Artificial Neural Networks and Thermal Image for Temperature Prediction in Apples
2016
Badia-Melis, R. | Qian, J. P. | Fan, B. L. | Hoyos-Echevarria, P. | Ruiz-García, L. | Yang, X. T.
The inability to correctly implement and safeguard a product cold chain leads to premature product spoilage and increased product waste. Special care is required to both implement and monitor the cold chain for perishable goods in order to preserve them. Many technologies are available on the market today with varying levels of success. This article presents a new technique, namely thermal imaging predicts surface temperature over a pallet of apples whilst comparing packaging (plastic boxes and cardboard boxes). This temperature data was then introduced as an input in artificial neural network (ANN) software to estimate the temperature across the entire pallet. Results obtained (root mean squared error [RMSE]) indicate that the estimation with plastic boxes has an error of 0.41 °C whilst the error, taking as a reference the surface temperature, would be RMSE 2.14 °C. In the case of cardboard boxes, the estimation error is 0.086 °C whilst only taking into account the thermal image, data would be RMSE 3.56 °C. This article proves the concept of the possibility of temperature monitoring by ANN through thermal imaging technology.
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