Development of a machine learning plant growth estimator for hydroponics
Abstract
Growth stage estimation in indoor farming is crucial for precision agriculture. It reduces
the wastage of resources such as nutrients and water in plant cultivation. Overly, yield
and quality of the agricultural produce are improved while minimising running costs.
However, plants at different growth stages pose similar morphological shapes, making
growth stage estimation complex.
Therefore, an algorithm that can investigate other features of plant leaf besides shape is
needed to distinguish plant growth stages accurately. In this work, we suggested three
approaches: first, a machine learning approach that combines morphological operators to
generate a contour mask dataset that highlights the morphological leaf structures, Gabor
filters for feature extraction, and traditional machine learning algorithms for
classification, second, a deep learning approach through transfer learning, and third, an
approach that uses a feature extractor based on deep learning techniques and a classifier
based on traditional machine learning techniques. Side-by-side experiments are run to
evaluate the performance of the three proposed approaches In addition, two datasets
(canola and radish) were created from a publicly available dataset as "bccr-segset" to
develop and test these approaches.
We found that the third approach of using a feature extractor based on deep learning
techniques and a classifier based on traditional machine learning techniques can classify
four plant growth stages with the highest accuracy of 98.4% and a faster average
classification time per image of 0.07 seconds. On the other hand, the first approach, the
machine learning method, achieved slightly lower classification accuracy than the deep
learning models but had the shortest classification time per image of 0.04 seconds.
Finally, we compared our results with similar research studies and found out that our
proposed approaches compete well with these studies.
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