The reduction of the population and labour force in rural areas, adverse climate changes, soil pollution and degradation, and reduced soil fertility necessitate new and intensive approaches to and methods of vegetable production. In this study, a new high-tech, small-scale hydroponic system based on the Internet of Things (IoT) for growing leafy vegetables was designed. For the assembly of the high-tech small-scale hydroponic system, low-cost materials and sensors are used, allowing remote monitoring and process automation during the cultivation of leafy vegetables and seedlings. Experimental investigations of the installation were conducted, with lettuce as the grown crop. The environmental and technological parameters of the system were monitored and analysed in order to assess the suitability of the system. The obtained results confirm that the small-scale hydroponic system maintained the set parameters of air temperature, air humidity and pH of the nutrient solution within the optimal limits for growing lettuce. Other parameters, such as temperature and electrical conductivity of the nutrient solution, had small deviations from the required optimal limits. In the initially set experiment, 75% germination of the lettuce seeds was achieved. The main advantages of the proposed hydroponic system are the simplicity of its management and easy installation. Furthermore, it offers the possibility for remote monitoring and control without any high requirements towards the experience of the user with such facilities. The designed and implemented small hydroponic system can help small and medium-sized vegetable growers achieve indoor sustainable farming of leafy vegetables year-round.

The operation of an agricultural biogas plant is associated with the formation of a large amount of digestate. The basic trend in digestate management, after taking into account its physicochemical properties, should be its use as a fertilizer. The possibility of the agricultural use of digestate as a fertilizer was determined. Digestate obtained from the agricultural biogas plant was tested for the content of macronutrients and heavy metals. The content of macronutrients was also determined in soil for winter rape cultivation. The analysis showed an increase in the yield depending on the digestate dose applied. In addition, an increase in the fat and protein content was found in winter rape seeds. The best results of mean values were obtained during the application of the 50,000 L ha⁻¹ dose. The average diameter of the stem base was 1.48 cm. The average height was 36.20 cm. The seed yield was 3.44 t ha⁻¹. The thousand seed weight was—5.34 g. The fat and protein contents were 43.62% and 22.95%, respectively. By applying a digestate dose of 50,000 L ha⁻¹, the highest content of macronutrients, as well as polyunsaturated fatty acids (PUFA) (31.17%), monounsaturated fatty acids (MUFA) (61.89%) and saturated fatty acids (SFA) (6.87%), was recorded.

Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study is aimed at the creation of a novel algorithm to identify and distinguish white flowering honey plants, such as black locust (Robinia pseudo-acacia) and to determine the areas occupied by this forest species in mixed forest ecosystems using UAV-based RGB imaging. In our study, to determine the plant cover of black locust in mixed forest ecosystems we used a DJI (Da-Jiang Innovations, Shenzhen, China) Phantom 4 Multispectral drone with 6 multispectral cameras with 1600 × 1300 image resolution. The monitoring was conducted in the May 2023 growing season in the village of Yuper, Northeast Bulgaria. The geographical location of the experimental region is 43°32′4.02″ N and 25°45′14.10″ E at an altitude of 223 m. The UAV was used to make RGB and multispectral images of the investigated forest massifs, which were thereafter analyzed with the software product QGIS 3.0. The spectral images of the observed plants were evaluated using the newly created criteria for distinguishing white from non-white colors. The results obtained for the scanned area showed that approximately 14–15% of the area is categorized as white-flowered trees, and the remaining 86–85%—as non-white-flowered. The comparison of the developed algorithm with the Enhanced Bloom Index (EBI) approach and with supervised Support Vector Machine (SVM) classification showed that the suggested criterion is easy to understand for users with little technical experience, very accurate in identifying white blooming trees, and reduces the number of false positives and false negatives. The proposed approach of detecting and mapping the areas occupied by white flowering honey plants, such as black locust (Robinia pseudo-acacia) in mixed forest ecosystems is of great importance for beekeepers in determining the productive potential of the region and choosing a place for an apiary.

One of the important factors negatively affecting the yield of row crops is weed infestations. Using non-contact detection methods allows for a rapid assessment of weed infestations’ extent and management decisions for practical weed control. This study aims to develop and demonstrate a methodology for early detection and evaluation of weed infestations in maize using UAV-based RGB imaging and pixel-based deep learning classification. An experimental study was conducted to determine the extent of weed infestations on two tillage technologies, plowing and subsoiling, tailored to the specific soil and climatic conditions of Southern Dobrudja. Based on an experimental study with the DeepLabV3 classification algorithm, it was found that the ResNet-34-backed model ensures the highest performance compared to different versions of ResNet, DenseNet, and VGG backbones. The achieved performance reached precision, recall, F1 score, and Kappa, respectively, 0.986, 0.986, 0.986, and 0.957. After applying the model in the field with the investigated tillage technologies, it was found that a higher level of weed infestation is observed in subsoil deepening areas, where 4.6% of the area is infested, compared to 0.97% with the plowing treatment. This work contributes novel insights into weed management during the critical early growth stages of maize, providing a robust framework for optimizing weed control strategies in this region.