T. Saracoglu, Cengiz Ozarslan, A. F. Hacıyusufoğlu
HighlightsA poppy harvester can be used as an alternative to the manual harvesting.Effective field capacity of a poppy harvester is 34 to 53 times higher than the manual harvest.Depending on the forward speed, field losses increase.Cleaning efficiency can be increased by a more aggressive sieving application.Abstract. The operations of collecting the poppy from the field by hand and then breaking it apart require intensive labor and time consumption, which increases the cost significantly. A mechanical harvester to be used for poppy harvesting will save human labor and reduce time consumption. Hence, a poppy harvester was designed to harvest and crush poppy capsules, and separate the stalks, seeds, and capsule parts from the shredded material with this study. The prototype harvester consists of a harvest unit, conveying unit, threshing unit, separating and cleaning unit, bagging unit, and power transmission unit. The machine is pulled by the tractor and its moving units are driven by the PTO and hydraulic system. In field experiments with the prototype poppy harvester, the material capacity of the machine (seed, capsule pieces, and total product), cleaning efficiency, and harvest losses were determined. The experiments were conducted in a randomized complete block design with three replicates. The prototype machine was operated at two forward speeds of 1.24 km h-1 and 1.95 km h-1. The effective field capacity of the harvester was determined to be 34 to 53 times higher than the manual harvest, and increasing forward speed, increased machine capacity by approximately 50%. The cleaning efficiency was determined to be approximately 84% for both forward speeds. Depending on the forward speed, field losses increased and varied between 13% and 21%. Keywords: Keywords., Cleaning capacity, Field losses, Harvesting capacity, Poppy harvester.
罂粟收割机可用作人工收割的替代品。罂粟收获机的有效田间容量是人工收获的34至53倍。磁场损耗随着前进速度的增加而增加。采用更强力的筛分可以提高清洗效率。手工采集罂粟，然后将其分解，需要大量的劳动和时间，这大大增加了成本。一种用于罂粟收获的机械收割机将节省人力，减少时间消耗。因此，本研究设计了一种罂粟收割机，用于收获和粉碎罂粟胶囊，并从粉碎的材料中分离出茎、种子和蒴果部分。该原型收割机由收获单元、输送单元、脱粒单元、分离清洗单元、装袋单元、动力传输单元组成。机器由牵引车牵引，其运动单元由PTO和液压系统驱动。在样机罂粟收获机的田间试验中，确定了机器的物料容量(种子、蒴果片和总产品)、清洗效率和收获损失。试验采用完全随机区组设计，设3个重复。原型机以1.24 km h-1和1.95 km h-1两种前进速度运行。确定了收获机的有效田间容量比人工收获高34 ~ 53倍，并且增加前进速度，机器容量增加了约50%。在两种前进速度下，清洗效率均约为84%。随着前进速度的增加，磁场损失增加，在13%到21%之间变化。关键词:关键词。清洗能力，田间损失，收获能力，罂粟收割机。
{"title":"Determination of the Field Performance of a Prototype Combined Poppy Harvester","authors":"T. Saracoglu, Cengiz Ozarslan, A. F. Hacıyusufoğlu","doi":"10.13031/aea.15373","DOIUrl":"https://doi.org/10.13031/aea.15373","url":null,"abstract":"HighlightsA poppy harvester can be used as an alternative to the manual harvesting.Effective field capacity of a poppy harvester is 34 to 53 times higher than the manual harvest.Depending on the forward speed, field losses increase.Cleaning efficiency can be increased by a more aggressive sieving application.Abstract. The operations of collecting the poppy from the field by hand and then breaking it apart require intensive labor and time consumption, which increases the cost significantly. A mechanical harvester to be used for poppy harvesting will save human labor and reduce time consumption. Hence, a poppy harvester was designed to harvest and crush poppy capsules, and separate the stalks, seeds, and capsule parts from the shredded material with this study. The prototype harvester consists of a harvest unit, conveying unit, threshing unit, separating and cleaning unit, bagging unit, and power transmission unit. The machine is pulled by the tractor and its moving units are driven by the PTO and hydraulic system. In field experiments with the prototype poppy harvester, the material capacity of the machine (seed, capsule pieces, and total product), cleaning efficiency, and harvest losses were determined. The experiments were conducted in a randomized complete block design with three replicates. The prototype machine was operated at two forward speeds of 1.24 km h-1 and 1.95 km h-1. The effective field capacity of the harvester was determined to be 34 to 53 times higher than the manual harvest, and increasing forward speed, increased machine capacity by approximately 50%. The cleaning efficiency was determined to be approximately 84% for both forward speeds. Depending on the forward speed, field losses increased and varied between 13% and 21%. Keywords: Keywords., Cleaning capacity, Field losses, Harvesting capacity, Poppy harvester.","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":"67052808","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 The optimal speed of each mechanism of the sugarcane combine harvester in the medium harvesting working condition is different. A power intelligent matching strategy for sugarcane combine harvester is proposed. Precisely matched performance indicators of target harvesting conditions. A number of suggestions are provided to optimize the power matching of the entire powertrain. Abstract. Due to the complex topography, small plot size, rainy climate, and different crop sparsity in western China, sugarcane harvesting operations suffer from poor harvesting performance and unstable harvesting effect. Therefore, the power matching strategy of sugarcane combine harvester needs to be optimized to solve these serial problems. In this article, the whole power system of sugarcane combine harvester is designed, and the load-sensitive system control is used to improve the efficiency of the hydraulic system and optimize the power distribution. Meanwhile, this article studies the power control system and proposes a power intelligent