{"title":"Investigating the Influence of Grain Variety on Calibration of Microwave Moisture Sensors","authors":"S. Trabelsi, M. Lewis, S. Nelson","doi":"10.13031/aea.15385","DOIUrl":"https://doi.org/10.13031/aea.15385","url":null,"abstract":"","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67052871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Azadwinder Chahal, Daniel E. Ciolkosz, Virendra Puri, Jude Liu, Michael Jacobson
Highlights Technoeconomic analysis helps increase understanding of the potential of shrub willow debarking and provide assessment of economic opportunities. Harvest and transport costs are increased by debarking and account for more than 50% of total costs. The fraction of clean wood material is higher when shrub willow is harvested during the dormant season. Energy to break the wood-bark bond is almost five times higher in the dormant season than the growing season. High yield of willow biomass, high wood fraction, and high field capacity have a positive influence on profitability. Abstract. A technoeconomic model was developed for integrated debarking/harvesting of shrub willow that assesses the costs associated with debarking of willow and provides a platform for estimating the minimum selling price (MSP) for separated bark material. Harvest and transport costs are influenced by the addition of a debarking process and accounts for more than 50% of the total costs of willow production. The estimated MSP for bark material is $24.53 Mg -1 when the willow biomass is harvested in the dormant season and rises to $28.65 Mg -1 when harvested during growing season. The fraction of clean wood material recovered during the dormant season harvest averaged to 72% compared to 66% in the growing season (for shrub willow cultivars in this study). Increasing the field size from 10 to 50 ha decreases MSP of bark by 47%. High yield (~26 Mg ha -1 ) creates a condition in which a producer can be profitable by selling clean wood material only (with positive NPV). Likewise, sensitivity analysis shows that under the conditions modeled in this study, shrub willow varieties with high wood fraction have a lower MSP for bark material; MSP for bark material approaches zero when the fraction of wood rises to 86.6%. Keywords: Biomass, Debarking, Shrub willow, Techno-economic analysis.
技术经济分析有助于提高对灌木柳树剥皮潜力的了解，并提供经济机会评估。收获和运输成本因剥离而增加，占总成本的50%以上。灌木柳在休养期采伐时，清洁木材的比例较高。在休眠期，打破木树皮结合的能量几乎是生长季的五倍。柳生物量高产、木材分率高、田间容量大对盈利能力有正向影响。摘要为灌木柳树的综合脱皮/采收开发了一个技术经济模型，该模型评估了柳树脱皮的相关成本，并为估计分离树皮材料的最低销售价格(MSP)提供了一个平台。收获和运输成本受附加剥皮过程的影响，占柳树生产总成本的50%以上。在柳树休眠季节收获树皮材料的估计MSP为24.53 Mg -1美元，而在生长季节收获树皮材料的MSP则上升到28.65 Mg -1美元。在休眠季节收获的清洁木材平均回收率为72%，而在生长季节(本研究的灌木柳树品种)平均回收率为66%。将农田面积从10公顷增加到50公顷，树皮的MSP降低了47%。高产(~26 Mg / ha -1)创造了一种条件，在这种条件下，生产者可以通过销售清洁木材材料(净现值为正)来获利。同样，敏感性分析表明，在本研究模拟的条件下，高木材分数的灌木柳树品种对树皮材料的MSP较低;当木材含量达到86.6%时，树皮材料的MSP趋于零。关键词:生物量;去皮;灌柳;
{"title":"A Technoeconomic Model for Estimating Costs of Harvesting and Debarking Shrub Willow","authors":"Azadwinder Chahal, Daniel E. Ciolkosz, Virendra Puri, Jude Liu, Michael Jacobson","doi":"10.13031/aea.15454","DOIUrl":"https://doi.org/10.13031/aea.15454","url":null,"abstract":"Highlights Technoeconomic analysis helps increase understanding of the potential of shrub willow debarking and provide assessment of economic opportunities. Harvest and transport costs are increased by debarking and account for more than 50% of total costs. The fraction of clean wood material is higher when shrub willow is harvested during the dormant season. Energy to break the wood-bark bond is almost five times higher in the dormant season than the growing season. High yield of willow biomass, high wood fraction, and high field capacity have a positive influence on profitability. Abstract. A technoeconomic model was developed for integrated debarking/harvesting of shrub willow that assesses the costs associated with debarking of willow and provides a platform for estimating the minimum selling price (MSP) for separated bark material. Harvest and transport costs are influenced by the addition of a debarking process and accounts for more than 50% of the total costs of willow production. The estimated MSP for bark material is $24.53 Mg -1 when the willow biomass is harvested in the dormant season and rises to $28.65 Mg -1 when harvested during growing season. The fraction of clean wood material recovered during the dormant season harvest averaged to 72% compared to 66% in the growing season (for shrub willow cultivars in this study). Increasing the field size from 10 to 50 ha decreases MSP of bark by 47%. High yield (~26 Mg ha -1 ) creates a condition in which a producer can be profitable by selling clean wood material only (with positive NPV). Likewise, sensitivity analysis shows that under the conditions modeled in this study, shrub willow varieties with high wood fraction have a lower MSP for bark material; MSP for bark material approaches zero when the fraction of wood rises to 86.6%. Keywords: Biomass, Debarking, Shrub willow, Techno-economic analysis.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135910585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul D. Colaizzi, Susan A. O’Shaughnessy, Steven R. Evett, Gary W. Marek, David Brauer, Karen S. Copeland, Brice B. Ruthardt
