Wheat Production Transition Towards Digital Agriculture Technologies: A Review

Academic Editor: Jian Zhang Received: 15 October 2025 Revised: 12 November 2025 Accepted: 12 November 2025 Published: 18 November 2025 Citation: Magazin, N.; Vuji´c, S.; Lali´c, B.; Koˇci, V.; Benka, P.; ´ Ciri´c, V.; Sedlar, A.; ´ Cupina, B.; Bitakou, E.; Nychas, K.; et al. Wheat Production Transition Towards Digital Agriculture Technologies: A Review. Agronomy 2025,15, 2640. https://doi.org/ 10.3390/agronomy15112640 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Review Wheat Production Transition Towards Digital Agriculture Technologies: A Review Nenad Magazin 1, Svetlana Vuji´c 1,* , Branislava Lali´c 1, Vladimir Koˇci 2, Pavel Benka 1, Vladimir ´ Ciri´c 1, Aleksandar Sedlar 1, Branko ´ Cupina 1, Effrosyni Bitakou 3, Konstantinos Nychas 4, Vasilis Psiroukis 4, Marianna I. Kotzabasaki 5and Konstantinos Demestichas 3 1Faculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovi´ca 8, 21000 Novi Sad, Serbia; [email protected] (N.M.); [email protected] (B.L.); [email protected] (P.B.); vladimir[email protected] (V. ´ C.); aleksandar[email protected] (A.S.); [email protected] (B. ´ C.) 2Climate Smart Solutions, 21000 Novi Sad, Serbia; vladimir[email protected] 3Informatics Laboratory, Department of Agricultural Economics and Rural Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; [email protected] (E.B.); [email protected] (K.D.) 4Laboratory of Farm Machine Systems, Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; [email protected] (K.N.); [email protected] (V.P.) 5 Laboratory of Farm Structures, Department of Natural Resources Development and Agricultural Engineering, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; [email protected] *Correspondence: [email protected] Abstract 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. Keywords: wheat production; digital agriculture technologies; review; PRISMA 2020 1. Introduction Wheat (Triticum aestivum L.) is one of the staple foods worldwide [ 1 , 2 ], and it makes up the majority of the human diet and provides a significant amount of the daily required energy and nutrients. The history of wheat utilization and domestication passes from the human efforts to control food supply and prevent starvation [ 3 ] through to the evolution of agricultural production, which increased yield and made wheat the strategic trade good. Agronomy 2025,15, 2640 https://doi.org/10.3390/agronomy15112640 Agronomy 2025,15, 2640 2 of 22 The early 1960s were the years when the Green Revolution made a significant change in agriculture with the introduction of semi-dwarf wheat and rice varieties to the fields and with the application of mineral fertilizers, pesticides, moldboard plowing, and irrigation [ 4 ]. This turnover resulted in higher yields and reduced hunger, making many countries selfsufficient, and even providing extra profit. However, these initial benefits of the revolution made serious changes to the soil, water, and air biodiversity and to the environment in general. The Green Revolution innovations lasted until the 1990s, when yield in many regions started to decline [ 4 ]. Therefore, the pathway from “green revolution to green agriculture” is the result of decades of practices that initially had a significant impact on food production but through uncontrolled use have led to the deterioration of the quality of natural resources. The digitalization of agriculture is one way to reduce the harmful effects of intensive agriculture introduced by the Green Revolution, while still achieving adequate yields. According to the data analysis presented by [ 5 ], wheat was the most frequently grown crop in the world by 2018, with an estimated 217 million (M) hectares, followed by maize with nearly 200 M ha and rice with 165 M ha. It is grown in diverse regions from latitudes 60 ◦ N to 44 ◦ S and at 3000 m above sea level [ 6 ], which is a wider cultivation area compared to rice and maize. Globally, Asia is the region that produces the most wheat (44%, TE2018), followed by Europe (34%, TE) [ 5 ], while in terms of wheat trade in 2020 TE, Europe had the highest exports (110 Mt), while Asia registered the highest imports (78 Mt) [ 7 ]. All facts and figures emphasized the importance of the continuous production of wheat for food, but also for the economic benefits. The late 1990s were the years when digital agriculture was introduced in agricultural practice as a way to increase agricultural productivity and profitability through the use of information and GIS technology [ 8 ]. Digitalization in agriculture refers to the use of digital technologies to monitor and gather information for the optimization of farming practices and to reduce the use of resources (soil, water) and inputs (fertilizers, pesticides) [ 9 ]. Today, the agriculture and agri-food sector is significantly impacted by DA technologies, such as big data, Internet of Things (IoT), robotics, sensors, artificial intelligence (AI), machine