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Digital agriculture technologies provide potential for increased yield and quality of wheat grain with an optimized input use related to site-specific conditions. This review aims to present the global distribution of digitalization in wheat production, to identify the core digital technologies applied in wheat management, and to address challenges and future directions for ensuring the security of producing this staple food. For this purpose, a systematic literature review based on the PRISMA 2020 guidelines was conducted, and 113 peer-reviewed papers within the period of 2015–2025 were selected and examined. The highest number of research papers refers to Asia (37.4%), followed by Europe (17.4%) and North America (15.7%). The majority of the papers related to the field of remote sensing, more specifically, in 40.2% of the papers, satellites are listed as a platform, followed by UAVs (in 33.0% of studies). The review reveals uneven global distribution of digitalization, with a significant need for improvement in less developed countries to address food safety in a more balanced way. This comprehensive analysis proposes integration of the current state of digitalizing wheat production with future opportunities for large, but moreover, for small and medium farmers, along with strong support for the policies.

Climate change is a variation in the normal behavior of the climate. These variations and their effects will be seen in the coming years, the most imminent being anomalous fluctuations in atmospheric temperature and precipitation. This scenario is counterproductive for agricultural production. This study evaluated the effect of climate change on oil palm production for conditions in the Central Pacific of Costa Rica, in three simulation scenarios: the baseline between the years 2000 and 2019, a first climate change scenario from 2040 to 2059 (CCS1), and a second one from 2080 to 2099 (CCS2), using the modeling framework APSIM, and the necessary water requirements were established as an adaptive measure for the crop with the irrigation module. A decrease in annual precipitation of 5.55% and 7.86% and an increase in the average temperature of 1.73 °C and 3.31 °C were identified, generating a decrease in production yields of 7.86% and 37.86%, concerning the Baseline, in CCS1 and CCS2, respectively. Irrigation made it possible to adapt the available water conditions in the soil to maintain the baseline yields of the oil palm crop for the proposed climate change scenarios.

Crop protection information, such as how to control emergent and outbreak crop diseases and pests, as well as the latest research, regulations, and quality control measures for pesticides and fertilizers, is important to farmers. Rural smallholder farmers in Tanzania have traditionally relied on government agricultural officers who visit them in their villages to provide this crop protection information. However, these officers are few and cannot reach all the farmers on time. This means that farmers fail to make critical farming decisions on time, which can lead to low crop productivity. In this study, we aim to provide farmers with reliable and instant crop protection information by developing a system based on the Short Message Service (SMS) and the Web. This system automatically replies to farmers’ requests for the latest crop protection information in the Swahili language through SMS on a mobile phone or a Web system. The findings reveal that our proposed system can provide farmers with crop protection information at lower cost (500 times cheaper) than the existing Tigo Kilimo system. Furthermore, our proposed system’s deep learning model is effective in understanding and processing Swahili natural language SMS queries for crop protection information with an accuracy of 96.43%. This crop protection information will help farmers make better critical farming decisions on time and improve crop productivity.

The article discusses modern legal, managerial, digital approaches to ensuring the sustainable development of agriculture. The norms and standards of agricultural risk insurance, budget lending, organic agribusiness modeling and food safety developed by international institutions are evaluated. Methods of comparative, legal, informational analysis and scientific generalization of theoretical knowledge revealed organizational problems of budgeting and quality control of agricultural products, preventing food losses, and combating falsification of food products. It was noted that legal measures and management standards optimize agribusiness segments, reduce the number of intermediaries, and improve the trust of suppliers and consumers through direct links. The digitalization of agriculture contributes to the transparency of data exchange while reducing information imbalances and transaction costs for participants in agricultural markets. The connection of standards and norms of legal regulation of sustainable development of agriculture with food security in the EU and the EAEU is shown. The conclusions are indicated by the positive prospects for digital management of agribusiness subsidies and digital budgeting as the level increases in the EU and the EAEU. Unlike the EU, the EAEU is building a common space for organic and digital agriculture on the principles of Eurasian integration and cooperation.