Highlights A quality control procedure was developed for infrared thermometer data. The procedure included ten tests that can identify data quality conditions. The test results were subject to criteria to recommend which data to use. Test data included six crop seasons and fallow periods. 56% of the data passed the test for the highest level of data quality. Abstract . The increased adoption of infrared thermometers (IRTs) for irrigation management of crops has resulted in increasingly large surface temperature datasets, resulting in a need for data quality assurance and control (QA/QC) procedures similar to those developed for agricultural weather station data. A QC procedure was developed to test for seven common data conditions, including sensor not deployed, missing, too high, too low, upward spike, downward spike, or stuck. The conditions of “too high” or “too low” used a simple energy balance procedure similar to the crop water stress index, where the theoretical lower and upper temperature limits of a surface were calculated, accounting for the vegetation view factor appearing in the IRT field-of-view. After passing the seven tests, data were assigned as Plausible, and further tested as Confirmed or Confirmed+. The Confirmed test compared each IRT to the median of the other IRTs during 2 h before sunrise and applied a threshold of ±0.5°C. The Confirmed+ test compared each IRT to the median of the other IRTs during ±2 h of solar noon and applied a threshold of ±2.0°C. The set of tests was applied to an IRT dataset that included six seasons of crops and fallow periods with 15-min time steps. Temperature differences greater than the thresholds (i.e., assigned Plausible but not Confirmed or Confirmed+) could detect anomalies including ice, dirty or obscured lenses, or biased data that other tests did not detect. Of all time intervals when 20 IRTs viewing a crop were deployed, 80% resulted in Plausible, 61% resulted in Confirmed, and 56% resulted in Confirmed+. The procedure can be easily customized and can increase the value of IRT datasets used for irrigation management. Keywords: Canopy temperature, Infrared thermometer, QA/QC, Quality assurance, quality control, Test, Weather data.
{"title":"Data Quality Control for Stationary Infrared Thermometers Viewing Crops","authors":"Paul D. Colaizzi, Susan A. O’Shaughnessy, Steven R. Evett, Gary W. Marek, David Brauer, Karen S. Copeland, Brice B. Ruthardt","doi":"10.13031/aea.15642","DOIUrl":"https://doi.org/10.13031/aea.15642","url":null,"abstract":"Highlights A quality control procedure was developed for infrared thermometer data. The procedure included ten tests that can identify data quality conditions. The test results were subject to criteria to recommend which data to use. Test data included six crop seasons and fallow periods. 56% of the data passed the test for the highest level of data quality. Abstract . The increased adoption of infrared thermometers (IRTs) for irrigation management of crops has resulted in increasingly large surface temperature datasets, resulting in a need for data quality assurance and control (QA/QC) procedures similar to those developed for agricultural weather station data. A QC procedure was developed to test for seven common data conditions, including sensor not deployed, missing, too high, too low, upward spike, downward spike, or stuck. The conditions of “too high” or “too low” used a simple energy balance procedure similar to the crop water stress index, where the theoretical lower and upper temperature limits of a surface were calculated, accounting for the vegetation view factor appearing in the IRT field-of-view. After passing the seven tests, data were assigned as Plausible, and further tested as Confirmed or Confirmed+. The Confirmed test compared each IRT to the median of the other IRTs during 2 h before sunrise and applied a threshold of ±0.5°C. The Confirmed+ test compared each IRT to the median of the other IRTs during ±2 h of solar noon and applied a threshold of ±2.0°C. The set of tests was applied to an IRT dataset that included six seasons of crops and fallow periods with 15-min time steps. Temperature differences greater than the thresholds (i.e., assigned Plausible but not Confirmed or Confirmed+) could detect anomalies including ice, dirty or obscured lenses, or biased data that other tests did not detect. Of all time intervals when 20 IRTs viewing a crop were deployed, 80% resulted in Plausible, 61% resulted in Confirmed, and 56% resulted in Confirmed+. The procedure can be easily customized and can increase the value of IRT datasets used for irrigation management. Keywords: Canopy temperature, Infrared thermometer, QA/QC, Quality assurance, quality control, Test, Weather data.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135911206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights To the best of our knowledge, this study was the first intelligent recognition for Chinese cabbage growth period and proposed the Swin Transformer+1 model. If the four growth periods were considered, the recognition accuracy rate of the model on the test set was 96.15%. If the transition periods of Chinese cabbage growth were considered, the model recognition accuracy rate was 97.17%. Experiments showed that the Swin Transformer+1 model was robust and could be applied in real agricultural production. Abstract. In order to facilitate agricultural management and improve the quality and yield of Chinese cabbage, it is necessary to intelligently identify the growth periods of Chinese cabbage. In this study, a transfer learning-based recognition model for Chinese cabbage growth periods was proposed, which could identify four growth periods of Chinese cabbage: “germination and seedling period,” “rosette period,” “heading period,” and “dormant period.” The data set of Chinese cabbage growth periods was built. The recognition model was named Swin Transformer+1, using Swin Transformer as the backbone network to extract image features, and a fully connected layer as the classifier. To optimize the model, we used Letterbox instead of