Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study |
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| Authors: | R Zakaluk R Sri Ranjan |
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| Institution: | (1) Civil Engineering Technology Department, Red River College, Winnipeg, MB, R3H 0J9, Canada;(2) Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada |
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| Abstract: | Plant water status information of potato (Solanum tuberosum L. cv. Russet Burbank) is needed at the farm level for irrigation scheduling. This research investigated the feasibility
of using a 5-megapixel digital camera to determine the leaf water potential (ΨL) of potato plants by capturing red, green, blue (RGB) digital images in the visible region of the electromagnetic spectrum.
A greenhouse experiment was conducted in containerized cv. Russet Burbank potato plants subjected to five soil nitrate-nitrogen
(N) levels and four soil water content levels. An artificial neural network (ANN) model, built with RGB images, RGB image
transformations, RGB vegetation indices, and principal components analysis, found that for the validation data set, the measured
ΨL and predicted ΨL results were from common populations. Other results showed: (1) a linear trend between soil nitrate-N levels and leaf reflectance
in the G image band, (2) that the RG image bands were more suitable than the B image band for classifying leaf pigment from
leaf shadow and leaf damage, (3) soil nitrate-N interacted with leaf greenness, affecting ΨL prediction, and (4) some image variables were more important than others in the ANN model. Although this greenhouse research
shows promise, further field-based research is required to validate the selection of input neurons used and also validate
the use of ANN modelling to determine ΨL at the plant canopy level with cv. Russet Burbank and other cultivars. In addition, an image acquisition method needs to
be developed to obtain periodic representative sample coverage over a field. |
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| Keywords: | Chromaticity transformation IHS transformation Nitrogen Plant water Principal components Remote sensing Vegetation indices |
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