Abstract: |
The procedure of processing the vermicompost production includes several stages, where the
vermicompost material has different temperatures during these different stages. Thermal sensors play a key
role in numerous fields, such as medical and agricultural applications. Thermal cameras can produce a
thermal image or an array of values representing the array of sensory data. i.e., an array of temperatures.
In this study, we proposed the first thermal imagery dataset of the vermicompost production process. The
contributions of this work are two-fold using the proposed dataset. First, we framed the process of predicting
the vermicompost production process as a classification problem. Second, we compared classifying the
different stages of the process of vermicompost production based on two different input types, namely,
thermal images and an array of temperatures. In other words, the classifier will be fed with an input (an
image or an array of temperatures), and then the classifier will predict the vermicompost production stage.
In this context, we utilized several machine and deep learning models as classifiers. For the utilized dataset,
the study has been conducted on a set of images collected during the vermicompost production procedure
which was collected every 14 days over 42 consecutive days, i.e., four classes. We proposed running a series
of experiments to determine which input type yields better classification accuracy. The obtained results show
that using thermal images for the sake of classifying the vermicompost production stages achieved higher
accuracy, about 92%, in comparison to using the sensor array data, about 60%.
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