Vision-based Moisture Prediction for Food Drying

This study presents a vision-based method to predict the moisture ratio of a kiwifruit slice in a dryer. Firstly, an automated image processing workflow was used to extract colour and morphology features from drying kiwifruit slices. These features, along with pretreatment methods, slice thickness, and drying temperature, were then modelled using a random forest regression. The model exhibited exceptional performance, with an average Mean Absolute Error (MAE) of 0.0056 and an average Root Mean Square Error (RMSE) of 0.0312. The R-2 values remained consistently high across all folds, averaging 0.9879, indicating a substantial proportion of variance in the data explained by the model. Lastly, this study introduces a model-based drying control system, where the moisture ratio prediction method plays a pivotal role. This system eliminates the need for strict control over parameters such as fruit type, slicing state, or drying temperature by adhering to an automated optimal drying profile, thus reducing ownership costs and enhancing yield rates.