Stretching to resize the image, used Focal Loss instead of Cross Entropy Loss as the loss function, and used Stochastic Weight Averaging instead of Adam as the optimizer. Transfer learning was used for training, which could solve the problems of overfitting and underfitting when training deep network with a small data set. We verified the effectiveness of the above improved methods through ablation experiments. Experiments showed that the Swin Transformer+1 model had a high recognition accuracy rate. If only the four growth periods were considered, the recognition accuracy rate was 96.15%. If the transition periods between two growth periods of Chinese cabbage were considered, the recognition accuracy rate was 97.17%. The model had strong robustness. It maintained a high recognition accuracy rate when the images in the test set were augmented. In general, Swin Transformer+1 model has high application value in actual agricultural production scenarios. Keywords: Chinese cabbage growth period, Deep learning, Image recognition, Swin transformer, Transfer learning
{"title":"Research on Recognition Method of Chinese Cabbage Growth Periods Based on Swin Transformer and Transfer Learning","authors":"Xin Chen, Yuexin Shi, Xiang Li","doi":"10.13031/aea.15260","DOIUrl":"https://doi.org/10.13031/aea.15260","url":null,"abstract":"Highlights To the best of our knowledge, this study was the first intelligent recognition for Chinese cabbage growth period and proposed the Swin Transformer+1 model. If the four growth periods were considered, the recognition accuracy rate of the model on the test set was 96.15%. If the transition periods of Chinese cabbage growth were considered, the model recognition accuracy rate was 97.17%. Experiments showed that the Swin Transformer+1 model was robust and could be applied in real agricultural production. Abstract. In order to facilitate agricultural management and improve the quality and yield of Chinese cabbage, it is necessary to intelligently identify the growth periods of Chinese cabbage. In this study, a transfer learning-based recognition model for Chinese cabbage growth periods was proposed, which could identify four growth periods of Chinese cabbage: “germination and seedling period,” “rosette period,” “heading period,” and “dormant period.” The data set of Chinese cabbage growth periods was built. The recognition model was named Swin Transformer+1, using Swin Transformer as the backbone network to extract image features, and a fully connected layer as the classifier. To optimize the model, we used Letterbox instead of Stretching to resize the image, used Focal Loss instead of Cross Entropy Loss as the loss function, and used Stochastic Weight Averaging instead of Adam as the optimizer. Transfer learning was used for training, which could solve the problems of overfitting and underfitting when training deep network with a small data set. We verified the effectiveness of the above improved methods through ablation experiments. Experiments showed that the Swin Transformer+1 model had a high recognition accuracy rate. If only the four growth periods were considered, the recognition accuracy rate was 96.15%. If the transition periods between two growth periods of Chinese cabbage were considered, the recognition accuracy rate was 97.17%. The model had strong robustness. It maintained a high recognition accuracy rate when the images in the test set were augmented. In general, Swin Transformer+1 model has high application value in actual agricultural production scenarios. Keywords: Chinese cabbage growth period, Deep learning, Image recognition, Swin transformer, Transfer learning","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135910287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights Risk assessment for highly automated and autonomous agricultural machines must consider risks beyond operator risk. Engineering standards are a starting point for autonomous equipment risk assessment but are not yet adequate. Engineers designing highly automated equipment now assess risk holistically but need more tools and support. Education in accredited engineering programs and professional development should include risk assessment. Abstract. Technology continues to advance in agricultural machines and includes the development of highly automated, robotic, autonomous, and other types of machines used in fields, farmsteads, buildings, and other farm production locations. New engineering design and safety-related standards have been developed in the past half-decade, but safety remains a concern of key stakeholders and is a barrier that could influence widespread adoption. A survey of practicing engineers and researchers involved with highly automated and autonomous agricultural machine design will be presented that shows the methods for risk assessment and control currently in use including different frameworks for hazard and failure identification, prediction, and quantification. The use of engineering design standards (ASABE, ISO, and others) among practitioners is discussed including some important needs that go beyond obstacle detection and injury prevention for operators. These include safety and risk issues connected to animals, property, civic infrastructure, downtime, cyber, and environmental risk. Commonly used risk assessment methods such as the related failure modes and effects analysis (FMEA) or hazard analysis and risk assessment (HARA) are a useful starting point but are based on historical data and experience that can be used to estimate the probability and severity levels of undesirable failures or incidents such as injuries. These data do not yet exist as compared to risk assessment data that can be used to assess incident occurrence probability, failure, detectability, or controllability in more traditional machines. Suggestions are presented for further development of standards and practice recommendations including software needs and operational data that might be used by autonomous machines that is informed by what we do know about past farm incidents that could include accidents, injuries, and other unexpected failures. Keywords: Automation, Autonomous agricultural machinery, Engineering design standards, Farm equipment, Risk assessment, Robotics, Safety.
