## 3 Results and Discussion
### Other Algorithms in Artificial Intelligence vs. Drying
The best results were observed precisely in the differentiation of dried carrots obtained by convective drying (R\({}^{2}\) = 0.998). [PERSON] et al. (2022) [115] carried out an experiment on apples in the process of convective drying. The process of drying was performed at the temperature of about 50, 60 and 70 \({}^{\circ}\)C with the air velocity of 1, 2 and 3 m\(\cdot\)s\({}^{-1}\). As a result, a deep neural network (DNN) was developed based on 4526 groups of apple slice drying data. It was used to predict changes in moisture ratio (MR), for which R\({}^{2}\) = 0.998, and dry matter (DBMC), where R\({}^{2}\) = 1.00. As it was mentioned before, [PERSON], 2022 [64] conducted a number of studies aimed at predicting the moisture ratio in the drying process of the two varieties of oranges. Apart from the aforementioned typology, the author took advantage of the other methods of learning, such as support vector regression (SVR), gaussian processes (GP), random forest and k-NN. Each of the above algorithms demonstrated a high coefficient of prediction effectiveness of dried orange fruits. The results of predicting the speed of drying showed that k-NN obtained the highest levels of R\({}^{2}\) at the level of 1.0000 and 0.9954 for the Navel and Valencia varieties, respectively. [PERSON] et al. (2022) [60], as mentioned above in this study, used deep artificial neural networks to improve classification performance using image texture based on GLCM. In this case, the network analyzing the images was expressed in eight-bit grayscale, and this translated into achieving 100% four-class classification quality. This leads us to believe that the deep applied networks with MobileNet architecture seek solutions to the classification problem within the boundaries defined in the considered hyperspace by the training set. This increases the reliability of the results returned by the obtained models. MobileNet (grayscale) for four-class classification also achieved high values of testing and validation quality. In the test set, the best-discriminated class was class 4 (with a temperature of 90 \({}^{\circ}\)C during drying), for which 83% of cases were classified correctly. The remaining 18% of misclassified cases were evaluated by the network as class 3 (including drying temps of 80 \({}^{\circ}\)C). For the validation set, 78% of the validation cases belonging to class 3 were classified correctly, and the mistaken cases were evaluated as belonging to classes 1 and 2. Seventy-two percent of the cases belonging to class 2 were classified correctly, while twenty-two percent were mistaken for class 3. This indicates some similarity in the texture of the cases from classes 2 (drying temp. equals 70 \({}^{\circ}\)C) and 3. Only 6% of the cases were falsely assigned to class 1 (drying temp. equals 60 \({}^{\circ}\)C). Seventy-eight percent of cases belonging to Class 1 were classified correctly, while seventeen percent were falsely assigned to Class 2. Only 6% were misclassified as Class 3. \begin{table}
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In summary, the above research confirms the high efficiency of matching various AI methods, which translates into obtaining excellent forecasting or classification results obtained as a result of drying vegetables and fruits. The value of the fit coefficient below 0.7 would mean that the discrepancies between the results of the designed AI methods and the model results for the network are not fully satisfactory in research work. A fit quality factor below 0.6 would mean that the fit of the model is not sufficient for ANN methods to be able to independently decide on the qualification of dried vegetables and fruits with sufficient accuracy.