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R2 = 0.771) and performance (R2 = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture.

As digitalization in the agricultural sector has intensified, the number of studies addressing adoption and use of digital technologies in crop production and livestock farming has also increased. However, digitalization trends in the context of small-scale farming have mainly been excluded from such studies. The focus of this paper is on investigating the sequential adoption of precision agriculture (PA) and other digital technologies, and the use of multiple technologies in a small-scale agricultural region in southern Germany. An online survey of farmers yielded a total of 2,390 observations, of which 1,820 operate in field farming, and 1,376 were livestock farmers. A heuristic approach was deployed to identify adoption patterns. Probable multiple uses of 30 digital farming technologies and decision-support applications, as well as potential trends of sequential technology adoption were analyzed for four sequential points of adoption (entry technology, currently used technologies, and planned short-term and mid-term investments). Results show that Bavarian farmers cannot be described as exceedingly digitalized but show potential adoption rates of 15–20% within the next five years for technologies such as barn robotics, section control, variable-rate applications, and maps from satellite data. Established use of entry technologies (e.g., automatic milking systems, digital field records, automatic steering systems) increased the probability of adoption of additional technologies. Among the most used technologies, the current focus is on user-friendly automation solutions that reduce farmers’ workload. Identifying current equipment and technology trends in small-scale agriculture is essential to strengthen policy efforts to promote digitalization.

Smart farming is a development that has emphasized information and communication technology used in machinery, equipment, and sensors in network-based hi-tech farm supervision cycles. Innovative technologies, the Internet of Things (IoT), and cloud computing are anticipated to inspire growth and initiate the use of robots and artificial intelligence in farming. Such ground-breaking deviations are unsettling current agriculture approaches, while also presenting a range of challenges. This paper investigates the tools and equipment used in applications of wireless sensors in IoT agriculture, and the anticipated challenges faced when merging technology with conventional farming activities. Furthermore, this technical knowledge is helpful to growers during crop periods from sowing to harvest; and applications in both packing and transport are also investigated.

Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

Warming of the earth is considered as the major adverse effect of climate change along with other abnormalities such as non-availability of water resources, decreased agriculture production, food security, rise in seawater level, glaciers melting, and loss of biodiversity. Over the years, decreased agriculture production and water quality degradation have been observed due to climatic abnormalities. Crop production is highly sensitive to climate. It gets affected by long-term trends in average rainfall and temperature, annual climate variations, shocks during different stages of growth, and extreme weather events. Globally, the areas sown for the major crops of barley, maize, rice, sorghum, soya bean, and wheat have all seen an increase in the percentage of area affected by drought as defined in terms of the Palmer Drought Severity Index since the 1960s, from approximately 5–10% to approximately 15–25%. Increase in temperature will be observed in terms of wheat yield losses − 5.5 ± 4.4% per degree Celsius for the United States, − 9.1 ± 5.4% per degree Celsius for India, and − 7.8 ± 6.3% per degree Celsius for Russia as these countries are more vulnerable to temperature increase. Water management through increasing storage capacity (or rainwater storage), fair policies for water supply and distribution, river health, and watershed management can reduce the negative effects of climate change on water resource availability. Similarly, climate change-resistant crop development, water management in irrigation, adapting climate-smart agriculture approach, and promoting indigenous knowledge can ensure the food security via increasing agricultural yield. Technical intervention can equip the farmers with the scientific analyses of the climatic parameters required for the sustainable agriculture management. These technologies may include application of software, nutrient management, water management practices, instruments for temperature measurement and soil health analysis etc. Holistic efforts of the stakeholders (farmers, local society, academia, scientists, policy makers, NGOs etc.) can provide better results to reduce the risks of climate change on agriculture and water resources as discussed in this paper. Graphical abstract

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.