matching strategy for sugarcane combine harvester to adjust the power output of the power system. The power intelligent matching strategy considers the harvesting conditions and system structure of the sugarcane harvester, optimizes the engine output power and operating point distribution by using filters and fuzzy control algorithms, uses an accumulator to store excess energy and replenish system power to precisely match performance targets for target harvesting conditions and improve fuel economy. The experimental results show that the sugarcane combine harvester with a power intelligent matching strategy can freely switch the working mode in upslope, downslope, sunny, rainy and various crop density fields, and meet the demanded power of each device by dynamically adjusting the working point of the engine according to the operating conditions, enabling the system to work better. The research method in this article can provide a theoretical basis for small sugarcane combine harvesters to harvest sugarcane in hilly areas, rainy seasons, and different crop densities. Keywords: Harvest conditions, Power matching, Smooth power following control strategy, Sugarcane combine harvester.
{"title":"A Power Matching Control Strategy for Sugarcane Combine Harvesters","authors":"Ke Liang, Yuzhen Feng, Bowei Yao, Huasheng Chen, Mingzhang Pan, Yongzhi Tang, Wei Guan","doi":"10.13031/aea.15560","DOIUrl":"https://doi.org/10.13031/aea.15560","url":null,"abstract":"Highlights The optimal speed of each mechanism of the sugarcane combine harvester in the medium harvesting working condition is different. A power intelligent matching strategy for sugarcane combine harvester is proposed. Precisely matched performance indicators of target harvesting conditions. A number of suggestions are provided to optimize the power matching of the entire powertrain. Abstract. Due to the complex topography, small plot size, rainy climate, and different crop sparsity in western China, sugarcane harvesting operations suffer from poor harvesting performance and unstable harvesting effect. Therefore, the power matching strategy of sugarcane combine harvester needs to be optimized to solve these serial problems. In this article, the whole power system of sugarcane combine harvester is designed, and the load-sensitive system control is used to improve the efficiency of the hydraulic system and optimize the power distribution. Meanwhile, this article studies the power control system and proposes a power intelligent matching strategy for sugarcane combine harvester to adjust the power output of the power system. The power intelligent matching strategy considers the harvesting conditions and system structure of the sugarcane harvester, optimizes the engine output power and operating point distribution by using filters and fuzzy control algorithms, uses an accumulator to store excess energy and replenish system power to precisely match performance targets for target harvesting conditions and improve fuel economy. The experimental results show that the sugarcane combine harvester with a power intelligent matching strategy can freely switch the working mode in upslope, downslope, sunny, rainy and various crop density fields, and meet the demanded power of each device by dynamically adjusting the working point of the engine according to the operating conditions, enabling the system to work better. The research method in this article can provide a theoretical basis for small sugarcane combine harvesters to harvest sugarcane in hilly areas, rainy seasons, and different crop densities. Keywords: Harvest conditions, Power matching, Smooth power following control strategy, Sugarcane combine harvester.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"30 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":"135910272","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}
Derek Koji Uemura, Sanjay B. Shah, Prafulla Regmi, Jesse Grimes, Lingjuan Wang-Li
Highlights Simple, low-cost infrared camera calibration method proposed. Calibration equation can improve accuracy for a narrower range of surface temperature. Infrared camera moderately sensitive to both emissivity and reflected air temperature. Abstract. Infrared (IR) or thermal cameras are being increasingly used in livestock research and management. An IR camera’s accuracy is specified over its entire surface temperature measurement range, whereas in livestock research and management, a narrower range suffices. A camera’s accuracy could be higher in a narrower range of temperatures. Hence, a novel low-cost method was used to calculate the FLIR E8 camera’s accuracy in a range of 24°C to 37°C, representative of surface temperature of poultry birds. Sensitivity analyses were also performed to evaluate the impact of three user specified parameters, namely, emissivity (e), distance between camera and surface (d), and reflected air temperature (tair). A linear regression model was used to correct the camera’s absolute error of 2.8°C (greater than its published error). However, the camera possessed precision and hence, repeatability. The IR camera was moderately sensitive to e, and slightly sensitive to tair and d, but its error could increase with the difference between the measured and assumed tair values. Attention is required to accurately characterize e and tair. This simple calibration method can reduce cost and could improve accuracy in a narrower temperature range than the IR camera’s published range, which could be useful for applied research. Keywords: Absolute error, Accuracy, Emissivity, Heat stress, IR, Precision, Reflected air temperature, Sensitivity analysis.
