The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform
Ping Tang - One of the best experts on this subject based on the ideXlab platform.
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Increasing Shape Bias to Improve the Precision of Center Pivot Irrigation System Detection
Remote Sensing, 2021Co-Authors: Jiwen Tang, Zhang Zheng, Zhao Lijun, Ping TangAbstract:Irrigation is indispensable in agriculture. Center Pivot Irrigation systems are popular means of Irrigation since they are water-efficient and labor-saving. Monitoring Center Pivot Irrigation systems provides important information for the understanding of agricultural production, water resources consumption and environmental change. Deep learning has become an effective approach for object detection and semantic segmentation. Recent studies have shown that convolutional neural networks (CNNs) are prone to be texture-biased rather than shape-biased, and increasing shape bias can improve the robustness and performance of CNNs. In this study, a simple yet effective method was proposed to increase shape bias in object detection networks to improve the precision of Center Pivot Irrigation system detection. We extracted edge images of training samples and integrated them into the training data to increase shape bias in the networks. With the proposed shape increasing training scheme, we evaluated and compared PVANET and YOLOv4. Experiments with the images in Mato Grosso have shown that both PVANET and YOLOv4 achieved improved performance, which demonstrated the validity of the proposed method.
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Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images
Water, 2021Co-Authors: Jiwen Tang, Damien Arvor, Thomas Corpetti, Ping TangAbstract:Irrigation systems play an important role in agriculture. Center Pivot Irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of Center Pivot Irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of Center Pivot Irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate Center Pivot Irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of Center Pivot Irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of Center Pivot Irrigation systems detected, the area of Irrigation in the region was estimated.
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PVANET-HOUGH: DETECTION AND LOCATION OF Center Pivot Irrigation SYSTEMS FROM SENTINEL-2 IMAGES
ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences, 2020Co-Authors: Jiwen Tang, Damien Arvor, Thomas Corpetti, Ping TangAbstract:Irrigation systems play an important role in agriculture. As being labor-saving and water consumption efficient, Center Pivot Irrigation systems are popular in many countries. Monitoring the distribution of Center Pivot Irrigation systems can provide important information for agriculture production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of Center Pivot Irrigation systems, PVANET-Hough, is proposed. The proposed method combines a lightweight real-time object detection network PVANET based on deep learning and accurate shape detection Hough transform to detect and accurately locate Center Pivot Irrigation systems. The method proposed in this paper does not need any preprocessing, PVANET is lightweight and fast, Hough transform can accurately detect the shape of Center Pivot Irrigation systems, and reduce the false alarms of PVANET at the mean time. Experiments with the Sentinel-2 images in Mato Grosso demonstrated the effectiveness of the proposed method.
Jiwen Tang - One of the best experts on this subject based on the ideXlab platform.
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Increasing Shape Bias to Improve the Precision of Center Pivot Irrigation System Detection
Remote Sensing, 2021Co-Authors: Jiwen Tang, Zhang Zheng, Zhao Lijun, Ping TangAbstract:Irrigation is indispensable in agriculture. Center Pivot Irrigation systems are popular means of Irrigation since they are water-efficient and labor-saving. Monitoring Center Pivot Irrigation systems provides important information for the understanding of agricultural production, water resources consumption and environmental change. Deep learning has become an effective approach for object detection and semantic segmentation. Recent studies have shown that convolutional neural networks (CNNs) are prone to be texture-biased rather than shape-biased, and increasing shape bias can improve the robustness and performance of CNNs. In this study, a simple yet effective method was proposed to increase shape bias in object detection networks to improve the precision of Center Pivot Irrigation system detection. We extracted edge images of training samples and integrated them into the training data to increase shape bias in the networks. With the proposed shape increasing training scheme, we evaluated and compared PVANET and YOLOv4. Experiments with the images in Mato Grosso have shown that both PVANET and YOLOv4 achieved improved performance, which demonstrated the validity of the proposed method.
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Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images
Water, 2021Co-Authors: Jiwen Tang, Damien Arvor, Thomas Corpetti, Ping TangAbstract:Irrigation systems play an important role in agriculture. Center Pivot Irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of Center Pivot Irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of Center Pivot Irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate Center Pivot Irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of Center Pivot Irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of Center Pivot Irrigation systems detected, the area of Irrigation in the region was estimated.
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PVANET-HOUGH: DETECTION AND LOCATION OF Center Pivot Irrigation SYSTEMS FROM SENTINEL-2 IMAGES
ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences, 2020Co-Authors: Jiwen Tang, Damien Arvor, Thomas Corpetti, Ping TangAbstract:Irrigation systems play an important role in agriculture. As being labor-saving and water consumption efficient, Center Pivot Irrigation systems are popular in many countries. Monitoring the distribution of Center Pivot Irrigation systems can provide important information for agriculture production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of Center Pivot Irrigation systems, PVANET-Hough, is proposed. The proposed method combines a lightweight real-time object detection network PVANET based on deep learning and accurate shape detection Hough transform to detect and accurately locate Center Pivot Irrigation systems. The method proposed in this paper does not need any preprocessing, PVANET is lightweight and fast, Hough transform can accurately detect the shape of Center Pivot Irrigation systems, and reduce the false alarms of PVANET at the mean time. Experiments with the Sentinel-2 images in Mato Grosso demonstrated the effectiveness of the proposed method.
Crystal Snodgrass - One of the best experts on this subject based on the ideXlab platform.