learning, digital twins, and the blockchain [10–13], for collecting past data, to monitor the present and to predict the future, and to make accurate timely decisions and actions [ 14 – 16 ]. Terms like “digital agriculture”, “precision agriculture”, and “smart farming” refer to the use of basic applications, like a mobile phone, to the use of robots and satellites to support decision making and to reduce resource exploitation while obtaining an adequate quantity and quality of products [17,18]. Digitalization is also present in wheat production worldwide (Figure 1). Globally, wheat farmers are facing many challenges. If the geopolitical issues are excluded, even with the very significant impact on the global production and trade, the most notable challenges in wheat production is the growing need for food, natural resource degradation [ 19 ]— mainly soil degradation—the rising labor costs, lowering our carbon footprint, the effects of climate change [ 20 ], and the requirement to cut inputs in many areas [ 21 ]. For instance, soil organic matter content, which is crucial for soil health and quality in terms of fertility, structure, activity of microorganisms, and nutrient cycling [ 22 , 23 ], significantly declined in agricultural soils over the years as a result of management practices and environmental conditions [ 24 , 25 ]. In addition, unfavorable abiotic and biotic conditions have significantly hindered production and increased uncertainty [ 26 – 28 ]. In the analyses of Pinke and Lovei [29] in Hungary, for a 30-year period (1981–2010), it was determined that for wheat, a 1 ◦ C temperature increase caused a yield loss of around 10%. In France in 2015, extremely high temperatures in late autumn stimulated the development of aphids and leafhoppers, which contributed to a 25% decrease in winter wheat yield harvested in 2016 [ 30 ]. Large- Agronomy 2025,15, 2640 3 of 22 scale field surveillance, monitoring changes in microclimate conditions, and the detection of pests and diseases is time-consuming and requires more labor, which is not always in accordance with the labor costs and available workers [ 31 – 33 ]. Joshi et al. [ 34 ] pointed out that digital technologies, such as unmanned aerial vehicles (UAVs) for image capture, computer vision, and machine learning algorithms, can be used for fast disease detection and the timely implementation of adequate measures. Figure 1. Visualization of the wheat management evolution; DA—Digital Agriculture. Advancements in blockchain technology offer prospects for its application in the supply chain system of wheat crops, thereby enhancing traceability, transparency, and security. Farooq et al. [ 35 ] developed a transparent and efficient framework for the wheat crop supply chain utilizing blockchain technology. The proposed blockchain network utilizes a decentralized system to monitor wheat transactions among farmers, suppliers, and traders through cryptographically secure ledgers and smart contracts, initiated with tokens like “wheat coin” (WC). Additionally, the interplanetary file system (IPFS) has been developed for the secure storage of confidential transaction data. The proposed model for the wheat supply chain is anticipated to transform the value chains of wheat crops regarding efficiency and sustainability in agricultural supply systems worldwide. Moreover, digital twin (DT) technology for wheat growth has been used in several recent research papers. Xu et al. [ 36 ] proposed a DT model for winter wheat by combining a DSSAT framework with the SUBPLEX optimization algorithm, obtaining a coefficient of determination (R 2 ) value of 0.98 for simulating both the leaf area index (LAI) and aboveground biomass (AGB). Similarly, another research paper by Skobelev et al. [ 37 ] proposed a multi-agent cyber-physical system simulating a DT model for wheat for precision agricultural purposes. All the above-mentioned studies demonstrate DT technology’s efficiency in enhancing wheat growth [38,39]. Digital monitoring technologies offer essential data streams for identifying and reacting to disasters caused by climate change, which can affect wheat growth [ 40 – 43 ]. Remote sensing technology, such as satellites and UAS, allows for immediate identification of drought, flooding, and heat stress [ 36 , 44 – 48 ]. Environmental sensors measure soil moisture, temperature, and related growth indicators for crops in real-time [ 49 ]. Artificial intelligence (AI) models rely on these data streams for early warning alerts on infestations with pests and outbreaks of plant diseases fueled by climate change [ 36 , 50 – 54 ]. Disaster response strategies modeled by digital data include managing irrigation regimes for wheat crops, fertilizing sections with improved resistance to climatic instability, and introducing climateresilient varieties for improved wheat growth. An all-encompassing digital system for disaster response is more accurate than traditional assessment strategies for estimating