Detection of water stress level in romaine lettuce plants through CNN
Abstract
Agriculture in Mexico is dealing with significant challenges in the area of water. An alternative to address this problem is the implementation of modern agricultural techniques that allow precision farming methods in greenhouses that can produce during all seasons of the year and with more efficient use of water. For this, intelligent systems are necessary to monitor and control the resources for plant growth according to the conditions they present. This article describes the design and training of a convolutional neural network (CNN) to detect the degree of dehydration of romaine lettuce plants through images. The experiments show 83% precision and sensitivity of the CNN model in identifying the level of dehydration, and 98.8% in both metrics, considering a tolerance of plus/minus one level of difference concerning the real one.
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References
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Copyright (c) 2023 Jesús Guillermo Bermúdez-Rojas, Jorge Enrique Luna-Taylor, Fernando Daniel Von-Borstel-Luna, Jesús Alberto Sandoval-Galarza
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