{"title":"Risk Assessment Methods for Autonomous Agricultural Machines: A Review of Current Practices and Future Needs","authors":"J. Shutske, Kelly J. Sandner, Zachary Jamieson","doi":"10.13031/aea.15281","DOIUrl":"https://doi.org/10.13031/aea.15281","url":null,"abstract":"Highlights Risk assessment for highly automated and autonomous agricultural machines must consider risks beyond operator risk. Engineering standards are a starting point for autonomous equipment risk assessment but are not yet adequate. Engineers designing highly automated equipment now assess risk holistically but need more tools and support. Education in accredited engineering programs and professional development should include risk assessment. Abstract. Technology continues to advance in agricultural machines and includes the development of highly automated, robotic, autonomous, and other types of machines used in fields, farmsteads, buildings, and other farm production locations. New engineering design and safety-related standards have been developed in the past half-decade, but safety remains a concern of key stakeholders and is a barrier that could influence widespread adoption. A survey of practicing engineers and researchers involved with highly automated and autonomous agricultural machine design will be presented that shows the methods for risk assessment and control currently in use including different frameworks for hazard and failure identification, prediction, and quantification. The use of engineering design standards (ASABE, ISO, and others) among practitioners is discussed including some important needs that go beyond obstacle detection and injury prevention for operators. These include safety and risk issues connected to animals, property, civic infrastructure, downtime, cyber, and environmental risk. Commonly used risk assessment methods such as the related failure modes and effects analysis (FMEA) or hazard analysis and risk assessment (HARA) are a useful starting point but are based on historical data and experience that can be used to estimate the probability and severity levels of undesirable failures or incidents such as injuries. These data do not yet exist as compared to risk assessment data that can be used to assess incident occurrence probability, failure, detectability, or controllability in more traditional machines. Suggestions are presented for further development of standards and practice recommendations including software needs and operational data that might be used by autonomous machines that is informed by what we do know about past farm incidents that could include accidents, injuries, and other unexpected failures. Keywords: Automation, Autonomous agricultural machinery, Engineering design standards, Farm equipment, Risk assessment, Robotics, Safety.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67052133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhihong Zhang, Heping Zhu, H. Jeon, E. Ozkan, Zhiming Wei, R. Salcedo
Highlights A turbidity sensor module was investigated to monitor concentrations of pesticide mixtures in real time. A flow-through measurement platform was built to calibrate the turbidity sensor and determine its accuracy. Optimal location of the turbidity sensor was determined for monitoring in-line mixture concentrations. Spray mixture uniformity reached acceptable level for the premixing in-line injection system. Abstract. Monitoring mixture concentrations for precision pesticide spray systems in real time can assure the desired amount of chemicals distributed uniformly to target areas. An in-line turbidity sensor module was investigated to monitor concentrations of spray mixtures produced with a premixing in-line injection system developed for precision variable-rate orchard sprayers. The turbidity sensor was calibrated with simulated pesticides at concentrations ranging from 0% to 30.0%. A cubic polynomial regression model was established for the relationship between sensor output voltages and mixture concentrations. Sensors were mounted at three in-line locations to detect the mixture uniformity differences in the premixing in-line injection system. The module was found to have adequate precision and accuracy to measure concentrations of spray mixtures with simulated pesticides. Relative errors of the sensor were less than 4.70% and the sensor accuracy did not vary with mixture flow rates. Mounting the turbidity sensor downstream of the buffer tank in the premixing in-line injection system would be the optimal location to monitor spray mixture uniformity for variable-rate spray applications. At this location, the relative errors of measured mixture concentrations were between 0.12% and 3.70% which agreed with previous manual measurements. Therefore, there would be a great potential to integrate the in-line turbidity sensor into the variable-rate and even conventional constant-rate sprayers to achieve uniform spray applications in the target field. Keywords: Agitation, Mixture uniformity, Pesticide concentration, Sensor calibration, Variable-rate sprayer.
