Wheat Production Transition Towards Digital Agriculture Technologies: A Review

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.

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.

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 self-sufficient, 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 [10,11,12,13], for collecting past data, to monitor the present and to predict the future, and to make accurate timely decisions and actions [14,15,16]. 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 [26,27,28]. 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-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 [31,32,33]. 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.

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 (R²) value of 0.98 for simulating both the leaf area index (LAI) and above-ground 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 [40,41,42,43]. Remote sensing technology, such as satellites and UAS, allows for immediate identification of drought, flooding, and heat stress [36,44,45,46,47,48]. 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 [36,50,51,52,53,54]. Disaster response strategies modeled by digital data include managing irrigation regimes for wheat crops, fertilizing sections with improved resistance to climatic instability, and introducing climate-resilient 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].

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. 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 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 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

and Figure 2).

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).

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).

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).

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.

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%).

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).

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 represented countries are the ones with a large scope for the application of digital tools in agriculture.

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. Remote sensing (in 58 papers), then precision agriculture (in 48 papers), and machine learning (in 28 papers), as well as agronomy (20 papers), agriculture (18 papers), or agricultural science (in 16 papers), were the subject areas for the most reviewed papers. The frequency of occurrence of certain terms representing the subject area is shown in Figure 11.

3.3. Platform and Sensor Type

Given that the papers are most often related to the field of remote sensing, we analyzed which platforms were listed as the sensor carriers that were being investigated. In the largest number, satellites are listed as the platform (in 40.2% of cases). The UAV is in second place, being mentioned in 33.0% of cases. Other platforms are tied to the surface of the earth (ground-based, vehicle, or handheld). Representation of the individual platforms in the research papers is given in Figure 12.

In the case where the platforms are satellites, satellites from the Sentinel 2 program and the MODIS program are most often used (Figure 13).

Analyzing which sensor types were mentioned in the selected articles, it was determined that the sensor was omitted in 155 cases, out of which 18 different sensors were identified. Multispectral sensors were mentioned most often (in 41.3% of cases), followed by RGB sensors (14.2%) and hyperspectral sensors (13.5%). In addition, we should also mention the thermal sensor, which occurred in 9% of cases (Figure 14).

3.4. Data Availability

For the largest number of works reviewed, it is stated that the data used in the research can be obtained upon request from the author of the work (50%). For a certain number of works, it is highlighted that they used publicly available data (17.9%). In 12.5% of cases, only partial data was available, and in a certain percentage, it was determined that no data was available (Figure 15). In one case, a purchase was required to be able to download the used data.

The small percentage of publicly available data probably lies in the fact that it is mostly remote sensing data, as the need for a large amount of data storage space is implied. Instances of partially available data and unavailable data are probably cases where the data owners do not want their data to be published for business or other reasons. The public availability of the used data could be increased by stimulating the use of public databases, with appropriate copyright protection.

4. Discussion

4.1. An Overview of the Geographical and Timely Distribution over the Ten-Year Research Period

Digital agriculture technologies have significantly transformed the utilization of land and resources, as well as farming practices, over the past 30 years worldwide [61,62,63]. The analysis of the research distribution revealed that China, followed by the USA, contributed the most publications to the DA field [64,65,66,67]. In Europe, several countries are singled out, like Germany, Denmark, and Hungary, which could be the result of the significant area under the wheat stand in those countries, as well as an increased concern regarding climate change’s effect on wheat production and potential yield losses [68]. The same distribution of publications was registered in the systematic review of crop yield provided by Darra et al. [59]. The authors stated that out of 55 registered countries, China was the most productive regarding studies, then the USA, India, Australia, and Brazil. China performs significantly with 98 publications, while the most active European countries—Germany, Spain, Italy, and France—have fewer than 8 publications. Therefore, China and the USA have a notable position and impact on research in digital technologies and crop production. Xie et al. [58] stated that this is the outcome of a significant investment, as well as the number of researchers working on DA.