{"title":"Low-cost Calibration Method for the Infrared Camera","authors":"Derek Koji Uemura, Sanjay B. Shah, Prafulla Regmi, Jesse Grimes, Lingjuan Wang-Li","doi":"10.13031/aea.15546","DOIUrl":"https://doi.org/10.13031/aea.15546","url":null,"abstract":"Highlights Simple, low-cost infrared camera calibration method proposed. Calibration equation can improve accuracy for a narrower range of surface temperature. Infrared camera moderately sensitive to both emissivity and reflected air temperature. Abstract. Infrared (IR) or thermal cameras are being increasingly used in livestock research and management. An IR camera’s accuracy is specified over its entire surface temperature measurement range, whereas in livestock research and management, a narrower range suffices. A camera’s accuracy could be higher in a narrower range of temperatures. Hence, a novel low-cost method was used to calculate the FLIR E8 camera’s accuracy in a range of 24°C to 37°C, representative of surface temperature of poultry birds. Sensitivity analyses were also performed to evaluate the impact of three user specified parameters, namely, emissivity (e), distance between camera and surface (d), and reflected air temperature (tair). A linear regression model was used to correct the camera’s absolute error of 2.8°C (greater than its published error). However, the camera possessed precision and hence, repeatability. The IR camera was moderately sensitive to e, and slightly sensitive to tair and d, but its error could increase with the difference between the measured and assumed tair values. Attention is required to accurately characterize e and tair. This simple calibration method can reduce cost and could improve accuracy in a narrower temperature range than the IR camera’s published range, which could be useful for applied research. Keywords: Absolute error, Accuracy, Emissivity, Heat stress, IR, Precision, Reflected air temperature, Sensitivity analysis.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"4 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":"135559322","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}
Gary W. Marek, Steve Evett, Thomas Henry Marek, Dana Porter, Robert C. Schwartz
Highlights Soil profile water content derived from Acclima TDR-315™ sensors approximated those from NMM measurements. Soil profile water content from Campbell Scientific SoilVUE™10 sensors grossly underestimated those from the NMM. VWC values from SoilVUE10 sensors were consistently less than those reported by the TDR-315 sensors at all depths. These findings do not support SoilVUE10 use for irrigation scheduling in clay loam soils. Abstract. A field study was performed to evaluate the efficacy of two commercially available time domain reflectometry (TDR) soil water sensors for irrigation scheduling in a clay loam soil near Bushland, Texas. SoilVUE10 (Campbell Scientific Inc., Logan, Utah) and TDR-315 (Acclima Inc., Meridian, Idaho) sensors were installed within 30 cm of neutron moisture meter (NMM) access tubes in a research field planted to corn (Zea mays L) in 2020 and irrigated by a center pivot sprinkler system. Irrigation treatments included 50%, 75%, and 100% of evapotranspiration (ET) replacement with two access tubes installed in each plot, totaling six sensor evaluation sites. Semiweekly measurements with a field-calibrated NMM were used to monitor soil water status and schedule irrigation throughout the growing season. Soil profile water content values integrated over the surface to 1.1-m depth range were derived from SoilVUE10 and vertically distributed arrays of Acclima TDR-315 sensors installed at equivalent depths and were compared with those from NMM data. Average profile soil water contents from the TDR-315 sensors trended well with those from the NMM having mean bias difference (MBD) values of -9.8, -3.1, and 8.4 mm for the 50%, 75%, and 100% treatments, respectively. In contrast, soil profile water content values from the SoilVUE10 sensors grossly underestimated those from the NMM for all irrigation treatments with MBD values of -54.4, -70.5, and -89.8 mm for the 50%, 75%, and 100% treatments, respectively. Comparisons of volumetric water content (VWC) at each of the nine depths common to both electromagnetic sensor types revealed that values from the SoilVUE10 sensors were consistently less than TDR-315 values for all irrigation treatments. Underestimation at the near surface (5 and 10 cm depths) was attributed to loss of soil to electrode contact possibly associated with clay shrinkage during periodic drying following irrigation. Although soil to electrode contact can be problematic at greater depths, the explanation for chronic underestimation of VWC was less obvious except to note that underestimation occurred immediately after installation, which indicated poor electrode-soil contact after installation despite use of manufacturer guidelines and tools. Other possible reasons include challenges for accurate estimation of soil permittivity for a measured permittivity that includes the plastic sensor body. Results from this study suggest vertically distributed arrays of TDR-315 sensors can provide profile water content values
