AI-driven agricultural economics and farm decision support systems

. Weather forecasting, yield and market prediction, climate-risk assessment, and adaptive management are some of the climate-smart agriculture activities supported by AI, which also helps supply chains, insurance, credit assessment and other economic decisions (Jha et al., 2019) [29] . Nevertheless, high prices, lack of infrastructure, limited technical capacity, scattered landholding patterns, interoperability problems and concerns about data privacy and algorithm transparency continue to be significant obstacles, emphasizing the necessity for enabling infrastructure, policies, capacity development and responsible AI governance.

Evolution of Agricultural Economics in the Era of Artificial Intelligence

Agricultural economics has undergone a significant transformation from traditional production and resource allocation theoretical frameworks to smart and data-based decision support systems (Tantalaki et al., 2019) [65] . Farm management decisions historically were informed by record keeping, observation in the field, experience of the producer and generalized recommendations, which often did not adequately account for spatial or temporal variability within agricultural production. The development of precision agriculture brought in new technologies like Global Positioning Systems (GPS), Geographic Information Systems (GIS), remote sensing, and Variable Rate Technology (VRT) for site-specific management, and improved application of agricultural inputs (Sharma, 2023) [57] . These developments paved the way for the application of artificial intelligence in agricultural economics (Wolfert et al., 2017) [72] . The rapid growth of digital farming has also contributed to this evolution through the mass adoption of IoT sensors and other technologies, for example drones, satellite images, automated weather stations, cloud computing or mobilebased agriculture solutions (Dhal et al., 2024) [16] . These technologies produce constant streams of large volumes of real-time agronomic, environmental, climatic, and economic data that are beyond the capabilities of traditional statistical methods. AI addresses these limitations by leveraging machine learning and sophisticated predictive analytics to discover intricate interactions between production, market, and environmental variables, which results in improved and timelier economic decision-making (Knizia et al., 2025) [38] . Contemporary agricultural economics is more and more dependent on AI and its scope is expanding towards predicting yields, estimating costs of production, optimizing inputs, forecasting prices, evaluating risks, and analyzing profitability (Assimakopoulos et al., 2024; Redouane et al., 2025) [4,52] . Deep learning-based crop monitoring and disease diagnosis from images reinforcement learning-based management strategies that adapt to dynamic field environments are continually evolving. Explainable AI enhances transparency by delivering interpretable suggestions which lead to an increased trust of the farmers in the AI-enabled decision making. More recent developments have seen the introduction of generative AI and Large Language Models into the agricultural advisory space through intelligent conversational systems that simplify sophisticated scientific knowledge into actionable ad farm advice (Choi et al., 2025) [12] . At the same time, Farm DSSs have been turned into digital platforms that integrate agronomic, climatic, environmental and financial data to enable strategic, tactical and operational decision at the farm management level (Gerasymchuk et al., 2026) [25] . New era technologies such as digital twins, blockchain, autonomous robotics, cloudbased analytics enable more accurate production planning, transparent supply chain, and resource utilization optimization. As a result, agricultural economics now is in a transformative phase from being a descriptive economic analysis to a predictive and prescriptive intelligence, which derives farm profit maximization solution based on data while facilitating climate resilience, environmental sustainability, and long-term food and nutritional security (Tyagi and Tiwari, 2025) [66] .

Source: Aijaz et al., 2025 [2] International Journal of Advanced Biochemistry Research https://www.biochemjournal.com

Fundamentals of AI and Farm Decision Support Systems

Artificial intelligence (AI) is a computational system that can learn from data, identify patterns, make predictions and assist in decision making with limited human intervention (Jaboob et al., 2024) [28] . In farming, AI is now compiling data from satellites, drones, IoT sensors, weather stations, equipment, soil tests and market databases to deliver actionable, hyperlocal advice. Instead of static advisory systems, AI can evolve by learning from data to adapt to changing environment of production (Hossain et al., 2025) [26] . Farm Decision Support Systems (FDSS) are information technology-based systems intended to support the farmer and other stakeholders in taking agronomic and economic decisions in the farm production process. Contemporary FDSS integrate agroclimatic and environmental conditions with economic information to suggest the best crop varieties, sowing dates, irrigation schedules, fertilizer dosages, pest and disease control strategies, harvesting dates, and marketing alternatives. These systems have the effect of reducing risk, minimizing input use, maximizing operating efficiency, and increasing farm profitability (Achille and Velamuri, 2026) [1] . A standard AI-powered FDSS may include four key elements: data acquisition, data processing, decision analytics and information delivery (Panda, 2021) [47] . Data are gathered by IoT sensors, remote sensing, drones, weather stations, and market information systems. Machine learning and predictive models processed these datasets to detect patterns, predict outcomes, and provide suggestions for management. Ultimately, the information is provided to the end user through mobile applications, web portals, or cloud-based solutions, delivering decision in real time. With the rapid development of AI based-technologies, FDSS are now considered essential for precision farming, climate change adaptation and sustainable agriculture management (Khwidzhilli and Ijatuyi, 2026) [37] .

