Drying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (D_eff) were calculated in these dryers. Accordingly, the best result of the D_eff (3.14 × 10⁻¹⁰ m²/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of D_eff (2.09 × 10⁻¹¹ m²/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical Applications: Recently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

In recent years, global warming, climate change, and carbon emissions have emerged due to the uncontrolled use of fossil fuels and the lack of widespread use of renewable energy sources on a global scale. This research investigated specific energy consumption (SEC) and greenhouse gases (GHG) emissions (carbon dioxide [CO₂] and nitrogen oxides [NO_X]) during the drying of pear samples by five different types of dryers, namely, convective (CV), infrared (IR), microwave (MW), combined IR/CV, and MW/CV. Moreover, the quality properties of dried pears, such as shrinkage, rehydration (RR), and color change were determined. The results showed that the highest shrinkage (72.53%) and color change (ΔE = 33.41) values were obtained in CV drying at 50 °C and thickness of 2 mm and IR dryer 1000 W and thickness of 6 mm, respectively. The greatest rehydration rate (4.25) was also determined in MW/CV drying at 450 W and an air temperature of 60 °C. The lowest SEC was observed for the MW/CV dryer with a power of 630 W and air temperature 70 °C (20.25 MJ/kg), while the highest SEC (267.61 MJ/kg) was obtained in the CV drying (50 °C, sample thickness of 6 mm). The highest CO₂ and NO_X emissions (GT-GO power plant) were 280.45 and 1.55 kg/kg water in the CV dryer at 50 °C and a thickness of 6 mm. In conclusion, the increases in IR and MW power and temperatures led to reduced CO₂ and NO_X emissions, while the increases in sample thickness led to increase CO₂ and NO_X emissions.