{"title":"Field Evaluation of Conventional and Downhole TDR Soil Water Sensors for Irrigation Scheduling in a Clay Loam Soil","authors":"Gary W. Marek, Steve Evett, Thomas Henry Marek, Dana Porter, Robert C. Schwartz","doi":"10.13031/aea.15574","DOIUrl":"https://doi.org/10.13031/aea.15574","url":null,"abstract":"Highlights Soil profile water content derived from Acclima TDR-315™ sensors approximated those from NMM measurements. Soil profile water content from Campbell Scientific SoilVUE™10 sensors grossly underestimated those from the NMM. VWC values from SoilVUE10 sensors were consistently less than those reported by the TDR-315 sensors at all depths. These findings do not support SoilVUE10 use for irrigation scheduling in clay loam soils. Abstract. A field study was performed to evaluate the efficacy of two commercially available time domain reflectometry (TDR) soil water sensors for irrigation scheduling in a clay loam soil near Bushland, Texas. SoilVUE10 (Campbell Scientific Inc., Logan, Utah) and TDR-315 (Acclima Inc., Meridian, Idaho) sensors were installed within 30 cm of neutron moisture meter (NMM) access tubes in a research field planted to corn (Zea mays L) in 2020 and irrigated by a center pivot sprinkler system. Irrigation treatments included 50%, 75%, and 100% of evapotranspiration (ET) replacement with two access tubes installed in each plot, totaling six sensor evaluation sites. Semiweekly measurements with a field-calibrated NMM were used to monitor soil water status and schedule irrigation throughout the growing season. Soil profile water content values integrated over the surface to 1.1-m depth range were derived from SoilVUE10 and vertically distributed arrays of Acclima TDR-315 sensors installed at equivalent depths and were compared with those from NMM data. Average profile soil water contents from the TDR-315 sensors trended well with those from the NMM having mean bias difference (MBD) values of -9.8, -3.1, and 8.4 mm for the 50%, 75%, and 100% treatments, respectively. In contrast, soil profile water content values from the SoilVUE10 sensors grossly underestimated those from the NMM for all irrigation treatments with MBD values of -54.4, -70.5, and -89.8 mm for the 50%, 75%, and 100% treatments, respectively. Comparisons of volumetric water content (VWC) at each of the nine depths common to both electromagnetic sensor types revealed that values from the SoilVUE10 sensors were consistently less than TDR-315 values for all irrigation treatments. Underestimation at the near surface (5 and 10 cm depths) was attributed to loss of soil to electrode contact possibly associated with clay shrinkage during periodic drying following irrigation. Although soil to electrode contact can be problematic at greater depths, the explanation for chronic underestimation of VWC was less obvious except to note that underestimation occurred immediately after installation, which indicated poor electrode-soil contact after installation despite use of manufacturer guidelines and tools. Other possible reasons include challenges for accurate estimation of soil permittivity for a measured permittivity that includes the plastic sensor body. Results from this study suggest vertically distributed arrays of TDR-315 sensors can provide profile water content values ","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"183 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":"135560338","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}
Lingyan Hu, Wei Xu, Zhanjun Guo, Shaohang Qiu, Y. Pei, Zu-Min Wang
Highlights Asynchronous overlapping—an automatic image acquisition method for key feature regions of plant phenotypes. The distance from the plant to the camera can be characterized by the brightness in the grayscale image. Asynchronously acquire daytime RGB and nighttime grayscale images of the plant to use the proposed algorithm. In the test of the plant images, the IoU is 0.8497, reaching a similar level of interactive algorithms. Abstract. Acquiring and describing plant phenotyping is an important proposition in botany and agronomy research. In this study, a computer vision-based asynchronous overlapping segmentation algorithm is proposed for automatic image acquisition of key feature regions of plant phenotyping. Firstly, day-time RGB and night-time grayscale images of infrared light filling the crop body at the same angle are asynchronously obtained using a common closed-circuit television surveillance camera. Then, thresholding and morphological filtering of grayscale images are conducted to extract the initial region contours. With this as a precondition, the algorithm adaptively finds edge paths of key feature regions in daytime RGB images. In the test of the cherry plant image, the intersection over union (IoU) of the algorithm to segment the key feature regions is 0.8497, reaching a similar level of interactive algorithms that require human involvement. The proposed method has low cost, high segmentation accuracy, and strong applicability. The proposed method can independently realize the acquisition of the key feature regions of plant image phenotypes and can be applied to large-scale agricultural production.