AI Technologies Used in Agricultural Economics

Artificial intelligence encompasses a broad range of computational technologies that improve agricultural decision-making by analyzing complex datasets and generating predictive insights. These technologies support production planning, economic optimization, market forecasting, and resource management, thereby enhancing both productivity and profitability. [40,74] .

Machine Learning

Generative AI and Large Language Models

Generative AI and Large Language Models (LLMs) are novel agri-advisory tools that comprehend and produce human-like language. [13,41] .

Reinforcement Learning

Reinforcement learning (RL) is a sophisticated AI methodology that enables algorithms to learn optimal management policies by interacting with the farming system and being informed by the outcomes of the decisions. Different from traditional predictive models, RL is a sequential decision-making process which enables the system to find a sequence of actions to achieve the greatest long-term productivity and economic benefits. In precision irrigation scheduling, fertilizer management, greenhouse climate control, autonomous agricultural machinery, robotic harvesting, and adaptive pest management, reinforcement learning is gaining popularity in agriculture. As more field data are generated, RL algorithms adapt and update their advice such that intelligent and dynamic farm management is possible in an evolving climatic and production scenario (Khanna et al., 2024; Liu, 2025) [35,42] .

Explainable AI

Explainable Artificial Intelligence (XAI) provides a more complete understanding of the processes behind the predictions or recommendations, notably the input features used in the decision-making process. Even though many complex AI models function as "black boxes", not allowing users to know the logic behind results. XAI maps this constraint by disaggregating the underlying factors that drive each recommendation and in the process building greater confidence with farmers, researchers, financial institutions, and policymakers. [73,17] . [14,33] .

Computer Vision

Internet of Things (IoT) and Edge AI

The Internet of Things (IoT) combines agricultural sensors and equipment, weather stations, drones, and irrigation systems into a connected system to monitor soil, crop, and environmental conditions in real time (Duguma and Bai, 2024) [20] . Edge AI processes this data at or near the data source, eliminating communication latency, bandwidth consumption, and dependency on cloud connectivity. IoT combined with Edge AI can intervene quickly with irrigation and nutrition management, greenhouse automation, livestock monitoring, and air control, thus reducing resource use and enhancing efficiency (Khan et al., 2023; Renda, 2024) [34,53] .

Digital Twins

Digital twins are digital constructs of agricultural systems that integrate live sensor data, simulation models, and artificial intelligence to mirror field conditions and forecast field performance. They can model crop growth, soil processes, irrigation, machinery operations, climate impacts and farm finances under what-if scenarios. By assessing the management concepts in advance of use, digital twins contribute to the optimal use of inputs, minimizing risks, enhancing production scheduling, and providing assistance for financial decisions. They are also considered to be more and more promising for precision farming, climate adaptation, sustainability assessment and long-term farm management (Verdouw et al., 2021; van Evert et al., 2026) [68, 67] .

AI Applications in Agricultural Economics

Artificial intelligence has become a key driver of agricultural economics by enabling data-driven decisionmaking across the entire agricultural value chain. By integrating agronomic, climatic, financial, and market information, AI helps optimize resource allocation, improve profitability, reduce production risks, and enhance the overall efficiency of farming systems. Its applications extend from production planning and financial management to market forecasting, supply chain optimization, and sustainable resource utilization (Weersink et al., 2018) [70] .

Production Economics

Artificial intelligence enhances the production economics by ensuring the optimal use of land, labour, water, fertilizers, pesticides, and machinery. Machine learning uses soil, weather, crop growth and yield data, as well as management information to suggest the most profitable cropping system and site-specific input application. Decision support based on AI is used to support decisions related to crop and variety selection, planting dates, irrigation and nutrient management, as well as pest and disease forecasting. These applications lead to higher efficiency in use of inputs, reduced production costs and environmental load, increased productivity and improved farm profit and farm sustainability (Talaviya et al., 2020) [64] .

