difficulties in feature extraction and optimization,which are critical factors in predicting accuracy with few false alarms,and another significant dif-ficulty is assessing germination quality.Additionally,the majority of these contri-butions make use of benchmark classification methods that are either inept or too complex to train with the supplied features.This manuscript addressed these issues by introducing a novel ensemble classification strategy dubbed“Assessing Germination Quality of Seed Samples(AGQSS)by Adaptive Boosting Ensemble Classification”that learns from quantitative phase features as well as universal features in greyscale spectroscopic images.The experimental inquiry illustrates the significance of the proposed model,which outperformed the currently avail-able models when performance analysis was performed.
文摘The North-East China is nation commercial grain base of China.It provides important grain supply for other areas of the country every year.The nation and modern farmers are looking for advanced technological solutions to increase production and preserve environment.Considering of this aim,this paper introduce a new planning that using 3S technology to develop precision farming,explaining its technology frame,operation steps and advantages.On the other hand,this paper also introduce the concept of precision farming and discusses the role of 3S technology as a data collection,management and analysis tool.
文摘This paper summarized the application of computer technology in fruit science, including crop modelling, expert system, decision support system (DSS), computer vision (CV), the Internet, 3 “S”technology, etc. The existing problems and prospects are also discussed in the paper.
文摘The increasing global population has led to a higher demand for food production, while a decrease in rural labor and a rise in production costs present complex challenges for the food industry. Smart agriculture is a farm management concept that considers the deployment of Internet of Things (IoT) to address current food production challenges. In this regard, the agricultural sector is becoming increasingly data-focused, and requires data and technologies that are more precise, advanced, and cutting-edge than in the past. IoT enables agriculture to become data-driven, resulting in timely and more cost-effective farm intervention while reducing environmental impact. This review provides an analytical survey of the current and potential applications of IoT in smart agriculture to overcome challenges posed by spatio-temporal variability under varying environments and task diversity. This review also discusses the challenges that may arise from IoT deployment and presents an overview of the existing applications and those that may be developed in the future.
文摘In order to be able to produce safe,uniform,cheap,environmentally-and welfare-friendly food products and market these products in an increasingly complex international agricultural market,livestock producers must have access to timely production related information.Especially the information related to feeding/nutritional issues is important,as feeding related costs are always significant part of variables costs for all types of livestock production.Therefore,automating the collection,analysis and use of production related information on livestock farms will be essential for improving livestock productivity in the future.Electronically-controlled livestock production systems with an information and communication technology(ICT)focus are required to ensure that information is collected in a cost effective and timely manner and readily acted upon on farms.New electronic and ICT related technologies introduced on farms as part of Precision Livestock Farming(PLF)systems will facilitate livestock management methods that are more responsive to market signals.The PLF technologies encompass methods for electronically measuring the critical components of the production system that indicate the efficiency of resource use,interpreting the information captured and controlling processes to ensure optimum efficiency of both resource use and livestock productivity.These envisaged real-time monitoring and control systems could dramatically improve production efficiency of livestock enterprises.However,further research and development is required,as some of the components of PLF systems are in different stages of development.In addition,an overall strategy for the adoption and commercial exploitation of PLF systems needs to be developed in collaboration with private companies.This article outlines the potential role PLF can play in ensuring that the best possible management processes are implemented on farms to improve farm profitability,quality of products,welfare of livestock and sustainability of the farm environment,especially as it related to intensive livestock species.
基金LOWeMEAT(LOW Emission MEAT),thanks to the decisive contribution from the Regional Rural Development Programmes(PSR),which are co-financed by the European fund for rural devel development(FEASR)-Bando Regione Veneto PSR 2014-2020 DGR 1203/2016misura16.1.