异步重叠-植物表型关键特征区域的自动图像采集方法。从植物到相机的距离可以通过灰度图像中的亮度来表征。采用该算法异步获取植物的日间RGB和夜间灰度图像。在对植物图像的测试中，IoU为0.8497，达到了与交互式算法相似的水平。摘要植物表型的获取和描述是植物学和农学研究中的一个重要课题。本文提出了一种基于计算机视觉的异步重叠分割算法，用于植物表型关键特征区域的图像自动获取。首先，利用普通闭路电视监控摄像机，异步获取相同角度的红外光填充作物体的日间RGB和夜间灰度图像;然后对灰度图像进行阈值分割和形态学滤波，提取初始区域轮廓;在此前提下，该算法自适应地找到白天RGB图像中关键特征区域的边缘路径。在对樱桃植物图像的测试中，该算法分割关键特征区域的IoU (intersection over union)为0.8497，达到了与需要人工参与的交互式算法相似的水平。该方法成本低，分割精度高，适用性强。该方法可独立实现植物图像表型关键特征区域的获取，可应用于大规模农业生产。
{"title":"Asynchronous Overlapping: An Image Segmentation Method for Key Feature Regions of Plant Phenotyping","authors":"Lingyan Hu, Wei Xu, Zhanjun Guo, Shaohang Qiu, Y. Pei, Zu-Min Wang","doi":"10.13031/aea.15083","DOIUrl":"https://doi.org/10.13031/aea.15083","url":null,"abstract":"Highlights Asynchronous overlapping—an automatic image acquisition method for key feature regions of plant phenotypes. The distance from the plant to the camera can be characterized by the brightness in the grayscale image. Asynchronously acquire daytime RGB and nighttime grayscale images of the plant to use the proposed algorithm. In the test of the plant images, the IoU is 0.8497, reaching a similar level of interactive algorithms. Abstract. Acquiring and describing plant phenotyping is an important proposition in botany and agronomy research. In this study, a computer vision-based asynchronous overlapping segmentation algorithm is proposed for automatic image acquisition of key feature regions of plant phenotyping. Firstly, day-time RGB and night-time grayscale images of infrared light filling the crop body at the same angle are asynchronously obtained using a common closed-circuit television surveillance camera. Then, thresholding and morphological filtering of grayscale images are conducted to extract the initial region contours. With this as a precondition, the algorithm adaptively finds edge paths of key feature regions in daytime RGB images. In the test of the cherry plant image, the intersection over union (IoU) of the algorithm to segment the key feature regions is 0.8497, reaching a similar level of interactive algorithms that require human involvement. The proposed method has low cost, high segmentation accuracy, and strong applicability. The proposed method can independently realize the acquisition of the key feature regions of plant image phenotypes and can be applied to large-scale agricultural production.","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":"67051127","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 Design storms should incorporate nonstationarity under changing climate scenarios. Three generalized extreme value distributions were fitted to represent nonstationarity for local precipitation analysis. The nonstationary models proposed in this study perform well at sites with strong precipitation trends. Abstract. As climate change advances, the stationarity assumption that governs traditional precipitation analysis is becoming untenable. Studies that incorporate nonstationarity typically use global circulation model (GCM) projections to determine the magnitude and direction of expected precipitation changes. However, the high computational costs and the coarse spatial resolution of GCMs make this method unsuitable for local precipitation analysis. In this study, nonstationarity is represented by a precipitation probability distribution with time-varying parameters. Three generalized extreme value (GEV) distributions are fitted: (1) the shift model, where the GEV location parameter varies linearly with time, (2) the stretch model, where the GEV location and scale parameters both vary linearly with time, and (3) the stationary model, a time-invariant distribution provided for the purpose of comparison. This procedure is applied to 24-h annual maximum precipitation records for ninety years (1900-1989) at five long-term measuring sites in Pennsylvania. Results varied among the five sites, suggesting that localized climate effects can cause precipitation differences at a small spatial scale. No significant nonstationarity was detected in two of the five locations. In three locations, however, increases in GEV location and scale combined to create a substantial, though not always significant, rise in the frequency of extreme precipitation. These trends were extrapolated forward over 30 years (1990-2019) and compared with an observed distribution for that year. The nonstationary models appeared to perform better at sites with stronger precipitation trends, which suggests a simple procedure for selecting sites where nonstationary analysis is most needed. Keywords: Climate change, Design storm, Generalized extreme value, Nonstationarity.