{"title":"A Turbidity Module to Measure Spray Mixture Concentration for Premixing In-Line Injection System","authors":"Zhihong Zhang, Heping Zhu, H. Jeon, E. Ozkan, Zhiming Wei, R. Salcedo","doi":"10.13031/aea.15245","DOIUrl":"https://doi.org/10.13031/aea.15245","url":null,"abstract":"Highlights A turbidity sensor module was investigated to monitor concentrations of pesticide mixtures in real time. A flow-through measurement platform was built to calibrate the turbidity sensor and determine its accuracy. Optimal location of the turbidity sensor was determined for monitoring in-line mixture concentrations. Spray mixture uniformity reached acceptable level for the premixing in-line injection system. Abstract. Monitoring mixture concentrations for precision pesticide spray systems in real time can assure the desired amount of chemicals distributed uniformly to target areas. An in-line turbidity sensor module was investigated to monitor concentrations of spray mixtures produced with a premixing in-line injection system developed for precision variable-rate orchard sprayers. The turbidity sensor was calibrated with simulated pesticides at concentrations ranging from 0% to 30.0%. A cubic polynomial regression model was established for the relationship between sensor output voltages and mixture concentrations. Sensors were mounted at three in-line locations to detect the mixture uniformity differences in the premixing in-line injection system. The module was found to have adequate precision and accuracy to measure concentrations of spray mixtures with simulated pesticides. Relative errors of the sensor were less than 4.70% and the sensor accuracy did not vary with mixture flow rates. Mounting the turbidity sensor downstream of the buffer tank in the premixing in-line injection system would be the optimal location to monitor spray mixture uniformity for variable-rate spray applications. At this location, the relative errors of measured mixture concentrations were between 0.12% and 3.70% which agreed with previous manual measurements. Therefore, there would be a great potential to integrate the in-line turbidity sensor into the variable-rate and even conventional constant-rate sprayers to achieve uniform spray applications in the target field. Keywords: Agitation, Mixture uniformity, Pesticide concentration, Sensor calibration, Variable-rate sprayer.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67051986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aseema Singh, S. Taghvaeian, A. Mirchi, D. Moriasi
HighlightsStation aridity can cause overestimation of ETref at weather monitoring networks in irrigated areas.Station aridity was demonstrated in a mesoscale weather monitoring network.Station aridity is amplified in water-scarce irrigated areas during droughts.Station aridity should be accounted for to achieve water conservation through weather-informed irrigation.Abstract. Many weather monitoring networks such as the Oklahoma Mesonet provide estimates of reference evapotranspiration (ETref) to facilitate weather-informed irrigation decisions. However, weather stations that collect the required input data to estimate ETref using the widely applied ASCE standardized ETref equation are not typically installed over a reference surface, defined as a large expanse of dense, well-watered, stress-free grass or alfalfa having a specified height, surface resistance, and albedo. The deviation of actual surface conditions in the surrounding environment of the weather stations from the reference condition creates station aridity effects that can lead to overestimation of ETref. Daily hydroclimate datasets for a period of 20 years (2000-2019) were used to evaluate the prevalence and spatiotemporal characteristics of station aridity across the Oklahoma Mesonet. Station aridity was characterized based on mean dew point deviation (MDD = Tmin - Tdew), maximum relative humidity (RHmax), and normalized difference vegetation index (NDVI). Results demonstrate that station aridity is prevalent and highly variable in both space and time across the Oklahoma Mesonet, as it increases from southeast to northwest in the Oklahoma Panhandle. Larger average seasonal MDD (up to 13°C), lower RHmax (e.g., 57%), and lower NDVI (e.g., 0.22) were observed during extreme to exceptional drought of 2011 in western Oklahoma, where a majority of the state’s irrigated agriculture (88%) is located. Spatiotemporal patterns of station aridity demonstrate the profound effect of wet and dry periods that influence the utility of ETref estimates to improve agricultural water conservation during high irrigation requirement times in water-scarce irrigated areas. Keywords: Evapotranspiration, Irrigation requirement, Reference condition, Station aridity, Weather station.
