The calcium fertilization of strawberry plants (Fragaria × ananassa Duchesne) was evaluated using two types of nozzles, with two liquid pressure levels and two driving speeds. The calcium content of the leaves and fruit were analyzed via flame photometry. Higher leaf calcium content was found in plots sprayed with standard nozzles, while higher fruit calcium content was observed for those sprayed with air induction nozzles. The fruit quality was assessed by determining the basic physical and mechanical properties, using uniaxial compression tests integrated with surface pressure measurements. Different spraying techniques influenced the mechanical resistance of the fruit. A spraying speed of 5 km/h and an operating pressure of 0.4 MPa significantly increased the firmness of the fruit by ~66%, the critical load level by 36%, and the maximum surface pressure by up to 38%, but did not increase the geometrical parameters of the strawberries. Regular foliar feeding during harvest could improve the mechanical strength of strawberries. An appropriate spraying technique with a calcium agent could effectively improve the mechanical properties of the delicate fruit, which is particularly important for limiting losses during harvesting, transportation, and storage.

In this paper, the authors present the relationship between the assumptions of ergonomics in the work of a strawberry picker and quality of picked fruit. The body posture that a person adopts while working has a significant impact on their health, working comfort, and productivity, but also on the quality of the fruit that is harvested. This paper identifies three characteristic picker positions during strawberry harvesting. A synchronized surface electromyography (sEMG) instrument together with the Tekscan^® surface pressure measurement system allowed for the determination of the influence of working position on changes in the load of the picker’s musculoskeletal system and the surface pressure exerted on the fruit during manual strawberry picking, which are decisive factors for maintaining fruit quality. In addition, compression tests on whole strawberry fruit were carried out as a benchmark to evaluate and compare the maximum forces as well as the destructive pressures on the fruit. From the tests, we found that the most comfortable position of the worker’s body was determined along with the harvesting technique (position during work) that has the least negative effect on the quality of the harvested fruit. Consequently, the level of dynamic load on the worker was determined.

From the producers’ point of view, there is no universal and quick method to predict bruise area when dropping an apple from a certain height onto a certain type of substrate. In this study the authors presented a very simple method to estimate bruise volume based on drop height and substrate material. Three varieties of apples were selected for the study: Idared, Golden Delicious, and Jonagold. Their weight, turgor, moisture, and sugar content were measured to determine morphological differences. In the next step, fruit bruise volumes were determined after a free fall test from a height of 10 to 150 mm in 10 mm increments. Based on the results of the research, linear regression models were performed to predict bruise volume on the basis of the drop height and type of substrate on which the fruit was dropped. Wood and concrete represented the stiffest substrates and it was expected that wood would respond more subtly during the free fall test. Meanwhile, wood appeared to react almost identically to concrete. Corrugated cardboard minimized bruising at the lowest discharge heights, but as the drop height increased, the cardboard degraded and the apple bruising level reached the results as for wood and concrete. Contrary to cardboard, the foam protected apples from bruising up to a drop height of 50 mm and absorbed kinetic energy up to the highest drop heights. Idared proved to be the most resistant to damage, while Golden Delicious was medium and Jonagold was least resistant to damage. Numerical models are a practical tool to quickly estimate bruise volume with an accuracy of about 75% for collective models (including all cultivars dropped on each of the given substrate) and 93% for separate models (including single cultivar dropped on each of the given substrate).

The harvest phase plays an important role in the whole process of production of tomato fruit. Therefore, it is necessary to ensure a technological process that will not damage biological materials. The harvest phase plays an important role in the whole process. Many growers use special machines for harvesting, but there are fruits and vegetables that should be harvested manually to avoid damaging the surface or parenchyma tissue of the harvested objects. In addition to maintaining the quality of biological materials, work comfort, and ergonomic conditions for pickers should be ensured because inadequate working conditions do not encourage employees to undertake manual work in horticulture. Therefore, there have been shortages of workers on Polish plantations in the past year. Based on manual tomato harvesting, the authors conducted a matched qualitative research study on biological materials and work ergonomics. For this purpose, the Grip System was used to investigate tomato quality by assessing the impact of picking hand pressure (in three different picker’s body positions) on the harvested objects. Simultaneously, for the picker’s ergonomic analysis, a non-invasive surface electromyography method was used to precisely measure changes in muscle motor unit action in the picker’s wrist and lumbar spine while in three characteristic picker’s positions. The tests found that the poorest body position was when the body was deeply inclined and simultaneously twisted. No significant effect was shown of the body position of the tomato picker on the deterioration of the picked fruit quality. However, body positions significantly affect the level of physical load and work comfort.

One of the methods used to improve the durability of blueberry fruits is the application of nutrients through foliar feeding with calcium, which can improve the post-harvest mechanical parameters. This study proposed an optimal selection of calcium spray parameters, which enables a rational minimisation of the negative impact of agrochemicals in the environment. The qualitative evaluation of blueberry fruit showed lime spraying induces a significant effect on the increase in fruit size, especially at a pressure of 0.2 MPa and with AIXR nozzles compared to the control group. To assess the mechanical properties, a modern method of identifying the actual loads and maximum surface pressures generated by the picker during harvesting is presented. Compression and fruit rupture tests were also used to determine the pressure values and forces that are considered safe from the perspective of harvest quality. The comparative analysis of destructive compression and detachment tests confirmed that fruit firmness (Fp) was approximately 80% higher than the detachment force (Fpf), with peak pressures more than twice as high, suggesting that handpicking poses minimal risk of mechanical damage. The implementation of optimal spraying techniques combined with the correct assessment of the mechanical properties of fruits is important in agricultural practice, where it is crucial to obtain high-quality blueberries after harvest.

This study explores the application of machine learning and genetic algorithm to model and optimize the spraying process of blueberry plants ( Vaccinium corymbosum L.). The research aimed to determine the relationship between sprayer operating parameters (nozzle type, liquid pressure, and driving speed), weather conditions (temperature, humidity, and wind speed), and spray quality, specifically the coverage of various plant surfaces. Three ML methods, namely Gradient Boosting Regressor, Support Vector Regressor, and Extreme Gradient Boosting were evaluated to develop predictive models based on experimental data collected using water-sensitive papers. The results indicate that all models achieved high accuracy, with the SVR and XGBoost models being particularly effective, showing a MAPE error as low as 4% for certain surfaces. These models were then integrated into a genetic algorithm to identify optimal operating parameters that maximize total spray coverage under specific meteorological conditions. Optimization results revealed that XR nozzles generally achieved higher total coverage compared to AIXR nozzles. Furthermore, a driving speed of 5.5 km/h was found to be optimal for both nozzle types across various conditions. This hybrid approach demonstrates significant potential for developing intelligent decision support systems in precision agriculture, enabling more efficient and sustainable use of plant protection products.