{"title":"Using Nonstationary Depth-Frequency Curves to Characterize Local Precipitation Trends","authors":"Kalra Marali, R. Cibin","doi":"10.13031/aea.15247","DOIUrl":"https://doi.org/10.13031/aea.15247","url":null,"abstract":"Highlights Design storms should incorporate nonstationarity under changing climate scenarios. Three generalized extreme value distributions were fitted to represent nonstationarity for local precipitation analysis. The nonstationary models proposed in this study perform well at sites with strong precipitation trends. Abstract. As climate change advances, the stationarity assumption that governs traditional precipitation analysis is becoming untenable. Studies that incorporate nonstationarity typically use global circulation model (GCM) projections to determine the magnitude and direction of expected precipitation changes. However, the high computational costs and the coarse spatial resolution of GCMs make this method unsuitable for local precipitation analysis. In this study, nonstationarity is represented by a precipitation probability distribution with time-varying parameters. Three generalized extreme value (GEV) distributions are fitted: (1) the shift model, where the GEV location parameter varies linearly with time, (2) the stretch model, where the GEV location and scale parameters both vary linearly with time, and (3) the stationary model, a time-invariant distribution provided for the purpose of comparison. This procedure is applied to 24-h annual maximum precipitation records for ninety years (1900-1989) at five long-term measuring sites in Pennsylvania. Results varied among the five sites, suggesting that localized climate effects can cause precipitation differences at a small spatial scale. No significant nonstationarity was detected in two of the five locations. In three locations, however, increases in GEV location and scale combined to create a substantial, though not always significant, rise in the frequency of extreme precipitation. These trends were extrapolated forward over 30 years (1990-2019) and compared with an observed distribution for that year. The nonstationary models appeared to perform better at sites with stronger precipitation trends, which suggests a simple procedure for selecting sites where nonstationary analysis is most needed. Keywords: Climate change, Design storm, Generalized extreme value, Nonstationarity.","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":"67051566","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}
Tesfay Gebretsadkan Gebremicael, G. Haile, Mulubrhan Kifle, Teferi Gebremedhin, Matthew J. Deitch, K. Reda, A. Reda
Highlights Irrigation experiments on onion productivity were conducted at two sites for two consecutive years in Ethiopia. Factors from irrigation interval and irrigation method analyzed independently and factorially. Basin irrigation and fixed irrigation produced higher bulb yields and water productivity. Farmers’ income can be enhanced using improved irrigation practices for increased onion productivity. Abstract. How to meet the crop water demand and improve crop productivity is a particular concern for small-scale farmers, where the availability of water resources is limited. This study evaluated three different irrigation methods (furrow, basin and border) and two types of irrigation scheduling (CROPWAT schedule and farmers’ practices) with three replications for two consecutive years (2016-2017) at two sites using onion crops. The results showed that the CROPWAT schedule, basin irrigation method, and their interactions showed better performances and produced higher yield and water productivity. An average of 26 and 27 metric tons/hectare of onion were obtained under the basin irrigation method and basin irrigation with CROPWAT schedule combined, respectively. The water productivity (WP) and irrigation water productivity (IWP) also showed higher results under the basin irrigation method compared to other treatment combinations. The basin irrigation method produced higher marketable onion bulbs with firm medium bulb sizes that are essential for onion producers to earn maximum profit. The findings of this study also indicate that focusing on enhanced irrigation scheduling techniques and irrigation methods is paramount for better onion productivity in irrigation water-limited areas. Keywords: Irrigation interval, Irrigation methods, Irrigation practices, Onion productivity, Water management.
{"title":"Effects of Irrigation Methods and Scheduling on Water and Onion Productivity in Semi-Arid Areas of Ethiopia","authors":"Tesfay Gebretsadkan Gebremicael, G. Haile, Mulubrhan Kifle, Teferi Gebremedhin, Matthew J. Deitch, K. Reda, A. Reda","doi":"10.13031/aea.15256","DOIUrl":"https://doi.org/10.13031/aea.15256","url":null,"abstract":"Highlights Irrigation experiments on onion productivity were conducted at two sites for two consecutive years in Ethiopia. Factors from irrigation interval and irrigation method analyzed independently and factorially. Basin irrigation and fixed irrigation produced higher bulb yields and water productivity. Farmers’ income can be enhanced using improved irrigation practices for increased onion productivity. Abstract. How to meet the crop water demand and improve crop productivity is a particular concern for small-scale farmers, where the availability of water resources is limited. This study evaluated three different irrigation methods (furrow, basin and border) and two types of irrigation scheduling (CROPWAT schedule and farmers’ practices) with three replications for two consecutive years (2016-2017) at two sites using onion crops. The results showed that the CROPWAT schedule, basin irrigation method, and their interactions showed better performances and produced higher yield and water productivity. An average of 26 and 27 metric tons/hectare of onion were obtained under the basin irrigation method and basin irrigation with CROPWAT schedule combined, respectively. The water productivity (WP) and irrigation water productivity (IWP) also showed higher results under the basin irrigation method compared to other treatment combinations. The basin irrigation method produced higher marketable onion bulbs with firm medium bulb sizes that are essential for onion producers to earn maximum profit. The findings of this study also indicate that focusing on enhanced irrigation scheduling techniques and irrigation methods is paramount for better onion productivity in irrigation water-limited areas. Keywords: Irrigation interval, Irrigation methods, Irrigation practices, Onion productivity, Water management.","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":"67051760","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 A mobile application embedded onto smart mobile devices was developed for on-site chicken health assessment based on fecal images. A trained deep learning image classification model was programmed into the application for classifying healthy birds or unhealthy birds infected with Coccidiosis , Salmonella , and Newcastle disease . Animal caretakers can capture fecal images on farms, upload them to the developed application on their mobile devices, and receive health assessment results during daily flock inspection. The study demonstrates a successful proof-of-concept system but requires further work for consolidating system performance. Abstract. Rapid and accurate chicken health assessment can assist producers in making timely decisions, reducing disease transmission, improving animal welfare, and decreasing economic loss. The objective of this research was to develop and evaluate a proof-of-concept mobile application system to assist caretakers in assessing chicken health during their daily flock inspections. A computer server was built to assign users with different usage credentials and receive uploaded fecal images. A dataset containing fecal images from healthy and unhealthy birds (infected with Coccidiosis, Salmonella, and Newcastle disease) was used for classification model development. The modified MobileNetV2 model with additional layers of artificial neural networks was selected after a comparative evaluation of six models. The developed model was embedded into a local server for image classification. An application was developed and deployed, allowing a user with the application on a mobile device to upload a fecal image to a website hosted on the server and receive results processed by the model. Health status is transferred back to the user and can be shared with production managers. The system achieved over 90% accuracy for identifying diseases, and the whole operational procedure took less than one second. This proof-of-concept demonstrates the feasibility of a potential framework for mobile poultry health assessment based on fecal images. However, further development is needed to expand applicability to different production systems through the collection of fecal images from various genetic lines, ages, feed components, housing backgrounds, and flooring types in the poultry industry and improve system performance. Keywords: Artificial intelligence, Coccidiosis, Newcastle disease, Salmonella, Software development.