{"title":"Station Aridity in Weather Monitoring Networks: Evidence from the Oklahoma Mesonet","authors":"Aseema Singh, S. Taghvaeian, A. Mirchi, D. Moriasi","doi":"10.13031/aea.15325","DOIUrl":"https://doi.org/10.13031/aea.15325","url":null,"abstract":"HighlightsStation aridity can cause overestimation of ETref at weather monitoring networks in irrigated areas.Station aridity was demonstrated in a mesoscale weather monitoring network.Station aridity is amplified in water-scarce irrigated areas during droughts.Station aridity should be accounted for to achieve water conservation through weather-informed irrigation.Abstract. Many weather monitoring networks such as the Oklahoma Mesonet provide estimates of reference evapotranspiration (ETref) to facilitate weather-informed irrigation decisions. However, weather stations that collect the required input data to estimate ETref using the widely applied ASCE standardized ETref equation are not typically installed over a reference surface, defined as a large expanse of dense, well-watered, stress-free grass or alfalfa having a specified height, surface resistance, and albedo. The deviation of actual surface conditions in the surrounding environment of the weather stations from the reference condition creates station aridity effects that can lead to overestimation of ETref. Daily hydroclimate datasets for a period of 20 years (2000-2019) were used to evaluate the prevalence and spatiotemporal characteristics of station aridity across the Oklahoma Mesonet. Station aridity was characterized based on mean dew point deviation (MDD = Tmin - Tdew), maximum relative humidity (RHmax), and normalized difference vegetation index (NDVI). Results demonstrate that station aridity is prevalent and highly variable in both space and time across the Oklahoma Mesonet, as it increases from southeast to northwest in the Oklahoma Panhandle. Larger average seasonal MDD (up to 13°C), lower RHmax (e.g., 57%), and lower NDVI (e.g., 0.22) were observed during extreme to exceptional drought of 2011 in western Oklahoma, where a majority of the state’s irrigated agriculture (88%) is located. Spatiotemporal patterns of station aridity demonstrate the profound effect of wet and dry periods that influence the utility of ETref estimates to improve agricultural water conservation during high irrigation requirement times in water-scarce irrigated areas. Keywords: Evapotranspiration, Irrigation requirement, Reference condition, Station aridity, Weather station.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67052407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights Context-driven yield data cleaning resulted in more accurate whole field yield estimates Using a context-driven yield data cleaning method can improve yield estimates for zones within fields Identifying error-prone areas in field where data quality is likely to be low and removing that data in bulk can reduce data cleaning bias Abstract. As agriculture becomes more data driven, decision-making has become the focus of the industry and data quality will be increasingly important. Traditionally, yield data cleaning techniques have removed individual data points based on criteria primarily focused on the yield values themselves. However, when these methods are used, the underlying causes of the errors are often overlooked and as a result, these techniques may fail to remove all of the inaccurate (error-prone) data and/or remove legitimate data. In this research, an alternative to data cleaning was developed. Data integrity zones (DIZ) within each field were identified by evaluating metadata which included data collected by the combine that reported the operating conditions of the machinery (i.e., travel speed, crop mass flow), data about the field environment (i.e., soil type, topography, weather), and data of field operations (e.g., field logs, as-applied maps). Data in DIZ were isolated using buffers and the analysis of the reduced datasets was compared to the raw data. The amount of data removed depended on the amount of variability (e.g. soil characteristics, topography) in the field. Statistical comparisons of the data showed the mean yield estimates for soil type polygons increased by an average of 1.4 Mg/ha for corn when DIZ data was used compared to raw data. On average, the confidence around the mean remains similar even with a large amount (70%) of data removed. Notably, the none of the mean estimates derived from raw datasets were contained in the confidence intervals produced from DIZ data. This meta-data (context-driven) alternative to data cleaning effectively removed errors and artifacts from yield data which would only be identified when looking beyond the yield measurements themselves. When similarly reduced datasets are used to analyze historical yield data, they should provide a clearer picture of true yield effects of treatments, management zones, soil types, etc.; this will improve decisions on input and resource allocation, support wiser adoption of precision agricultural technologies, and refine future data collection. Keywords: Combine yield monitor, Context, Data analysis, Integrity zones, Management zones, Metadata, Precision agriculture, Yield, Yield data.