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Water savings for potato production using Center Pivot Irrigation in southwest FloridaWater savings for potato production using Center Pivot Irrigation in southwest Florida
2018Co-Authors: Xiaolin Liao, Guodong Liu, Lincoln Zotarelli, Bielinski M. Santos, Teresa P. Salame-donoso, Crystal Snodgrass, Alan JonesAbstract:Seepage Irrigation is the most widely used Irrigation system for potato production in Florida but is inefficient in water use. To evaluate the potential water-savings under Center Pivot, field trials were conducted on a commercial potato farm in Parrish, FL, where both Center Pivot and seepage Irrigation systems were compared side by side. The Irrigation water usage, potato yield and quality were compared between the two Irrigation systems in the 2012–13 growing season at two locations. Two 20-foot rows were used for tuber yield measurement. Our results showed no significant difference in tuber yields and leaf greenness between seepage and Center Pivot Irrigation. The total water applied for seepage Irrigation and Center Pivot Irrigation ranged from 24 to 36 inches and 9 to 15 inches, respectively. Center Pivot Irrigation used 35% to 75% less water and had high water use efficiency compared to seepage Irrigation. In addition, after two freeze events of 2013, better foliage coverage, greener plants, and less freeze damage were found under the Center Pivot system. More research is required to fully evaluate the potential of switching from conventional seepage Irrigation to overhead Irrigation.
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Impact of soil moisture and temperature on potato production using seepage and Center Pivot Irrigation
Agricultural Water Management, 2016Co-Authors: Xiaolin Liao, Guodong Liu, Lincoln Zotarelli, Yuqi Cui, Crystal SnodgrassAbstract:Abstract Irrigation, soil moisture and temperature play an important role in potato production. This field study was conducted at a private potato farm in SW Florida from 2012 to 2014. The randomized complete block design was used: four production farms each with a pair of seepage and hybrid Center Pivot Irrigation systems. Soil moisture and temperature at five soil depths, rainfall, and water table in situ were monitored. Nitrate levels at the top 20 cm soils were measured at harvest in the second growing season. Water usage was calculated by the flow meters and rain gauges. Potato yields were measured. The stepwise linear regression showed that the potato yield was mainly regulated by the surface (10 cm) soil temperature and soil water moisture at 20 and 30 cm depths. Hybrid Center Pivot can save more than 50% of Irrigation water without significant yield loss, suggesting Center Pivot has great potential in water savings. Hybrid Center Pivot Irrigation had relatively low nitrate concentrations at the top 20 cm soil, indicating a new fertilizer program may be needed for overhead Irrigation.
Diyi Chen - One of the best experts on this subject based on the ideXlab platform.
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Effects of Travel Speed and Collector on Evaluation of the Water Application Uniformity of a Center Pivot Irrigation System
Water, 2020Co-Authors: Xin Hui, Haijun Yan, Diyi ChenAbstract:Water application uniformity is an important performance parameter when designing and operating an Irrigation system. Performance tests of a Center Pivot Irrigation system equipped with fixed and rotated spray plate sprinklers (FSPS and RSPS, respectively) were conducted at five travel speeds. The effects of travel speed, collector size, and setting height on water application uniformity were evaluated using Heermann and Hein’s coefficient of uniformity (CUH). The CUH was 12.7% higher for the RSPS than the FSPS and decreased as the travel speed increased. Collector size and setting height affected CUH, and CUH was higher when the collector had a large opening cross-section compared to the collector with a small opening cross-section. CUH was higher when the collector with a low setting height compared to when it a high setting height for the FSPS. However, collector setting height had no effect on CUH for the RSPS. The weighted average water application depth (Dw) decreased as the travel speed increased. Collector size had no significant effect on Dw, but Dw with a low collector setting height was larger than the values with a high collector setting height. The water application rate increased as distance from the Pivot point increased and was higher for the FSPS than the RSPS. The results will improve the selection of travel speed and collector when the water application uniformity of a Center Pivot Irrigation system is evaluated.
D. F. Heermann - One of the best experts on this subject based on the ideXlab platform.
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A statistical approach to estimating runoff in Center Pivot Irrigation with crust conditions
Agricultural Water Management, 2005Co-Authors: P.b. Luz, D. F. HeermannAbstract:There have been several proposals to evaluate potential runoff in Center Pivot Irrigation, through the integration of time varying infiltration‐precipitation rate curves, involving complex iterative procedures. Some methods use empirical infiltration functions, such as the Kostiakov equation. Others use physically based infiltration functions, such as the Green‐Ampt equation. Another option is to use the Richards equation, describing the one-dimensional vertical infiltration of water into the soil for a specified Irrigation event. This equation is generally accepted to provide a basis for comparison between other runoff estimation methods.
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Center Pivot Irrigation ATTACHED SPRAYER
Applied Engineering in Agriculture, 1997Co-Authors: H. R. Sumner, P. M. Garvey, D. F. Heermann, L. D. ChandlerAbstract:Sprayers were designed and installed on a four-tower and a single-tower Center Pivot Irrigation system. The Center Pivot-attached sprayer systems use micro-Irrigation equipment to reduce investment costs and provide ease of installation. Water application uniformity was determined with a computer simulation model and an aircraft spray pattern sampling analysis system. Micro-Irrigation sprinklers spaced 3 to 3.6 m (10 to 12 ft) apart on a separate Pivot mounted sprayer manifold with drop tubes 2 m (6 ft) above the ground resulted in 87 to 95 coefficient of uniformity.