{"title":"An On-Site Feces Image Classifier System for Chicken Health Assessment: A Proof of Concept","authors":"Guoming Li, Richard S Gates, Brett C. Ramirez","doi":"10.13031/aea.15607","DOIUrl":"https://doi.org/10.13031/aea.15607","url":null,"abstract":"Highlights A mobile application embedded onto smart mobile devices was developed for on-site chicken health assessment based on fecal images. A trained deep learning image classification model was programmed into the application for classifying healthy birds or unhealthy birds infected with Coccidiosis , Salmonella , and Newcastle disease . Animal caretakers can capture fecal images on farms, upload them to the developed application on their mobile devices, and receive health assessment results during daily flock inspection. The study demonstrates a successful proof-of-concept system but requires further work for consolidating system performance. Abstract. Rapid and accurate chicken health assessment can assist producers in making timely decisions, reducing disease transmission, improving animal welfare, and decreasing economic loss. The objective of this research was to develop and evaluate a proof-of-concept mobile application system to assist caretakers in assessing chicken health during their daily flock inspections. A computer server was built to assign users with different usage credentials and receive uploaded fecal images. A dataset containing fecal images from healthy and unhealthy birds (infected with Coccidiosis, Salmonella, and Newcastle disease) was used for classification model development. The modified MobileNetV2 model with additional layers of artificial neural networks was selected after a comparative evaluation of six models. The developed model was embedded into a local server for image classification. An application was developed and deployed, allowing a user with the application on a mobile device to upload a fecal image to a website hosted on the server and receive results processed by the model. Health status is transferred back to the user and can be shared with production managers. The system achieved over 90% accuracy for identifying diseases, and the whole operational procedure took less than one second. This proof-of-concept demonstrates the feasibility of a potential framework for mobile poultry health assessment based on fecal images. However, further development is needed to expand applicability to different production systems through the collection of fecal images from various genetic lines, ages, feed components, housing backgrounds, and flooring types in the poultry industry and improve system performance. Keywords: Artificial intelligence, Coccidiosis, Newcastle disease, Salmonella, Software development.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"40 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":"135910584","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}
HighlightsAn indirect Kalman filter algorithm is proposed to fuse GNSS/INS positioning information.Detailed kinematics and dynamics model of track vehicles was established.An MPC-based double-layer closed-loop controller combined with tracked vehicle model is designed.Tracked transport vehicle performs well in path tracking on soft soil road.Abstract. Orchards in hills and mountainous regions are more occluded and single satellite navigation is unstable. Therefore, the indirect Kalman filter information fusion algorithm was proposed to achieve high-precision positioning by establishing a state error equation based on GNSS/INS. A complete kinematics and dynamics model of tracked chassis was established. A double-layer closed-loop controller based on model predictive control (MPC) was designed. An MPC controller based on the kinematics model in the outer loop was designed to output the expected control value of the tracked transporter. The inner loop design was based on the extended state observer of the dynamic model to estimate and compensate for the internal and external disturbances of the system. The performance test was based on a tracked chassis platform. The test results presented that when driving at a speed of 0.50 m/s under soft soil road conditions, the maximum lateral deviation was 0.15 m, and the average absolute deviation was 0.05 m. This high level of control accuracy means that this control design enables the transfer vehicle to follow the navigation path precisely and complete its task. Keywords: Hills and mountainous regions, Integrated navigation, Model predictive control, Vehicle dynamics model.
