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The coverage of center pivot irrigation systems used around the world has increased. One potential factor driving their adoption is improved water application efficiency relative to some other sprinkler or surface irrigation approaches. Center pivot irrigation systems may be further improved by dynamic elevation spray application (DESA). DESA systems adjust the nozzle height in response to plant growth and canopy heterogeneities. The DESA approach is relatively new and there is uncertainty in its economic viability and worthiness of further investigation. Thus, an economic scenario analysis was performed to explore the potential economic benefits of DESA based on permutations of irrigation pivot efficiency without DESA, water-saving potential of DESA, and water cost. The weighted costs and benefits of the height-adjusted approach for a set of water cost savings scenarios showed the net return price with the water cost savings per season. We show that DESA could have economic viability at current component costs and is worthy of further investigation and refinement.
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Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks
Being hailed as the greatest mechanical innovation in agriculture since the replacement of draft animals by the tractor, center pivot irrigation systems irrigate crops with a significant reduction in both labor and water needs compared to traditional irrigation methods, such as flood irrigation. In the last few decades, the deployment of center pivot irrigation systems has increased dramatically throughout the United States. Monitoring the installment and operation of the center pivot systems can help: (i) Water resource management agencies to objectively assess water consumption and properly allocate water resources, (ii) Agro-businesses to locate potential customers, and (iii) Researchers to investigate land use change. However, few studies have been carried out on the automatic identification and location of center pivot irrigation systems from satellite images. Growing rapidly in recent years, machine learning techniques have been widely applied on image recognition, and they provide a possible solution for identification of center pivot systems. In this study, a Convolutional Neural Networks (CNNs) approach was proposed for identification of center pivot irrigation systems. CNNs with different structures were constructed and compared for the task. A sampling approach was presented for training data augmentation. The CNN with the best performance and less training time was used in the testing area. A variance-based approach was proposed to further locate the center of each center pivot system. The experiment was applied to a 30-m resolution Landsat image, covering an area of 20,000 km2 in North Colorado. A precision of 95.85% and a recall of 93.33% of the identification results indicated that the proposed approach performed well in the center pivot irrigation systems identification task.
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- Award ID(s):
- 1739705
- PAR ID:
- 10076265
- Date Published:
- Journal Name:
- Agriculture
- Volume:
- 8
- Issue:
- 10
- ISSN:
- 2077-0472
- Page Range / eLocation ID:
- 147
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/agriculture8100147
