scholarly journals Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2159 ◽  
Author(s):  
Anesmar Olino de Albuquerque ◽  
Osmar Abílio de Carvalho Júnior ◽  
Osmar Luiz Ferreira de Carvalho ◽  
Pablo Pozzobon de Bem ◽  
Pedro Henrique Guimarães Ferreira ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Precision Agriculture ◽  
High Efficiency ◽  
Irrigation System ◽  
Semantic Segmentation ◽  
Environmental Conservation ◽  
Landsat 8 ◽  
Central Brazil ◽  
Center Pivot ◽  
Definition Of

The center pivot irrigation system (CPIS) is a modern irrigation technique widely used in precision agriculture due to its high efficiency in water consumption and low labor compared to traditional irrigation methods. The CPIS is a leader in mechanized irrigation in Brazil, with growth forecast for the coming years. Therefore, the mapping of center pivot areas is a strategic factor for the estimation of agricultural production, ensuring food security, water resources management, and environmental conservation. In this regard, digital processing of satellite images is the primary tool allowing regional and continuous monitoring with low costs and agility. However, the automatic detection of CPIS using remote sensing images remains a challenge, and much research has adopted visual interpretation. Although CPIS presents a consistent circular shape in the landscape, these areas can have a high internal variation with different plantations that vary over time, which is difficult with just the spectral behavior. Deep learning using convolutional neural networks (CNNs) is an emerging approach that provokes a revolution in image segmentation, surpassing traditional methods, and achieving higher accuracy and efficiency. This research aimed to evaluate the use of deep semantic segmentation of CPIS from CNN-based algorithms using Landsat-8 surface reflectance images (seven bands). The developed methodology can be subdivided into the following steps: (a) Definition of three study areas with a high concentration of CPIS in Central Brazil; (b) acquisition of Landsat-8 images considering the seasonal variations of the rain and drought periods; (c) definition of CPIS datasets containing Landsat images and ground truth mask of 256×256 pixels; (d) training using three CNN architectures (U-net, Deep ResUnet, and SharpMask); (e) accuracy analysis; and (f) large image reconstruction using six stride values (8, 16, 32, 64, 128, and 256). The three methods achieved state-of-the-art results with a slight prevalence of U-net over Deep ResUnet and SharpMask (0.96, 0.95, and 0.92 Kappa coefficients, respectively). A novelty in this research was the overlapping pixel analysis in the large image reconstruction. Lower stride values had improvements quantified by the Receiver Operating Characteristic curve (ROC curve) and Kappa, and fewer errors in the frame edges were also perceptible. The overlapping images significantly improved the accuracy and reduced the error present in the edges of the classified frames. Additionally, we obtained greater accuracy results during the beginning of the dry season. The present study enabled the establishment of a database of center pivot images and an adequate methodology for mapping the center pivot in central Brazil.

scholarly journals Increasing Shape Bias to Improve the Precision of Center Pivot Irrigation System Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 612
Author(s):  
Jiwen Tang ◽  
Zheng Zhang ◽  
Lijun Zhao ◽  
Ping Tang
Keyword(s):  
Object Detection ◽  
Irrigation System ◽  
Semantic Segmentation ◽  
Training Data ◽  
Irrigation Systems ◽  
Mato Grosso ◽  
Training Scheme ◽  
Shape Bias ◽  
Center Pivot ◽  
Improved Performance

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.

scholarly journals Application of MATLAB for Designing and Simulation of High-Efficiency Center Pivot Irrigation System

2020 ◽  
Vol 33 (2) ◽  
Author(s):  
Filza Fatima Rizvi ◽  
Waqar Khan ◽  
Syed Mohsin Raza Kazmi ◽  
Muhammad Umer
Keyword(s):  
High Efficiency ◽  
Irrigation System ◽  
Center Pivot

scholarly journals Investment recovery period in electric motor of higher efficiency in pumping for irrigation

2013 ◽  
Vol 33 (2) ◽  
pp. 212-222 ◽  
Author(s):  
João L. Zocoler ◽  
Maurício A. Leite ◽  
João C. C. Saad ◽  
Raimundo L. Cruz ◽  
João G. Zocoler
Keyword(s):  
Electric Motor ◽  
High Efficiency ◽  
Irrigation System ◽  
Recovery Period ◽  
Standard Line ◽  
Pumping System ◽  
Motor Efficiency ◽  
Center Pivot ◽  
Tariff Structures ◽  
Tariff Structure

In this study, it was adjusted a mathematical model to measure the effect of electric motor efficiency on pumping system costs for irrigation on the tariff structure of conventional electricity and green horo-seasonal , and also to calculate the recovery period of the invested capital in higher efficiency equipment. Then, it was applied to a center pivot irrigation system in two options of electric motor efficiency, 92,6% (standard line) and 94,3% (high efficiency line), and the acquisition cost of the first corresponded to 70% the of the second. The power of the electric motor was 100hp. The results showed that the model allowed us to evaluate if a high efficiency motor was economically viable compared to the standard motor in each tariff structure. The high efficiency motor was not viable in the two tariff structures. In the green horo-seasonal tariff, would only be viable if its efficiency was 4.46% higher than the standard motor. In the conventional tariff, it would only be viable if the efficiency overcame 2.71%.

