TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification of water stress in cultured Sunagoke moss using deep learning

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Classification of water stress in cultured Sunagoke moss using deep learning

Convolutional neural network
Cultured Sunagoke moss
Machine vision
Non-destructive sensing
Water stress

Water stress greatly determines plant yield as it affects plant metabolism,
photosynthesis rate, chlorophyll content index, number of leaves,
physiological, biochemical compound, and vegetative growth. The research
aimed to detect and classify water stress of cultured Sunagoke moss into
several categories i.e. dry, semi-dry, wet, and soak by using a low-cost
commercial visible light camera combined with a deep learning model.
Cultured Sunagoke moss is a commercial product which has the potential use
as rooftop-greening and wall-greening material. This research compared the
performance of four convolutional neural network models, such as
SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional
neural network model according to the training and validation result was
ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this
also gained an accuracy rate at 87.50%. However, the best result of the
convolutional neural network model on data testing using confusion matrices
on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128
mini-batch size, and average testing accuracy of 94.15%. It can be concluded
that based on the overall results, convolutional neural network model seems
promising as a smart irrigation system that real-time, non-destructive, rapid,
and precise method when controlling water stress of plants.

Yusuf Hendrawan, Retno Damayanti, Dimas Firmanda Al Riza, Mochamad Bagus Hermanto

http://journal.uad.ac.id/index.php/TELKOMNIKA

Aug 3, 2021

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