文摘The precision livestock farming(PLF)has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production.Among the PLF techniques,the personalised management of each individual animal based on sensors systems,represents a viable option.It is worth noting that the implementation of an effective PLF approach can be still expensive,especially for small and medium-sized farms;for this reason,to guarantee the sustainability of a customized livestock management system and encourage its use,plug and play and cost-effective systems are needed.Within this context,we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera.By leveraging the current state-of-the-art methods for real-time object detection,(i.e.,YOLOv3)cattle's face areas,we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker.The cow IDs are read by an Optical Character Recognition(OCR)algorithm for which,an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs.Thanks to the detection of the tag position,the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed.Activity times for the areas are outputted as cattle activity recognition results.Evaluation results demonstrate the effectiveness of our proposed method,showing a mAP@0.50 of 89%.
文摘A tool was developed to assist the cooling systems designer in designing and installing the microsprinklers and fan cooling system. The tool was developed by integrating a mathematical model into an electronic spark map in order to use the mathematical model practically. The mathematical model was developed using the designs, parameters, variables, and constant values of the microsprinklers and fans cooling system. Subsequently, an electronic spark map （decision tree） was developed, and then the mathematical model was integrated into the electronic spark map. Afterwards, C# （C Sharp） programming language was used to develop a computer system via the electronic spark map, and to make the user interface. The developed computer system assists the designer in making decisions to specify and to calculate the required discharge of cooling system pump, length and diameter of cooling system pipelines, number of cooling fans, and number of microsprinklers. Moreover, this tool calculates the capital investment and the fixed, variable, and total costs of the cooling system. However, the mathematical model of the spark map requires some input data such as： pressure and discharge of microsprinklers, and some other engineering parameters. Data of 4 cooling systems were used to carry out the model validation. The differences between actual and calculated values were determined, and the standard deviations were calculated. The coefficients of variation were between 2.25% and 4.13%.
基金funding provided by Universitàdegli Studi di Padovapart of the project PROH-DAIRY project(Development of precision livestock breeding tools toward One Health in Italian and Israeli dairy chains)funded by the Ministry of Foreign Affairs and International Cooperation(MAECI)within the Italy-Israel R&D Cooperation Program(Roma,Italy)the Agritech National Research Center and received funding from the European Union Next-GenerationEU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)-MISSIONE 4 COM-PONENTE 2,INVESTIMENTO 1.4-D.D.103217/06/2022,CN00000022)。
文摘Background Various blood metabolites are known to be useful indicators of health status in dairy cattle,but their routine assessment is time-consuming,expensive,and stressful for the cows at the herd level.Thus,we evaluated the effectiveness of combining in-line near infrared(NIR)milk spectra with on-farm(days in milk[DIM]and parity)and genetic markers for predicting blood metabolites in Holstein cattle.Data were obtained from 388 Holstein cows from a farm with an AfiLab system.NIR spectra,on-farm information,and single nucleotide polymorphisms(SNP)markers were blended to develop calibration equations for blood metabolites using the elastic net(ENet)approach,considering 3 mod els:(1)Model 1(M1)including only NIR information,(2)Model 2(M2)with both NIR and on-farm information,and(3)Model 3(M3)combining NIR,on-farm and genomic information.Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study(GWAS)results.Results Results indicate that M2 improved the predictive ability by an average of 19%for energy-related metabolites(glucose,cholesterol,NEFA,B H B,urea,and c reatinin e),20%for liver functio n/hepatic damage,7%for inflammation/innate immunity.24%for oxidative stress metabolites,and 23%for minerals compared to M1,Meanwhile,M3 further enhanced the predictive ability by 34%for energy-related metabolites,32%for liver function/hepatic damage,22%for inflammation/innate immunity,42.1%for oxidative stress metabolites,and 41%for mineralse compared to M1.We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of>2.0by 5%for energy-related metabolites,9%for liver function/hepatic damage,8%for inflammation/innate immunity,22%for oxidative stress metabolites,and 9%for minerals.Slight redu ctions were observed fo r phosphorus(2%),ferricreducing antioxidant power(1%),and glucose(3%).Furthermore,it was found that prediction accuracies are influenced by using more restrictive thresholds(-log_(10)^(P-value)>2.5 and 3.0),with a lower increase in the predictive ability.Conclusion Our results highlighted the potential of combining several sources of information,such as genetic markers,on-farm information,and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle,representing an effective strategy for large-scale in-line health monitoring in commercial herds.