Identification of Agricultural Management Zones Through Clustering Algorithms with Thermal and Multispectral Satellite Imagery

2017 ◽  
Vol 25 (Suppl. 1) ◽  
pp. 121-140
Author(s):  
R. B. Arango ◽  
A. M. Campos ◽  
E. F. Combarro ◽  
E. R. Canas ◽  
I. Díaz
Keyword(s):  
Satellite Imagery ◽  
Precision Agriculture ◽  
Clustering Algorithms ◽  
Economic Benefits ◽  
Agricultural Management ◽  
Landsat 8 ◽  
Special Equipment ◽  
Management Zones ◽  
Ndvi Index ◽  
Cost Efficient

Precision Agriculture entails the appropriate management of the inherent variability of soil and crops, resulting in an increase of economic benefits and a reduction of environmental impact. However, site-specific treatments require maps of the soil variability to identify areas of land that share similar properties. In order to produce these maps, we propose a cost-efficient method that combines clustering algorithms with publicly available satellite imagery. The method does not require exploring the parcels with any special equipment or taking samples of the soil for laboratory analysis. The proposed method was tested in a case study for three vineyard parcels with topographical dissimilarities. The study compares different spectral and thermal bands from the Landsat 8 satellite as well as vegetation and moisture indices to determine which one produces the best clustering. The experimental results seem promising for identification of agricultural management zones. The findings suggest that thermal bands produce better clustering than those based on the NDVI index.

scholarly journals The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 846
Author(s):  
Mbulisi Sibanda ◽  
Onisimo Mutanga ◽  
Timothy Dube ◽  
John Odindi ◽  
Paramu L. Mafongoya
Keyword(s):  
Food Security ◽  
Precision Agriculture ◽  
Leaf Spot ◽  
Gray Leaf Spot ◽  
Economic Losses ◽  
Leaf Spot Disease ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maize Crop ◽  
Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

scholarly journals Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1365
Author(s):  
Tao Zheng ◽  
Zhizhao Duan ◽  
Jin Wang ◽  
Guodong Lu ◽  
Shengjie Li ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed ◽  
High Performance ◽  
High Efficiency ◽  
Semantic Segmentation ◽  
Raspberry Pi ◽  
Distance Transform ◽  
Testing Stage ◽  
Sampling Points ◽  
Information Sampling

Semantic segmentation of room maps is an essential issue in mobile robots’ execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943.

Coffee yield estimation by Landsat-8 imagery considering shading effects of planting row's orientation in center pivot

2021 ◽  
Vol 24 ◽  
pp. 100613
Author(s):  
Pedro Arthur de Azevedo Silva ◽  
Marcelo de Carvalho Alves ◽  
Fábio Moreira da Silva ◽  
Vanessa Castro Figueiredo
Keyword(s):  
Landsat 8 ◽  
Yield Estimation ◽  
Coffee Yield ◽  
Center Pivot ◽  
Shading Effects

Comparative analysis of three NDVI resolutions in different urban typologies. The case of Granollers in the Metropolitan Area of Barcelona.

2021 ◽  
Author(s):  
Alan García-Haro ◽  
Josep Roca
Keyword(s):  
Climate Change ◽  
Metropolitan Area ◽  
Urban Studies ◽  
Rural Areas ◽  
Industrial Area ◽  
Landsat 8 ◽  
Urban Context ◽  
Urban Core ◽  
First Results ◽  
Definition Of

<p>In recent years, the use of remote sensed NDVI has become recurrent in urban studies regarding the adaptation of cities to climate change. However, due to the physical diversity within cities and the different resolution offered by the sensors, the territorial interpretation of what the NDVI values really mean becomes difficult. Where the larger the size of the cells of the image, the greater the number of elements of the built environment within it, and the more complex the interpretation becomes.</p><p>In this work, the relationship between the NDVI of three sensors with different cell resolution for the same location and date is studied. In particular, the city of Granollers in the Metropolitan Area of Barcelona is analyzed. First, the NDVI images were obtained from Landsat-8 with 30m resolution, Sentinel-2 with 10m and from the Ministry of Agriculture, Livestock, Fisheries and Food of Catalonia (DARP) with 0.125m resolution. Then, the comparison was performed with a sample of five different typologies of the territory: dense urban core, suburban, industrial, area of highway and rural.</p><p>As first results, a supervised classification of the DARP image allowed the definition of 0.30 as the precise minimum value of NDVI that indicates the actual presence of vegetation. On the other hand, the comparison indicates that, in the urban context, the larger the cell size, the presence of vegetation quality is overestimated, where the higher percentage of cells is concentrated in higher NDVI values than in those with lower resolution. However, this behavior is not appreciated in rural areas, where higher percentages of cells of different resolutions were concentrated in the same NDVI ranges.</p><p>In such a way, it is corroborated that it is in the urban context where this indicator has a greater difficulty of territorial interpretation. Statements that are analyzed in greater depth in this study, where its implications in the use of NDVI in urban studies for the adaptation of cities to climate change are discussed.</p>

Analytical Head Loss Equation of Center-Pivot Irrigation System

2021 ◽  
Vol 147 (7) ◽  
Author(s):  
Amin Seyedzadeh ◽  
Amir Panahi ◽  
Eisa Maroufpoor ◽  
Abdolmajid Liaghat
Keyword(s):  
Irrigation System ◽  
Head Loss ◽  
Center Pivot
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