{"title":"Research on Automatic Driving Control of Tracked Transport Vehicle Based on Labview","authors":"Yao Yu, Yunwu Li, Yuyi Chen, Yingzheng Zhao","doi":"10.13031/aea.15127","DOIUrl":"https://doi.org/10.13031/aea.15127","url":null,"abstract":"HighlightsAn indirect Kalman filter algorithm is proposed to fuse GNSS/INS positioning information.Detailed kinematics and dynamics model of track vehicles was established.An MPC-based double-layer closed-loop controller combined with tracked vehicle model is designed.Tracked transport vehicle performs well in path tracking on soft soil road.Abstract. Orchards in hills and mountainous regions are more occluded and single satellite navigation is unstable. Therefore, the indirect Kalman filter information fusion algorithm was proposed to achieve high-precision positioning by establishing a state error equation based on GNSS/INS. A complete kinematics and dynamics model of tracked chassis was established. A double-layer closed-loop controller based on model predictive control (MPC) was designed. An MPC controller based on the kinematics model in the outer loop was designed to output the expected control value of the tracked transporter. The inner loop design was based on the extended state observer of the dynamic model to estimate and compensate for the internal and external disturbances of the system. The performance test was based on a tracked chassis platform. The test results presented that when driving at a speed of 0.50 m/s under soft soil road conditions, the maximum lateral deviation was 0.15 m, and the average absolute deviation was 0.05 m. This high level of control accuracy means that this control design enables the transfer vehicle to follow the navigation path precisely and complete its task. Keywords: Hills and mountainous regions, Integrated navigation, Model predictive control, Vehicle dynamics model.","PeriodicalId":55501,"journal":{"name":"Applied Engineering in Agriculture","volume":"118 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67051519","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}
Sixing Liu, Ming Liu, Yan Chai, Shuang Li, H. Miao
HighlightsAn improved YOLOv5s deep learning model was used to identify peppers in complex background.The deep-level features on 3D (O-XYZ) coordinate of peppers were extracted using RealSense depth camera.An image database set of pepper in different scenes was established.A pepper recognition and location system were constructed based on improved YOLOv5s network.The proposed method achieved a mean average precision of 95.6% and minimum depth error of 0.001 m.Abstract. In order to investigate the impact of different scenes on the recognition performance and obtain the location information of picking targets, the recognition and location system based on improved YOLOv5s network and RealSense depth camera was constructed in this study. An image database in different scenes was established including light intensity, occlusion and overlap degree of pepper. An improved YOLOv5s deep learning model with bidirectional feature pyramid network (BiFPN) was used for the deep feature extraction and high-precision detection of pepper, and the effects of different scenes on recognition accuracy of the model were studied. The results showed that mean average precision (mAP) of YOLOv5s model reached 0.956, which was respectively 6.1%, 9.3%, 44.4%, and 8.2% higher than that of YOLOv4, YOLOv3, YOLOv2, and Faster R-CNN model. The model had good robustness under daytime and evening scenes with the mAP value higher than 0.9. The detection accuracy of the model in the leaf occlusion scenes was better than that of fruit overlap. The detection error was 0.001m which could not affect the picking positioning precision when the Z value of three-dimensional coordinates (O-XYZ) of pepper was 0.2 m. The improved algorithm can accurately recognize and extract three-dimensional coordinates of pepper, which reduces the calculations by eliminating lots of duplicate and redundant prediction boxes and provides a reference for trajectory planning of pepper picking operation. Keywords: Different scenes, Pepper recognition and location, Picking operation, YOLOv5s.
{"title":"Recognition and Location of Pepper Picking Based on Improved YOLOv5s and Depth Camera","authors":"Sixing Liu, Ming Liu, Yan Chai, Shuang Li, H. Miao","doi":"10.13031/aea.15347","DOIUrl":"https://doi.org/10.13031/aea.15347","url":null,"abstract":"HighlightsAn improved YOLOv5s deep learning model was used to identify peppers in complex background.The deep-level features on 3D (O-XYZ) coordinate of peppers were extracted using RealSense depth camera.An image database set of pepper in different scenes was established.A pepper recognition and location system were constructed based on improved YOLOv5s network.The proposed method achieved a mean average precision of 95.6% and minimum depth error of 0.001 m.Abstract. In order to investigate the impact of different scenes on the recognition performance and obtain the location information of picking targets, the recognition and location system based on improved YOLOv5s network and RealSense depth camera was constructed in this study. An image database in different scenes was established including light intensity, occlusion and overlap degree of pepper. An improved YOLOv5s deep learning model with bidirectional feature pyramid network (BiFPN) was used for the deep feature extraction and high-precision detection of pepper, and the effects of different scenes on recognition accuracy of the model were studied. The results showed that mean average precision (mAP) of YOLOv5s model reached 0.956, which was respectively 6.1%, 9.3%, 44.4%, and 8.2% higher than that of YOLOv4, YOLOv3, YOLOv2, and Faster R-CNN model. The model had good robustness under daytime and evening scenes with the mAP value higher than 0.9. The detection accuracy of the model in the leaf occlusion scenes was better than that of fruit overlap. The detection error was 0.001m which could not affect the picking positioning precision when the Z value of three-dimensional coordinates (O-XYZ) of pepper was 0.2 m. The improved algorithm can accurately recognize and extract three-dimensional coordinates of pepper, which reduces the calculations by eliminating lots of duplicate and redundant prediction boxes and provides a reference for trajectory planning of pepper picking operation. Keywords: Different scenes, Pepper recognition and location, Picking operation, YOLOv5s.","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":"67052161","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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