Identification of Water Stress in Citrus Leaves Using Sensing Technologies
AbstractWater stress is a serious concern in the citrus industry due to its effect on citrus quality and yield. A sensor system for early detection will allow rapid implementation of control measures and management decisions to reduce any adverse effects. Laser-induced breakdown spectroscopy (LIBS) presents a potentially suitable technique for early stress detection through elemental profile analysis of the citrus leaves. It is anticipated that the physiological change in plants due to stress will induce changes in the element profile. The major goal of this study was to evaluate the performance of laser-induced breakdown spectroscopy as a method of water stress detection for potential use in the citrus industry. In this work, two levels of water stress were applied to Cleopatra (Cleo) mandarin, Carrizo citrange, and Shekwasha seedlings under the controlled conditions of a greenhouse. Leaves collected from the healthy and stressed plants were analyzed using LIBS, as well as with a spectroradiometer (visible-near infrared spectroscopy) and a thermal camera (thermal infrared). Statistical classification of healthy and stressed samples revealed that the LIBS data could be classified with an overall accuracy of 80% using a Naïve-Bayes and bagged decision tree-based classifiers. These accuracies were lower than the classification accuracies acquired from visible-near infrared spectra. An accuracy of 93% and higher was achieved using a bagged decision tree with visible-near infrared spectral reflectance data.
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Johnson, K.; Sankaran, S.; Ehsani, R. Identification of Water Stress in Citrus Leaves Using Sensing Technologies. Agronomy 2013, 3, 747-756.
Johnson K, Sankaran S, Ehsani R. Identification of Water Stress in Citrus Leaves Using Sensing Technologies. Agronomy. 2013; 3(4):747-756.Chicago/Turabian Style
Johnson, Kaitlin; Sankaran, Sindhuja; Ehsani, Reza. 2013. "Identification of Water Stress in Citrus Leaves Using Sensing Technologies." Agronomy 3, no. 4: 747-756.