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Taryn L. Bauerle, William L. Bauerle, Marc Goebel, and David M. Barnard

soil moisture sensor variability. We hypothesized that high variability in fine root density will result in high sensor variability. Additionally, we predicted that species with a high proportion of course roots would result in high sensor variability

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Rhuanito Soranz Ferrarezi, Sue K. Dove, and Marc W. van Iersel

labor ( USDA, 2010 ), and technological innovations, which reduce labor dependency, can help growers stay competitive. Automation can be achieved using technologies such as robots, sensors, and computer-controlled systems, which may also improve plant

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Alex B. Daniels, David M. Barnard, Phillip L. Chapman, and William L. Bauerle

sensors have become commercially available (e.g., Decagon Inc. Model EC-5; Irrometer Inc. Model Watermark 200SS). By logging the sensors with wireless nodes, substrate moisture data can be broadcast throughout a horticulture operation and transmitted to a

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Changying Li, Pengcheng Yu, Fumiomi Takeda, and Gerard Krewer

-528). Technical details of sensor design, development, and evaluation have been published in specialized papers ( Yu et al., 2011a , 2011b , 2012 ). Smart Berry Overall, the Smart Berry sensing system consists of three essential components: the sensor, the

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Carmen Mena, Alejandra Z. González, Raúl Olivero-David, and María Ángeles Pérez-Jiménez

Virgin olive oil, the main dietary fat in Mediterranean countries, differs from other edible oils because of its healthy properties and sensorial qualities. It is a genuine fruit juice, containing a high level of natural antioxidants associated with

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Lloyd L. Nackley, Elias Fernandes de Sousa, Bruno J.L. Pitton, Jared Sisneroz, and Lorence R. Oki

( de Lima et al., 2015 ; Marin et al., 2016 ; Nemali and van Iersel, 2008 ). Optimized irrigation management requires properly quantifying the water volume to maximize plant growth without wasting this critical resource. Sensor-based control systems

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Dalyn McCauley, Alexander Levin, and Lloyd Nackley

to evaluate and trigger the next system inputs. For lysimeter-controlled irrigation, this is done by using feedback from sensors that measure the change in weight of a container to inform the timing and duration of irrigation events. The primary

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Ana Centeno, Pilar Baeza, and José Ramón Lissarrague

availability. Some of them estimate volumetric soil water content [time-domain reflectometer (TDR) and neutron probe], while others measure matric water potential/soil moisture tension (tensiometer, gypsum blocks, and granular matrix soil moisture sensor). It

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Susmitha Nambuthiri, Ethan Hagen, Amy Fulcher, and Robert Geneve

estimate periodic water loss using sensors or physical methods ( Jones, 2004 ; van Iersel et al., 2013 ). Advanced irrigation scheduling methods have to be developed to address the concerns of the green industry. Environmental models have been used to

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Marc W. Van Iersel, Sue Dove, Jong-Goo Kang, and Stephanie E. Burnett

to 2 d before visible symptoms occur ( Blom-Zandstra and Metselaar, 2006 ; Kacira et al., 2002 ). Drawbacks of using sensors to detect plant stress for irrigation control are that such methods do not indicate how much water should be applied and that