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In this study, three different apple cultivars were dried using five different drying methods and moisture ratio (MR), moisture content (MC) and drying rate values were determined. Then, different machine learning algorithms (artificial neural network, k‐nearest neighbors, random forest, gaussian processes, and support vector regression) were used to estimate MR, MC and drying rate. For MR estimation of Golden Delicious, Oregon Spur and Granny Smith cultivars, Random Forest was most successful algorithm with correlation coefficients (R) of 0.9800, 0.9873, and 0.9841, respectively. This was followed by SVR with R: 0.9323 for Golden Delicious, ANN with R: 0.9766 for Oregon Spur and 5‐NN with R: 0.9827 for Granny Smith. MC and drying rate estimation results showed that RF, SVR, and k‐NN achieved higher R for all cultivars. It was concluded that machine learning algorithms are an effective approach for the accurate estimation of the drying characteristics of apple slices. Practical applications Machine learning‐like precise modeling techniques are used to estimate the drying characteristics of agricultural commodities. Models should be assessed and compared for optimization of drying conditions and operational costs. Machine learning models predictions agreed well with testing data sets and they could be useful for understanding and controlling the factors affecting drying behaviors.

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Size, mass, and shape attributes play a significant role in the quality assessment and post-harvest technologies of agricultural products. Pistachio is widely consumed worldwide, and Turkey has 3rd place in world pistachio production. In this study, physical attributes of 6 different pistachio cultivars (Beyaz Ben, Keten gömleği, Kirmizi, Siirt, Tekin, Uzun) were determined and machine learning algorithms (Multilayer Perceptron (MLP), k-Nearest Neighbor (kNN), Random Forest (RF), Gaussian processes (GP)) were used for mass prediction of these pistachio cultivars. Siirt and Tekin cultivars had the greatest gravitational and dimensional attributes. Among the pistachio cultivars, Kirmizi and Uzun had the greatest shape index and elongation values. Keten gömleği and Beyaz cultivars had the lowest averages of mass and area attributes both for nuts and kernels. Kernel and nut mass of pistachio had significant correlations with volume, geometric mean diameter, and projected and surface area (p < 0.01). Present findings revealed that Gaussian processes had the greatest correlation coefficients (0.976 for nut mass and 0.948 for kernel mass prediction) and the lowest RMSE values (0.038 for nut and 0.029 for kernel mass prediction). This algorithm was respectively followed by Multilayer Perceptron and Random Forest algorithms. Present findings revealed that Gaussian processes, Multilayer Perceptron, and Random Forest algorithms could potentially be used for mass prediction of pistachio cultivars.

BACKGROUND Drying is one of the most important post‐harvest processes in order to ensure that the products harvested in certain periods are consumed throughout the year. In this study, Golden Delicious, Oregon Spur and Granny Smith apple cultivars (fallen apples) were dried in open‐sun, a controlled glasshouse, microwave oven (200 W), hybrid system (100 W + 60 °C), convective greenhouse (70 °C) and freeze‐dryer (−55 °C) with and without ultrasound pretreatments. RESULTS Present findings revealed that the longest drying duration was observed in freeze‐drying and the shortest in microwave drying. The greatest color change (ΔE) was observed in ultrasound pretreated Oregon Spur apples dried in a controlled glasshouse. The greatest total phenolics (40.08 mg GAE g−1), DPPH (2,2‐diphenyl‐1‐picrylhydrazyl) inhibition activity (46.96%), FRAP (ferric reducing ability of plasma) antioxidant activity (68.01 μg Trolox g−1) and ascorbic acid content (1.52 mg AAE g−1) were observed in open sun‐dried Granny Smith apples without ultrasound pretreatment. CONCLUSIONS Generally, microwave and hybrid drying yielded better outcomes for bioactive, HMF (5‐hydroxymethyl‐2‐furfural) and drying characteristics of windfall apples. © 2022 Society of Chemical Industry.