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A. Naor - The Golan Research Institute, University of Haifa, Israel

Today there is no systematic way to identify and locate failures of irrigation systems mainly because of the labor costs associated with locating the failures. The general aim of this study was to develop an airborne thermal imaging system for semi - automatic monitoring and mapping of irrigation system failures, specifically, of leaks and clogs.

Detection of real faults was attempted: a preliminary algorithm was developed to identify suspicious areas (suspected faults) based on the distribution of canopy temperature. To examine the accuracy and reliability of the algorithm five sites were selected with olive groves (Gshur and Revivim), vines (Lachish), palm dates (Kalia and Almog) and almonds (Lavi). This paper presents the results obtained from two sites with olive groves and one site with vines. Each site covered approximately 100 hectares. To access the accuracy and reliability of the algorithm, it was combined with the estimated potential savings in labor. The results indicated the following: 1. according to the map produced by the algorithm, 14-20% of the area has to be scanned, which corresponds to a 60% saving of the time needed to scan the whole area 2. The automatically detected suspicious areas contain at least 80% of the visible faults. 3. Most of the area that is detected for scan will not contain visible faults. However, it was found that in 70% of the locations suspected for leaks the trees indeed received excess water relative to their surroundings, and in 90% of the locations suspected for clogs the trees suffered from lack of water relative to their surroundings. That is, the map produced by the developed algorithm allows to save about 60% of the scan time, detects about 80% of the visible leaks, and detects leaks and clogs that are not visible with a reliability of 70% and 90% respectively.

Despite the benefits of the semi - automatic algorithm it requires input of four empirical parameters that can change its performance: minimum distance between pixels to create the histogram, size of pixels clusters associated with leaks and clogs, parameters associated with image enhancement (erosion) and allowed tolerance in estimating the location of the leak or clog. The algorithm has not yet been tested on palm dates and we intend to test it in the future.

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Automatic Detection And Mapping Of Irrigation System Failures Using Remotely Sensed Canopy Temperature And Image Processing

A. Naor - The Golan Research Institute, University of Haifa, Israel

Automatic Detection And Mapping Of Irrigation System Failures Using Remotely Sensed Canopy Temperature And Image Processing

Today there is no systematic way to identify and locate failures of irrigation systems mainly because of the labor costs associated with locating the failures. The general aim of this study was to develop an airborne thermal imaging system for semi - automatic monitoring and mapping of irrigation system failures, specifically, of leaks and clogs.

Detection of real faults was attempted: a preliminary algorithm was developed to identify suspicious areas (suspected faults) based on the distribution of canopy temperature. To examine the accuracy and reliability of the algorithm five sites were selected with olive groves (Gshur and Revivim), vines (Lachish), palm dates (Kalia and Almog) and almonds (Lavi). This paper presents the results obtained from two sites with olive groves and one site with vines. Each site covered approximately 100 hectares. To access the accuracy and reliability of the algorithm, it was combined with the estimated potential savings in labor. The results indicated the following: 1. according to the map produced by the algorithm, 14-20% of the area has to be scanned, which corresponds to a 60% saving of the time needed to scan the whole area 2. The automatically detected suspicious areas contain at least 80% of the visible faults. 3. Most of the area that is detected for scan will not contain visible faults. However, it was found that in 70% of the locations suspected for leaks the trees indeed received excess water relative to their surroundings, and in 90% of the locations suspected for clogs the trees suffered from lack of water relative to their surroundings. That is, the map produced by the developed algorithm allows to save about 60% of the scan time, detects about 80% of the visible leaks, and detects leaks and clogs that are not visible with a reliability of 70% and 90% respectively.

Despite the benefits of the semi - automatic algorithm it requires input of four empirical parameters that can change its performance: minimum distance between pixels to create the histogram, size of pixels clusters associated with leaks and clogs, parameters associated with image enhancement (erosion) and allowed tolerance in estimating the location of the leak or clog. The algorithm has not yet been tested on palm dates and we intend to test it in the future.

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