The results within the given period show that there was an increased number of publications from 2020 up to 2025, which was the final observation year. The applied methodology selected papers where two publications were from 2019, while in 2020 there were 9, and in 2024 there was the highest production with 37 publications. This turnover in the literature on digitalization in agriculture was due to the COVID-19 pandemic, which was declared at the beginning of 2020 by the World Health Organization (WHO). Namely, lockdowns and severe restrictions on human movements and contacts changed the ways and organization of most human work and activities toward a higher involvement of digitalization. The same observation was also provided by Hassoun et al. [60], who noted that, according to data retrieved from the Scopus database from 2020, there has been an increase in research on digitalization in agri-food systems. Authors reported changes in the number of publications: in 2019 were nearly 150 papers, and in 2020, there were about 280, while in 2022, the papers almost reached 450. This systematic review has not selected papers related to COVID-19; however, the results clearly indicate much more productive research in DA after the pandemic. Sridhar et al. [69] noted that the pandemic created new conditions and an environment that led to the increased use of digital technologies.

The majority of publications, 74.6%, were research papers on wheat or related crops from the grass family, which is in line with what has been previously stated, i.e., created environments that stimulated the application of DA [70,71,72,73,74,75]. Moreover, the number of research experiments and, therefore, the research papers comes from the fact that wheat is one of the global staple crops, as well as the fact that crop management for wheat production is suitable for testing DA technologies [76,77,78]. The remaining papers, namely 20.3%, were review papers, while 5.1% were defined as other [79,80,81,82]. The selected papers were also analyzed by the publisher. Although the goal was not to favor specific journals and publishers, the analysis provided insight into the selection of researchers when presenting their research and work in general. Therefore, based on the selected criteria for systematic review, publishers such as Elsevier, MDPI, and Springer were predominantly selected for publishing papers. Moreover, out of 51 journals listed in the Clarivate Journal Citation Reports, 34 journals had an impact factor of up to 5.0, and 17 exceeded this value. It is also significant to emphasize that three journals had an IF higher than 10.0. These findings indicate that researchers prioritize moderate to high-impact journals, which can be connected with the growing implication of DA in diverse scientific fields and, therefore, in research, but also with the intention of researchers to select high-ranking publishers and journals for their scientific work.

4.2. An Overview of the Key Digital Technologies in Wheat Production over the Ten-Year Research Period

Digital technologies have delivered innovations in agriculture with high beneficial potential for future food production [35,83,84,85]. Crop production is going through a transition from industrial agriculture to smart agriculture, following the constant need for food security, but also, at the same level of importance, crop production is responding to challenges of improving soil health and water quality, and increasing biodiversity under the significant impact of climate change [86]. It is particularly important that digital technologies allow for monitoring site-specific weather conditions that are local and that affect crop production [87,88,89,90,91,92]. Sharma et al. [93] emphasized that, in addition to environmental aspects, smart agriculture also brings benefits to challenging energy and labor issues.

Out of the wide range of technologies that DA encompasses, the systematic review identified 43 articles in which machine learning was the keyword [94,95,96,97,98]. Gawdiya et al. [99] and Abbasi et al. [100] proved that machine learning is one of the most widely used techniques. This technology, with different models, can be effectively applied in different production aspects, water and soil management, disease detection [101,102], and pesticide use, and, therefore, enables farmers to make fast, cost-effective decisions [93,103,104,105,106,107]. Liakos et al. [108] and Xu et al. [109] stress that machine learning is a technique used in agriculture for the estimation of soil properties (moisture, pH, temperature, etc.), detection of diseases and weeds, determination of water and fertilization management, and prediction of precipitation events and crop yield. Therefore, this is in line with our analysis, in which machine learning techniques and algorithms were the most utilized for various applications and selected publications [110,111,112,113,114,115].

Remote sensing and deep learning were the next most frequent keywords, appearing in 34 and 29 articles, respectively [116,117,118,119,120,121,122]. Abbasi et al. [100] define deep learning as the extension of classical machine learning with a more complex approach to solving problems. The high number of selected papers that refer to deep learning [123] is in line with the statement of Gawdiya et al. [99] that using this technique on large datasets allows for more accurate and sophisticated yield prediction. According to Kamilaris and Prenafeta-Boldú [124], this technique is used not only in yield prediction but also in various parameters in crop production, such as soil properties, weather conditions, and pathogen infestation, as well as pest and weed detection, and, in general, crop growth. Therefore, this confirms the widespread adoption and application of deep learning across the reviewed literature [125,126,127,128,129,130]. Tchamyou et al. [131] noted that remote sensing, IoT, and big data are digital technologies that improve utilization efficiency and grain productivity through the production process. Remote sensing development accelerated the use of various platforms (satellite, UAVs, and ground) equipped with different sensors [2,132,133,134,135,136], enabling time- and cost-efficiency for obtaining data from large areas [137]. Within the performed literature review, satellites and UAVs were listed as the most used platforms, with 40.2% and 33%, respectively, while others were ground platforms [138,139,140,141,142,143]. This confirms that the most commonly used digital technologies selected in this review effectively reflect the practical aspect of their application in obtaining the spatial distribution of wheat production, providing scientific value and significant data within the given time frame and in a proper method [144,145,146,147].