{"title":"Improving Yield Data Analysis Using Contextual Data","authors":"Elizabeth M. Hawkins, Dennis R. Buckmaster","doi":"10.13031/aea.14655","DOIUrl":"https://doi.org/10.13031/aea.14655","url":null,"abstract":"Highlights Context-driven yield data cleaning resulted in more accurate whole field yield estimates Using a context-driven yield data cleaning method can improve yield estimates for zones within fields Identifying error-prone areas in field where data quality is likely to be low and removing that data in bulk can reduce data cleaning bias Abstract. As agriculture becomes more data driven, decision-making has become the focus of the industry and data quality will be increasingly important. Traditionally, yield data cleaning techniques have removed individual data points based on criteria primarily focused on the yield values themselves. However, when these methods are used, the underlying causes of the errors are often overlooked and as a result, these techniques may fail to remove all of the inaccurate (error-prone) data and/or remove legitimate data. In this research, an alternative to data cleaning was developed. Data integrity zones (DIZ) within each field were identified by evaluating metadata which included data collected by the combine that reported the operating conditions of the machinery (i.e., travel speed, crop mass flow), data about the field environment (i.e., soil type, topography, weather), and data of field operations (e.g., field logs, as-applied maps). Data in DIZ were isolated using buffers and the analysis of the reduced datasets was compared to the raw data. The amount of data removed depended on the amount of variability (e.g. soil characteristics, topography) in the field. Statistical comparisons of the data showed the mean yield estimates for soil type polygons increased by an average of 1.4 Mg/ha for corn when DIZ data was used compared to raw data. On average, the confidence around the mean remains similar even with a large amount (70%) of data removed. Notably, the none of the mean estimates derived from raw datasets were contained in the confidence intervals produced from DIZ data. This meta-data (context-driven) alternative to data cleaning effectively removed errors and artifacts from yield data which would only be identified when looking beyond the yield measurements themselves. When similarly reduced datasets are used to analyze historical yield data, they should provide a clearer picture of true yield effects of treatments, management zones, soil types, etc.; this will improve decisions on input and resource allocation, support wiser adoption of precision agricultural technologies, and refine future data collection. Keywords: Combine yield monitor, Context, Data analysis, Integrity zones, Management zones, Metadata, Precision agriculture, Yield, Yield data.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135910870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Le Yang, Huanhuan Zhang, Zhengkang Zuo, Jun Peng, Xiaoyun Yu, Huibin Long, Yuanjun Liao
Highlights The attention mechanism enhances the ability of the model to learn specific semantic information in encoder. The redesigned residual structure deepens the network while reducing the number of parameters. The feature extraction module and feature fusion module obtain richer boundary feature information and effectively integrate output results from different levels. The mIoU, mPA, and Precision values of AFU-Net in the self-built dataset are 87.25%, 92.23%, and 99.67%, respectively. Abstract. Rice diseases adversely affect rice growth and yield. Precise spot segmentation helps to assess the severity of the disease so that appropriate control measures can be taken. In this article, we propose a segmentation method called AFU-Net for rice leaf diseases, and its performance is verified through experiments. Based on the traditional UNet, this method incorporates an attention mechanism, a residual module and a feature fusion module (FFM). The attention mechanism is embedded in skip connections, which enhances the learning of particular semantic features in the encoder layer. In addition, the residual module is integrated into the decoder layer, which deepens the network and enables it to extract richer semantic information. The proposed FFM structure effectively enhances the learning of boundary information and local detail features. The experimental results show that the mean intersection over union (mIoU), mean pixel accuracy (mPA) and Precision of the proposed model on the self-built rice leaf disease segmentation dataset are 87.25%, 92.23%, and 99.67%, respectively. All three evaluation indexes were improved over the control group, while the proposed model had the lowest number of parameters and displayed a good segmentation effect for smaller disease points and disease parts with less obvious characteristics. Keywords: Attention mechanism, Feature fusion module, Residual module, Rice leaves, UNet model.
