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.

Modern agriculture necessitates the use of techniques and tools that pollute the environment less and improve the safety of food and feed production. In the field of plant protection, drones are attracting increasing attention due to their versatility and applicability in a variety of environmental and working conditions. Drone crop spraying techniques offer several advantages, including increased safety and cost effectiveness through autonomous and programmed operations based on specific schedules and routes. One of the main advantages of using drones for plant protection is their ability to monitor large areas of crops in a short amount of time. In addition to crop protection management, using drones for augmentative biocontrol facilitates the distribution of beneficial organisms to the exact locations where they are required, which can increase the effectiveness of biocontrol agents while reducing distribution costs. In this context, given the very limited commercial use of drones in the Western Balkans’ agri-food sector, the use of drones in the agri-food industry is a topic that needs to be elaborated on and highly promoted. Additionally, the specific legal regulations in Serbia that currently limit the use of drones in agriculture must be outlined. Conventional crop production is still significantly more prevalent in Serbia, but given the region’s continuous technological progress, there is no doubt that farmers’ education and future investments in precision agriculture will most likely increase the use of state-of-the-art technologies and drones in agriculture.

This paper shows the results of hydrothermal carbonization of biomass from seven different crops used as biofuel: wheat straw, soybean straw, corn cob, cor

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.

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.