disaster damage, particularly for irregularly irrigated crops like wheat that are affected by rapidly fluctuating climatic conditions on agricultural land [29,30,55,56]. Agronomy 2025,15, 2640 4 of 22 This review aims to provide a comprehensive synthesis of technologies and analytical methods used in different aspects of wheat production. It will focus on a scientific approach within the DA application in wheat production, as well as point out challenges and future research in wheat production to strengthen the implementation of digital technologies along the pathway from sowing and management to the harvest. Having emphasized this, the specific objectives of this study are as follows: (i) world spatial distribution of the research on DA in wheat production; (ii) to identify and categorize DA and analytical methods in wheat technology; and (iii) to highlight future research directions for broader DA implementation in wheat growing and grain management. 2. Materials and Methods 2.1. Search Queries and Strategy To ensure transparency and reproducibility, we have conducted this systematic review in accordance with the PRISMA 2020 guidelines [ 57 ]. Our comprehensive literature search of two respected scientific databases—Scopus and Web of Science—was performed to identify all the relevant peer-reviewed research studies regarding the application of DA technologies to wheat production. Therefore, we designed a detailed search query that was applied to both databases’ search engines, as shown in Table 1. Table 1. Search engines and queries. Search Engine Website Search Query Scopus http://www.scopus.com/ (“UAV” OR “UAS” OR “Drone” OR “RPAS” OR “Multispectral camera” OR “Hyperspectral camera” OR “UGV” OR “RGB Camera” OR “Image analysis” OR “Robot” or “Robotic” OR “Remote sensing”) AND (“Machine Learning” OR “Artificial Intelligence”) AND (“wheat” OR “triticum aestivum”) AND (“Disease detection” OR “weed detection” OR “pest detection” OR “Yield Prediction” OR “Yield Estimation” OR “Yield Forecast” OR “Damage Detection”) Web of Science http: //www.webofscience.com/ (accessed date 14 October 2025) (“UAV” OR “UAS” OR “Drone” OR “RPAS” OR “Multispectral camera” OR “Hyperspectral camera” OR “UGV” OR “RGB Camera” OR “Image analysis” OR “Robot” or “Robotic” OR “Remote sensing”) AND (“Machine Learning” OR “Artificial Intelligence”) AND (“wheat” OR “triticum aestivum”) AND (“Disease detection” OR “weed detection” OR “pest detection” OR “Yield Prediction” OR “Yield Estimation” OR “Yield Forecast” OR “Damage Detection”) The key strategy for ensuring the most efficient and representative search included the use of Boolean terms in addition to accurate keywords relating to digital technology practices performed in wheat agriculture. Thus, by incorporating the Boolean terms (AND, OR), we ensured a broad and accurate analysis of the literature. Another important aspect of our study was to ensure the relevance of the research papers obtained. Hence, we focused on both research articles and review articles in the English language that were released in the period from January 2015 to April 2025. 2.2. Methodology and Filtering Steps Having applied the advanced query search, we ended up obtaining one hundred and seventy papers from the Scopus database (n = 170), in addition to two hundred and twenty-two papers from the Web of Science database (n = 222). The meta-analysis of the Agronomy 2025,15, 2640 5 of 22 acquired papers was performed in Excel. We extracted metadata on the papers, in line with the PRISMA 2020 guidelines. After that, we screened for duplicates, also using Excel. Out of the three hundred and ninety-two papers (n = 392), one hundred and twenty-five were duplicates (n = 125). Following the duplicate exclusion, a separate group of reviewers performed a screening and reported on the articles without the ones that were excluded for title, abstract, or keywords irrelevance. After this process, a total of one hundred and fifty-two papers were excluded (n = 152). In the subsequent step, we included the final selection of one hundred and thirteen papers (n = 113) (Table 2and Figure 2). Table 2. Inclusion and exclusion criteria. Inclusion Criteria Exclusion Criteria The paper must have been published between January 2015 and April 2025 Articles published before January 2015 The article must be a journal article Non-peer-reviewed papers (such as book chapters, theses, etc.) The article must be written in English Article was not written in English Must not be a duplicate Appears in a search in a different database Figure 2. The PRISMA flow diagram of the literature review search for this study. 