基金the Scottish Government Strategic Research ProgrammeN-Circle project(BB/N013484/1)+1 种基金Teagasc in Irelandfunded by the New Zealand Government through the Global Research Alliance。
文摘Nitrous oxide(N2O)emissions make up a significant part of agricultural greenhouse gas emissions.There is an urgent need to identify new approaches to the mitigation of these emissions with emerging technology.In this short review four approaches to precision managements of agricultural systems are described based on examples of work being undertaken in the UK and New Zealand.They offer the opportunity for N2 O mitigation without any reduction in productivity.These approaches depend upon new sensor technology,modeling and spatial information with which to make management decisions and interventions that can both improve agricultural productivity and environmental protection.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(No.RS-2019-NR040079)the NRF grants funded by the Korea Government(MSIT)(2020R1A2C2013060)and(RS-2024-00392406).
文摘Cattle behavioral monitoring is an integral component of the modern infrastructure of the livestock industry.Ensuring cattle well-being requires precise observation,typically using wearable devices or surveillance cameras.Integrating deep learning into these systems enhances the monitoring of cattle behavior.However,challenges remain,such as occlusions,pose variations,and limited camera viewpoints,which hinder accurate detection and location mapping of individual cattle.To address these challenges,this paper proposes a multi-viewpoint surveillance system for indoor cattle barns,using footage from four cameras and deep learning-based models including action detection and pose estimation for behavior monitoring.The system accurately detects hierarchical behaviors across camera viewpoints.These results are fed into a Bird's Eye View(BEV)algorithm,producing precise cattle position maps in the barn.Despite complexities like overlapping and non-overlapping camera regions,our system,implemented on a real farm,ensures accurate cattle detection and BEV-based projections in real-time.Detailed experiments validate the system's efficiency,offering an end-to-end methodology for accurate behavior detection and location mapping of individual cattle using multi-camera data.
文摘The integration of Artificial Intelligence(AI)into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care.This comprehensive review explores the development,implementation,and challenges associated with AI-powered biometric facial identification in dairy agriculture.It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows,thereby facilitating thorough tracking of their health,behaviors,and productivity levels.Derived from facial recognition technologies originally designed for humans,this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations.The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks(CNNs)and deep learning frameworks signifies a transition toward data-driven agriculture.This analysis addresses notable challenges such as environmental variability,data collection difficulties,ethical considerations,and technological limitations.Furthermore,it compares various AI frameworks,highlighting their unique advantages and suitability in the dairy farming context.Despite these obstacles,facial recognition technology holds promise for enhancing farm efficiency,improving animal welfare,and promoting sustainable practices,underscoring the need for ongoing research and innovation.We advocate for future investigations focused on enhancing adaptability to diverse environments,ensuring ethical AI deployment,fostering compatibility across different breeds,and integrating with complementary agricultural technologies.Ultimately,this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices.
文摘This research introduces a new inclination correction method with increased accuracy applied to the guidance system of an agricultural vehicle.The method considers the geometry of a robot tractor and uses an Inertial Measurement Unit(IMU)to correct the lateral error of the RTK-GPS antenna measurements raised by the tractor's inclinations.A parameters optimization experiment and an automatic guidance experiment under real working conditions were used to compare the accuracy of a traditional correction method with the new correction method,by calculating the RMSE of the lateral error and the error reduction percentage.An additional tuned correction method was found by using a simple analytical method to find the optimal variables that reduced the lateral error to a minimum.The results indicate that the new correction method and the tuned correction method display a significant error reduction percentage compared to the traditional correction method.The methods could correct the GPS lateral error caused by the roll inclinations in real-time.The resulting lateral deviation caused by the tractor's inclinations could be reduced up to 23%for typical travelling speeds.
