Dataset for "Low-Cost, Multi-Sensor Non-Destructive Banana Ripeness Estimation Using Machine Learning"

Abstract

Processed datasets containing all numerical sensor data used for training and testing the ML algorithms discussed in the associated publication. Data from temperature, pressure, humidity, VOC and spectral sensors is included. The data is split into four datasets (as defined in Table V of the associated publication), each containing a different combination of sensor data and each subdivided into data ("x") and labels ("y") for both testing and training data. 30% of the cleaned data is randomly taken to form the testing data, while the remaining 70% forms the training data. Each data subset is balanced, as discussed in section 3.E.3 in the associated publication.The data collection methodology can be found in the associated publication.The data preparation & processing methodology can be found in the associated publication.The datasets were created with Python 3.10.13, with libraries Numpy 1.26.0 and Pandas 2.1.2. The data is saved in CSV format and does not require specialist software to read.Data organisation and encoding is described in the associated ReadMe files