Drying is one of the oldest methods for food preservation that removes the water from fruit and makes it available for consumption throughout the year. Dried fruits can be produced by small- and large-scale processors, which makes them a very popular food among consumers and food manufacturers. The most frequent uses of drying technology include osmotic dehydration, vacuum drying, freeze-drying and different combinations of other drying technologies. However, drying may provoke undesirable changes with respect to physiochemical, sensory, nutritional and microbiological quality. Drying process energy efficiency and the quality of dried fruits are crucial factors in fruit drying. Recently, innovative technologies such as ultrasound, pulsed electric field and high pressure may be used as a pretreatment or in combination with traditional drying technologies for process intensification. This could result in quality improvements of dried fruits and enhanced efficiency and capacity of the production process, with a positive impact on environmental and economic benefits.

Raspberries are one of Serbia’s best-known and most widely exported fruits. Due to market fluctuation, producers are looking for ways to preserve this fresh product. Drying is a widely accepted method for preserving berries, as is the case with freeze-drying. Hence, the aim was to evaluate convective drying as an alternative to freeze-drying due to better accessibility, simplicity, and cost-effectiveness of Polana raspberries and compare it to a freeze-drying. Three factors were in experimental design: air temperature (60, 70, and 80 °C), air velocity (0,5 and 1,5 m $·$ s⁻¹), and state of a product (fresh and frozen). Success of drying was evaluated with several quality criteria: shrinkage (change of volume), color change, shape, content of L-ascorbic acid, total phenolic content, flavonoid content, anthocyanin content, and antioxidant activity. A considerable influence of convective drying on color changes was not observed, as ΔE was low for all samples. It was obvious that fresh raspberries had less physical changes than frozen ones. On average, convective drying reduced L–ascorbic acid content by 80.00–99.99%, but less than 60% for other biologically active compounds as compared to fresh raspberries. Convective dried Polana raspberry may be considered as a viable replacement for freeze-dried raspberries.

This study aimed to determine the effects of osmotic dehydration on the kinetics of hot air drying of apricot halves under conditions that were similar to the industrial ones. The osmotic process was performed in a sucrose solution at 40 and 60 °C and concentrations of 50% and 65%. As expected increased temperatures and concentrations of the solution resulted in increased water loss, solid gain and shrinkage. The kinetics of osmotic dehydration were well described by the Peleg model. The effective diffusivity of water 5.50–7.387 × 10⁻⁹ m²/s and solute 8.315 × 10⁻¹⁰–1.113 × 10⁻⁹ m²/s was calculated for osmotic dehydration. Hot air drying was carried out at 40, 50, and 60 °C with air flow velocities of 1.0 m/s and 1.5 m/s. The drying time shortened with higher temperature and air velocity. The calculated effective diffusion of water was from 3.002 × 10⁻¹⁰ m²/s to 1.970 × 10⁻⁹ m²/s. The activation energy was sensitive to selected air temperatures, so greater air velocity resulted in greater activation energy: 46.379–51.514 kJ/mol, and with the osmotic pretreatment, it decreased to 35.216–46.469 kJ/mol. Osmotic dehydration reduced the effective diffusivity of water during the hot air drying process. It also resulted in smaller shrinkage of apricot halves in the hot air drying process.

Freeze-drying has proven to be one of the best drying processes thus far. The drying process itself lasts considerably longer than any other drying method. This paper makes a comparison of the energy consumption and time required for drying 100 kg of raspberries using lyophilization and convective drying. The calculations of energy consumption were performed for both drying methods, as well as their subsequent comparison. When calculating the energy consumption, special attention was paid to all consumers involved in the drying process (both directly and indirectly). The results obtained show that the energy consumption in the freeze-drying of raspberries was higher than that recorded in the convective drying method. The freeze-drying time was slightly more than 3 times longer than the convective drying time. Relative to the energy consumption and drying duration, the convective drying process was found to be far more efficient than the freeze-drying process.

Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) and Red-Green-Blue (RGB) imagery. Research analyzes five European wheat cultivars across 400 experimental plots created by combining 20 nitrogen, phosphorus, and potassium (NPK) fertilizer treatments. Yield variations from 1.41 to 6.42 t/ha strengthen model robustness with diverse data. The ML approach is automated using PyCaret, which optimized and evaluated 25 regression models based on 65 vegetation indices and yield data, resulting in 66 feature variables across 400 observations. The dataset, split into training (70%) and testing sets (30%), was used to predict yields at three growth stages: 9 May, 20 May, and 6 June 2022. Key models achieved high accuracy, with the Support Vector Regression (SVR) model reaching R² = 0.95 on 9 May and R² = 0.91 on 6 June, and the Multi-Layer Perceptron (MLP) Regressor attaining R² = 0.94 on 20 May. The findings underscore the effectiveness of precisely measured MS indices and a rigorous experimental approach in achieving high-accuracy yield predictions. This study demonstrates how a precise experimental setup, large-scale field data, and AutoML can harness UAV and machine learning’s potential to enhance wheat yield predictions. The main limitations of this study lie in its focus on experimental fields under specific conditions; future research could explore adaptability to diverse environments and wheat varieties for broader applicability.

Summary: Precision agriculture has increasingly incorporated the use of Unmanned Aerial Vehicles (UAVs) equipped with multispectral cameras. This study examined the influence of different UAV flight altitudes on the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge (NDRE), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Leaf Chlorophyll Index (LCI), indices critical to crop monitoring and health assessment. The experiment was conducted on a 2-hectare winter wheat field at the Institute of Field and Vegetable Crops in Novi Sad, Serbia. The field was divided into 400 plots, each containing different wheat varieties subjected to twenty distinct combinations of artificial mineral fertilizer (NPK) treatments. A DJI P4 Multispectral drone was employed to capture images at altitudes of 30, 60, and 90 meters on three separate dates, corresponding to different plant growth stages: May 9, May 20, and June 6, 2022. All other operating parameters were held constant. The data were processed using the DJI Terra and Pix4D software to generate orthomosaic maps, which were subsequently analyzed using ArcGIS (v10.5, ESRI, Redlands, CA, USA) to calculate the multispectral index values for each plot. The results were statistically analyzed using the STATISTICA Tibco software. The analysis revealed significant differences in the index values based on the UAV flight altitude (p < 0.05). This research underscores the centrality of selecting the optimal UAV flight altitude to ensure the accuracy and reliability of data. While higher altitudes enable UAVs to cover larger areas in a single flight, factors such as image resolution, wind conditions, and the precision of crop health indicators must be considered. These findings offer valuable insights for agricultural professionals seeking to improve crop monitoring and ultimately enhance agricultural productivity through more effective UAV deployment.