{"title":"AFU-Net: A Novel U-Net Network for Rice Leaf Disease Segmentation","authors":"Le Yang, Huanhuan Zhang, Zhengkang Zuo, Jun Peng, Xiaoyun Yu, Huibin Long, Yuanjun Liao","doi":"10.13031/aea.15581","DOIUrl":"https://doi.org/10.13031/aea.15581","url":null,"abstract":"Highlights The attention mechanism enhances the ability of the model to learn specific semantic information in encoder. The redesigned residual structure deepens the network while reducing the number of parameters. The feature extraction module and feature fusion module obtain richer boundary feature information and effectively integrate output results from different levels. The mIoU, mPA, and Precision values of AFU-Net in the self-built dataset are 87.25%, 92.23%, and 99.67%, respectively. Abstract. Rice diseases adversely affect rice growth and yield. Precise spot segmentation helps to assess the severity of the disease so that appropriate control measures can be taken. In this article, we propose a segmentation method called AFU-Net for rice leaf diseases, and its performance is verified through experiments. Based on the traditional UNet, this method incorporates an attention mechanism, a residual module and a feature fusion module (FFM). The attention mechanism is embedded in skip connections, which enhances the learning of particular semantic features in the encoder layer. In addition, the residual module is integrated into the decoder layer, which deepens the network and enables it to extract richer semantic information. The proposed FFM structure effectively enhances the learning of boundary information and local detail features. The experimental results show that the mean intersection over union (mIoU), mean pixel accuracy (mPA) and Precision of the proposed model on the self-built rice leaf disease segmentation dataset are 87.25%, 92.23%, and 99.67%, respectively. All three evaluation indexes were improved over the control group, while the proposed model had the lowest number of parameters and displayed a good segmentation effect for smaller disease points and disease parts with less obvious characteristics. Keywords: Attention mechanism, Feature fusion module, Residual module, Rice leaves, UNet model.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135559323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Zhang, R. Striker, Moruf Disu, E. Monono, A. Peckrul, Gurmukh Advani, Bingcan Chen, Benjamin D. Braaten, Xin Sun
HighlightsRadio frequency sensing technology was used to estimate clean grain mass based on grain moisture content and grain properties.Multiple variable regression analysis was used to develop grain mass estimation model.A grain mass estimation model with high R2 was developed by introducing dielectric properties and phase angle.Parameter of dielectric constant e' indicated the domination of moisture content in grain mass estimation model.Abstract. Grain mass estimation is critical in many precision agriculture applications, especially in yield monitoring during harvest procedures. A new clean grain mass estimation method using Radio Frequency (RF) sensing technology is discussed in this paper. RF sensing technology is sensitive to moisture content and grain properties. In this study, a vector network analyzer (VNA) and a pair of horn antennas were used to collect phase shift and attenuation data from 1 to 18 GHz of grain samples (soybean, canola, and corn) on a static testbed in an anechoic chamber. Using multiple variable linear regression analysis, a comprehensive clean grain mass estimation model was developed based on the dielectric properties of the grain samples derived from the S-Parameters at 13 GHz. Dielectric (e') constant/properties and phase shift were introduced into the regression models and generated a grain mass estimation result with R2 values of 0.976, 0.977, and 0.989 for soybean, canola, and corn samples, respectively. The results indicate that RF sensing technology can reveal how grain attributes interact with electromagnetic fields at a certain frequency and has the potential to provide more accurate sensing methods for estimating grain mass in multiple precision agricultural applications. Keywords: Keywords., Dielectric properties, Grain mass estimation, Microwave frequency, Phase shifts, Radio frequency sensing.
{"title":"Dielectric Constant-Based Grain Mass Estimation Using Radio Frequencies Sensing Technology","authors":"Yu Zhang, R. Striker, Moruf Disu, E. Monono, A. Peckrul, Gurmukh Advani, Bingcan Chen, Benjamin D. Braaten, Xin Sun","doi":"10.13031/aea.15121","DOIUrl":"https://doi.org/10.13031/aea.15121","url":null,"abstract":"HighlightsRadio frequency sensing technology was used to estimate clean grain mass based on grain moisture content and grain properties.Multiple variable regression analysis was used to develop grain mass estimation model.A grain mass estimation model with high R2 was developed by introducing dielectric properties and phase angle.Parameter of dielectric constant e' indicated the domination of moisture content in grain mass estimation model.Abstract. Grain mass estimation is critical in many precision agriculture applications, especially in yield monitoring during harvest procedures. A new clean grain mass estimation method using Radio Frequency (RF) sensing technology is discussed in this paper. RF sensing technology is sensitive to moisture content and grain properties. In this study, a vector network analyzer (VNA) and a pair of horn antennas were used to collect phase shift and attenuation data from 1 to 18 GHz of grain samples (soybean, canola, and corn) on a static testbed in an anechoic chamber. Using multiple variable linear regression analysis, a comprehensive clean grain mass estimation model was developed based on the dielectric properties of the grain samples derived from the S-Parameters at 13 GHz. Dielectric (e') constant/properties and phase shift were introduced into the regression models and generated a grain mass estimation result with R2 values of 0.976, 0.977, and 0.989 for soybean, canola, and corn samples, respectively. The results indicate that RF sensing technology can reveal how grain attributes interact with electromagnetic fields at a certain frequency and has the potential to provide more accurate sensing methods for estimating grain mass in multiple precision agricultural applications. Keywords: Keywords., Dielectric properties, Grain mass estimation, Microwave frequency, Phase shifts, Radio frequency sensing.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67051397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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