Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability.

This pilot study focuses on the design, implementation, optimization and verification of a novel solution of smart measuring of water consumption and crisis detection leading to a smart water management platform. The system implemented consists of a modular IoT platform based on a PCB (Printed Circuit Board) design using the M2.COM standard, a LoraWAN modem and a LoraWAN gateway based on the Raspberry Pi platform. The prototype is modular, low-cost, low-power, low-complex and it fully reflects the requirements of strategic technological concepts of Smart City and Industry 4.0, i.e., data integration, interoperability, (I)IoT, etc. The study was produced in cooperation with M.I.S Protivanov and VODARENSKA AKCIOVA SPOLECNOST, a.s. (industry partners distributing drinking water in the Olomouc and South-Moravian regions) to depict the current situation in the Czech Republic, characterized by extreme weather fluctuations and increasingly frequent periods of drought. These drinking water distributors are also constantly placing new demands on these smart solutions. These requirements include, above all, reliability of data transmission, modularity and, last but not least, low cost. However, smart water management (water consumption, distribution, system identification, equipment maintenance, etc.) is becoming an important topic worldwide. The functionality of the system was first verified in laboratory conditions and, then, in real operation. The study also includes checking signal propagation in the municipal area of the village of Zdarna, where the radius of the proposed measuring system was tested. A laboratory test with simulation of water leakage is also part of this work. Subsequently, the system was tested in a residential unit by means of water leakage detection using the MNF method (minimum night flow); the detection success rate was 95%.

This case study deals with the design of a hybrid system for the prevention of thermal events in mining waste disposal sites and landfills. The overall design, real implementation, optimization and experimental verification of the functionality of the entire system are described in detail. Both experimental platforms are built on the Internet of Things (LoRaWAN and Sigfox) basis and meet the conditions for autonomous long-term on-site monitoring. The data collected is periodically transmitted wirelessly to a database repository, which processes relevant parameters for the operators of dispatching workplaces. The study is focused on a combination of surface and depth measurement methods. The experimental results clearly confirm the functionality of the proposed solutions, which will enable timely interventions and elimination of underground and surface combustions. Thanks to centralized data collection, a unique database has also been created, which can be used for the implementation of prediction algorithms (based, for example, on machine learning or artificial intelligence).

The treatment, monitoring, and distribution of drinking water is an integral component of critical national infrastructure and therefore places continually increasing demands on Water Distribution Networks (WDNs). This domain and its sub-sectors face several major problems, namely climate change and drought-induced rises in water consumption from surface and underground reservoirs, in addition to the existence of significant water leaks during transmission to end users. These problems can be addressed by deploying Internet of Things (IoT) systems and smart distribution grids to improve the efficiency and safety of water distribution and to easily detect leaks or unauthorized consumption. This type of smart grid is referred to as Smart Water-IoT (SW-IoT), a novel, comprehensive water management concept. This review article discusses the application of IoT components and artificial intelligence (AI) in five basic categories (agriculture, water treatment, security, WDNs, and wastewater). Relevant legislation in the EU, USA, Canada, Australia, China, Japan, and India is also reviewed. In this context, the mandatory implementation of smart remote data reading solutions into the critical infrastructure of EU member states is outlined to highlight the importance of responsible water handling. The article provides a detailed analysis of the current research in SW-IoT and defines the main research challenges for future investigation.

The massive development of internet of things (IoT) technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth, smart city, agriculture or waste management. This ongoing development is further pushed forward by the gradual deployment of 5G networks. With 5G capable smart devices, it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT. Massive-IoT (low-power wide area network-LPWAN) enables improved network coverage, long device operational lifetime and a high density of connections. Despite all the advantages of massive-IoT technology, there are certain cases where the original concept cannot be used. Among them are dangerous explosive environments or issues caused by subsurface deployment (operation during winter months or dense greenery). This article presents the concept of a hybrid solution of IoT LoRaWAN (long range wide area network)/IRC-VLC (infrared communication, visible light communication) technology, which combines advantages of both technologies according to the deployment scenario.

Recently, the development of agricultural technology has been focused on achieving higher reliability and quality of work. The aim of the presented paper was to examine the possibilities of evaluating the quality of work of wide-area irrigation machinery by monitoring the coefficients of uniformity and non-uniformity of irrigation. The object of the research was pivot irrigation machinery equipped with sprinklers with a total length from 230 to 540 m. The commonly applied quality of work parameter for wide-range irrigators is the irrigation uniformity coefficient according to Heermann and Hein CUH. Work quality evaluations were also carried out through other parameters applicable in practice, such as irrigation uniformity coefficients calculated according to Christiansen CU, Wilcox and Swailes C_ws, and our introduced parameters, the coefficient a_r (derived from the degree of unevenness according to Oehler) and the degree of uniformity γ_r (derived from the degree of non-uniformity according to Voight). Other applied parameters for determining the quality of work of wide-range irrigation machinery were the coefficients of irrigation uniformity according to Hart and Reynolds CU_hr, further according to Criddle CU_cr and subsequently according to Beale and Howell CU_br. Next, the parameters of the non-uniformity coefficient according to Oehler a, the coefficient of variation according to Stefanelli C_v, the degree of non-uniformity according to Voigt γ and the degree of non-uniformity according to Hofmeister E_f were evaluated. Field tests were performed during the growing season of cultivated crops (potatoes, corn and sugar beet) in the village of Trakovice (agricultural enterprise SLOV-MART, southwest of the Slovakia) and in the district of Piešťany (Agrobiop, joint stock company). During the research, the inlet operating parameters (speed stage, inlet pressure, irrigation dose), technical parameters (number of sprayers, total length, number of chassis) and weather conditions (wind speed and temperature) were recorded. The obtained results were examined by one-way ANOVA analysis depending on the observed coefficient or input conditions and subsequently verified by Tukey and Duncan tests as needed. Irrigation uniformity values ranged from 67.58% (C_ws) to 95.88% (CU_bh) depending on the input conditions. Irrigation non-uniformity values ranged from 8.58 (a, E_f) to 32.42% (C_v). The results indicate a statistically significant effect of the site of interest and thus the impact of particular field conditions (p < 0.05). When evaluating the application of different coefficients of irrigation uniformity, the results showed a statistically significant effect only in the first test (p = 0.03, p < 0.05). During further repeated measurements, the quality of work increased due to the performed inspection of all sprayers and the reduction in the influence of the wind.