4.3. The Feasibility and Promotion Barriers of DA Technologies in Wheat Production Among Smallholder Farmers

Wheat production, which is often applied in large areas, provides opportunities for research and the application of digital technologies. However, it is questionable to what extent these technologies are applied by small- and medium-sized farmers. Pretty et al. [148] state that implementing innovative agricultural technologies could help small and marginal farmers address the challenges of productivity, sustainability, and economic stability regarding limited resource access and market volatility. However, implementation is often very limited by different barriers, like socio-economic and technical issues.

The adaptability of digital technologies is related to investment costs, the provision of information and knowledge, and policy support, which differ between the regions and countries [149]. Small-scale farmers are workers of no more than 10 hectares of land, whereas DA technologies, like drones or precision seeders, are not economically viable for these cases, as they are created for large-scale, mechanized farms and are less suitable for dispersed land owners. On the other hand, sensors and IoT technologies can be affordable and very helpful for designing cost-effective production. However, reasonable application of inputs (fertilizers, pesticides) will be a more demanding action for addressing environmental issues, which can be applied with adequate machines and tools. Therefore, this creates a new socio-economic approach in creating cooperatives and farmers’ associations as a way to increase the productivity of wheat growers. Dhillon and Moncur [149] emphasize the importance of other actors, like local communities, policymakers, private companies, and higher education and research institutes, for digital advancements of small-scale farming. Indeed, digitalization requires not only the farmer’s actions, but also the support and actions of different stakeholders to develop production in local communities. In line with this, it should emphasize that, for instance, smallholders still form almost two-thirds of the farms in the EU [150], and account for between 60 and 80% of the food produced in Sub-Saharan Africa [151]. Also, the fact is that in the 2005–2016 period in the EU, the number of farms larger than 100 ha increased by almost 20% [152], and they are supported by 80% of CAP subsidies [153]. This situation diminishes the important role of smallholder farmers in food production, and, more precisely, diminishes their role in staple food, such as wheat production, but also in other significant aspects. They produce diverse food for local populations, and, subsequently, have higher crop diversity than large-scale farms, impacting biodiversity, improving soil fertility, and creating more resilient and environmentally friendly cropping systems. Therefore, promoting access to knowledge, digitalization, and innovation for a different scale of farmers significantly increases their capability to cope with new technologies and environmental and climate issues, which are economically supported.

5. Conclusions

Digitalization has become deeply present in all aspects of agriculture, and thus in wheat production. However, a global distribution of the digital technology’s application is still unevenly allocated; in some countries, it has reached the highest possible level, and in others, it is still on a very basic one. Nevertheless, it was noted that, in the examined period, circumstances, such as COVID, created an environment that promoted the use of digital technologies. On the other hand, more challenging weather conditions created an environment for more challenging wheat production, and, therefore, created a need for technologies that make site-specific decisions and for the application of inputs with respect to both ecological and economic considerations.

Future research on digital agriculture in wheat production should aim to address several important gaps and identify specific entry points for subsequent research. Firstly, there is a need for more advanced models that integrate multi-source data with differing spatiotemporal resolutions, incorporating dynamic growth cycles and varying environments for improving accuracy in wheat yield estimation [43]. Secondly, it is necessary to extensively investigate and validate the adaptability and scalability related to small- and medium-scale farmers’ engagement with digital approaches under differing socio-economic realities [154]. Thirdly, issues related to improving data-sharing models with adequate emphasis on privacy and security provisions for innovative mutual benefits have been largely untouched. Finally, there is a need for more studies on AI explanations for agricultural implementation and collaboration towards implementing digital twins and blockchain for wheat value chains.