Application of neural networks in predicting the qualitative characteristics of fruits

In this research, the quality properties of persimmon were predicted using artificial intellect techniques. The persimmon samplewere transferred to a computer vision lab, room temperature of 24 °C and 22% RH. The samples were divided into three groupfor temperature treatment. They were kept at three temperature levels of 5 °C, 15 °C, and 24°C (control group) for 72 hourThe sample was then placed at room temperature and was imaged every second day for a 14 day period. After imaging, eacsample underwent destructive tests to determine their quality attributes, including sugar content, firmness, and pH. The resultindicate that the neural network’s predicted values of acidity, firmness, and sugar of persimmon were not statistically significandifferences from their actual values. In predicting the acidity of persimmon, the sugar RMSE is more than the two factors ofirmness and acidity. For this reason, the accuracy of firmness and acidity is higher than sugar. MAPE is 10.11, 20.81, and 6.0for acidity, firmness, and sugar, respectively. The model for sugar indicates a high difference between the actual values and thpredicted values.