2.3. Criteria for Analysis (Year of Publication, Impact Factor, Type of Publication, Publisher, and Journal) 2.3.1. Year of Publication An analysis of the temporal distribution of article publications was performed to observe trends over time. Hitherto, we have only considered articles published between January 2015 and April 2025 (Figure 3). Agronomy 2025,15, 2640 6 of 22 Figure 3. Number of studies by year. 2.3.2. Impact Factor We tracked the impact factor ratings, based on the latest Clarivate Journal Citation Reports, as an indicator of the scholarly influence of a publication. This was an important step in the visible and accurate representation of the quality of journals where the papers were being published (Figure 4). Figure 4. Journal impact factor in the period from January 2015 to April 2025. 2.3.3. Type of Publication To ensure the inclusion of papers that match rigorous scientific criteria, we exclusively included research articles and review papers published in peer-reviewed journals (Figure 5). Agronomy 2025,15, 2640 7 of 22 Figure 5. Type of reported studies. 2.3.4. Publisher and Journal In addition to the impact factor, we meticulously tracked journal and publisher names to inspect the ones that most often recurred. This allowed for a better understanding of trends in agricultural publishing and provided dedicated attention to the most popular publishers and journals. This analysis is presented in Figure 6. Figure 6. Spread of publications with different publishers. 3. Results On the basis of the set criteria presented in the scientific research method, works that satisfied the set criteria were singled out. After that, these papers were reviewed and analyzed. A total of 113 papers were reviewed. A total of 18 of these works were reviews, which is 15.9%, while the remaining 95 papers were research articles (84.1%). Agronomy 2025,15, 2640 8 of 22 3.1. Geographic Coverage of Research In addition to the type of published advice, the research area of each published article in our study is analyzed. Papers that were type reviews had global coverage. Of the other works, the largest part relates to research in the territory of China, a total of 28 papers, or 24.3% of all articles. In second place is the USA, which is processed in a total of 17 articles (14.8%). After that, it was determined that a total of nine papers were written for research in Australia (7.8%). The following three countries could also be singled out here: Germany, India, and Pakistan, all of which appear in five research papers, that is, 4.3% for each country. Another four countries (Denmark, Hungary, Poland, and Spain) appear in two reviewed articles. There is a total of 30 countries in the coverage overview, but the other 20 are listed only in one paper. The number of published studies is shown in the map (Figure 7) and diagram (Figure 8).  Figure 7. Number of published research papers by country, presented on a world map.  Figure 8. Number of published research papers by country. When comparing the number of published research papers by continent, the largest number of research papers refers to Asia (37.4%), followed by Europe (17.4%) and North America (15.7%), while Africa (3.5%) and South America (2.6%) are the least represented (Figure 9). From the results shown, the expected trend can be seen in that the most Agronomy 2025,15, 2640 9 of 22 represented countries are the ones with a large scope for the application of digital tools in agriculture.  Figure 9. Number of published papers by continent. Digital agricultural technologies have been revolutionizing wheat farming across the world. However, disparities in policies affect these technologies across nations. China’s policy on digital agricultural technology involves public investment and implementation on a national scale, which involves increasing adoption and big data integration [58]. The policy in the USA involves public investment with a focus on voluntary precision farming by agricultural stakeholders, which is conducted by agricultural extension programs [ 59 ]. Member nations that comprise Europe’s EU have adopted policy programs for digital agricultural technology [ 60 ]. These programs involve regulations on sustainability and inclusivity in digital agricultural technology access, especially for small-scale farmers. These policy approaches have resulted in differing levels of maturity in digital agricultural technology adaptation for wheat farming. 3.2. Keywords and Subject Area A total of 377 keywords were determined by reviewing selected papers. The term machine learning occurs the most and appears as a keyword in 43 articles, followed by remote sensing (in 34 articles) and deep learning (in 29 articles), as well as the term random forest (in 11 articles). The term UAV (independently or as part of the keyword) was mentioned in 20 papers; the term precision agriculture was mentioned in 7 papers, and crop yield (independently or as part of the keyword) was mentioned in 17 papers; artificial intelligence (independently or as part of the keyword) was mentioned in eight papers. The frequency of occurrence of certain keywords is shown in Figure 10. A total of 81 different terms out of a total of 275 terms are identified in the analysis of the reviewed articles as a subject area. 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