An Analysis of the Characteristics and Data Correction of Real-Time Water Use Data

2021 ◽  
Vol 29 (3) ◽  
pp. 131-152
Author(s):  
Jung-Hwan Yun ◽  
Junhyeong Lee ◽  
Younghoon Yoo ◽  
Won-joon Wang ◽  
Hung Soo Kim
Keyword(s):  
2019 ◽  
Vol 11 (22) ◽  
pp. 2645 ◽  
Author(s):  
Daniel Freeman ◽  
Shaurya Gupta ◽  
D. Hudson Smith ◽  
Joe Mari Maja ◽  
James Robbins ◽  
...  

As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use.


HortScience ◽  
2020 ◽  
Vol 55 (1) ◽  
pp. 83-88 ◽  
Author(s):  
Jeff B. Million ◽  
Thomas H. Yeager

Two experiments were conducted to determine if a leaching fraction (LF)-guided irrigation practice with fixed irrigation run times between LF tests (LF_FX) could be improved by making additional adjustments to irrigation run times based on real-time weather information, including rain, using an evapotranspiration-based irrigation scheduling program for container production (LF_ET). The effect of the two irrigation practices on plant growth and water use was tested at three target LF values (10%, 20%, and 40%). For both Viburnum odoratissimum (Expt. 1) and Podocarpus macrophyllus (Expt. 2) grown in 36-cm-diameter containers with spray-stake microirrigation, the change in plant size was unaffected by irrigation treatments. LF_ET reduced water use by 10% compared with LF_FX in Expt. 2 but had no effect (P < 0.05) on water use in Expt. 1. Decreasing the target LF from 40% to 20% reduced water use 28% in both experiments and this effect was similar for both irrigation practices. For the irrigation system and irrigation schedule used in these experiments, we concluded that an LF-guided irrigation schedule with a target LF of 10% resulted in plant growth similar to one with a target LF of 40% and that the addition of a real-time weather adjustment to irrigation run times provided little or no improvement in water conservation compared with a periodic adjustment based solely on LF testing.


2020 ◽  
Author(s):  
Maria Mar Alsina ◽  
Kyle Knipper ◽  
Martha Anderson ◽  
WIlliam Kustas ◽  
Nicolas Bambach ◽  
...  

&lt;p&gt;Grapevines are one of the major drivers of agriculture in California, representing a production equivalent to $6.25 billion in 2018. Water is scarce, and increasingly intense and prolonged drought periods, like one that recently occurred in the 2012-2016 period, may happen with greater frequency. Consequently, there is a need to develop irrigation management decision tools to help growers maximize water use while maintaining productivity. Furthermore, grapevines are deficit irrigated, and a correct management of the vine water status during the season is key to achieve the target yield and quality. Traditionally, viticulturists use visual clues and/or leaf level indicators of vine water status to regulate the water deficit along the season. However, these methods are time-consuming and only provide discrete data that do not represent the often-high spatial variability of vineyards. &amp;#160;Remote sensing techniques may represent a fast real-time decision-making tool for irrigation management, able to extensively cover multiple vineyards with low human or economic investments.&amp;#160;&lt;br&gt;While growers currently calculate the vine water demands using the reference evapotranspiration from a weather station located in the region and a crop coefficient, usually from literature, they don't have any means to measure or estimate the actual water used by the vines. Knowing the actual evapotranspiration (ET) in real-time and at a sub-field scale would provide essential information to monitor vine water status and adjust the irrigation amounts to the real water needs. The aim of the GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) project, has been to provide growers with an irrigation toolkit that integrates the spatial distribution of vine water use and water status. The project focuses on grapevines, but it will be easily extrapolated to orchards and other crop types.&lt;br&gt;We present the results of a pilot experiment where we applied the scientific developments of the GRAPEX project into a practical tool that growers can use for irrigation management. We run this pilot experiment over 6 commercial grapevine blocks, located in Cloverdale, Sonoma, CA. During the 2019 growing season, we provided the viticulturists with weekly maps of actual ET calculated using the DisALEXI model, Sentinel-2 Normalized Difference Vegetation and Normalized Vegetation Water Indices as well as local weather data, forecasted ET and soil moisture. The data were delivered weekly in a dashboard, including spatial and tabular views, as well as an irrigation recommendation derived from the past week's vine water use and water status data. Along with the remote sensing data, we took periodic measurements of leaf area index, leaf water potential, and gas exchange to evaluate the irrigation practices. We compared the irrigation prescription based on the provided data with the grower's practices. The total season irrigation ranged between 70 and 120 mm depending on the block, and our irrigation recommendations deviated between 10 and 30 mm from the growers' practices, also depending on the block. This analyzes the performance of the ET toolkit in assisting irrigation scheduling for improving water use efficiency of the vineyard blocks.&lt;/p&gt;


2019 ◽  
Vol 49 (2) ◽  
pp. 172-184 ◽  
Author(s):  
Joanne L Tingey-Holyoak ◽  
John Pisaniello ◽  
Peter Buss ◽  
Ben Wiersma

Primary producers need strategies and tools to assist in monitoring water use with a view to improving physical and financial productivity. The purpose of this research is to integrate farmer financial accounting data with soil moisture and climate data to better account for water use on farm. Farm-accounting systems, if present, lack the sophistication to allow growers to analyze use, loss, and productivity of water. Water-accounting technologies, if present, do not readily link to business systems to provide the optimal real-time financial decision-making data, nor the necessary context for new technologies to support a broader integrated approach to water management. Findings of desk-based technology benchmarking suggest elements required include real-time sensory data integration that allows for strategic allocation to the full suite of direct and indirect water costs. Key actor interview and producer surveys highlight demand for a farm business integrated water productivity tool and findings from field data collected in a potato case study provide demonstration of how irrigation decision-making can be supported by the crucial link between producers’ business systems and sensing technology.


2001 ◽  
Vol 44 (7) ◽  
pp. 17-22
Author(s):  
K. Yamada ◽  
T.-S. Kim ◽  
K. Nakamura ◽  
J. Nomura

In this research, we installed the storm water storage tank, which has three functions: pollutant control, flood control and water use, to the end pipe of a separate system. We examined the effect of real time control (RTC) introduction with the scenario selection in the study area in the catchment basin, which has measured data. As a result, a latter period centering-type case is satisfied with the pollutant reduction by the RTC and also at the water use tank, the best control settles COD concentration at about 0.45 mg/l. It was clarified how to use a RTC method as a measure of the discharge problem from an urban area during a storm event.


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