Farm Financial Management

Artificial intelligence contributes to managing farm finances with smart budgeting, cash flow projection, investment scheduling, and analysis of profitability. By combining production records, input and labor costs, machinery costs, weather information, and market prices, AI applications allow farmers to monitor performance and financial status in real-time and help in predictive financial analysis. They enable you to evaluate risk, investment, compare alternative enterprises, and identify efficiency gains. In turn, AI enables informed business planning, strengthens financial stability, and fosters sustainable farm management (Miller et al., 2023) [44] .

Cost-Benefit Analysis

Artificial intelligence enhances cost-benefit calculations by combining information on production as it occurs, weather forecasts, market information, availability of labor, and risk factors. Using predictive modeling and scenario simulations, the AI models also compare technologies, irrigation systems, nutrient regimens, mechanization alternatives, and crop types. These studies provide estimates of production costs, returns, risk to the investment, and the benefit-cost ratio across a range of situations and have been shown to support sound decisions by farmers, the agribusiness community, and policy makers regarding technology use, investment, and agriculture policy while mitigating economic uncertainty (Antle et al., 2017; Olutumise, 2026) [3,46] .

Yield and Profit

Forecasting AI provides precise prediction of crop yield and farm profitability through the integration of information on weather, soil, crop growth, satellite and UAV images, and historical production. Along with production costs, labor and input needs, the AI generates preliminary estimates for farm income and profitability when market prices are available. These projections inform production planning, marketing, storage, and financial choices, and help mediate risks associated with climate, pests, and market volatility (Carolan, 2024; Khin and Lee, 2026) [10,36] .

Market Intelligence

Based on commodity prices, consumers demand, weather activities, transportation cost, trade, policies and market trend, the intelligence systems driven by AI can examine and then predict the opportunities and demands. They are also used to analyze consumer preferences and supply and demand and identify profitable markets, buyers and selling periods. This enhances the efficiency of marketing, reduces uncertainty, empowers farmers in bargaining, and may lead to an increase in farm income (Pauw et al., 2023; Ladhar et al., 2023) [49,39] .

Price Forecasting

Agricultural commodity prices vary by production, demand, weather, transport, government policies, and international trade. The AI-based prediction models combine historical price trends and weather data with supply demand relationships, trade patterns and macroeconomic indicators to enhance price prediction. Forecast accuracy thus allows farmers to make better decisions on harvesting, storage, transportation, procurement and marketing, reducing financial risks, increasing bargaining power, and enhancing farm profitability (Farh et [23,62,6] .

Supply Chain Optimization

Artificial intelligence (AI) contributes to agricultural supply chain management through demand forecasting, logistics, transport, inventory, warehousing, and quality of products. AI minimizes processing delay, storage loss, and postharvest decay and computer vision is used for quality grading; intelligent logistics optimize delivery routes and warehouse utilization. Traceability, transparency and food safety are more securely established through integration with blockchain. Such applications contribute to lower operating costs, increased market efficiency, added value of products, and more competitive agriculture businesses (Ivanov, 2023; Dhal and Kar, 2024; Kanyepe et al., 2025) [27, 15, 32] .

Crop Insurance

AI is revolutionizing agricultural insurance with the fast evaluation of crop damages, risk modeling, premium calculation and claims procedure automation. Machine learning processes satellite and drone imagery, weather data, and remote sensing information to determine damages caused by droughts, floods, pests, diseases, and more. AI increases claim speed and transparency, reduces fraud, and facilitates objective compensation. Predictive risk models also help design tailored insurance products and appropriate premium levels that enhance risk management and delivery of financial resources to farmers in a timely manner (Benami et al., 2021; Prakash and Saravanan, 2023; Werulkar and Pande, 2025) [7,50,71] .

Credit Risk Assessment

AI enhances agricultural credit evaluation by processing farm outputs, financial statements, weather-related risks, repayment patterns, market behaviour, digital transactions, and satellite imagery. Analysis with AI enables more precise, data-driven risk analysis, which leads to more efficient lending and reduced defaults when compared to traditional methods. Besides, it has the potential to enable tailor-made financial products and increase access to institutional credit for smallholders with scant financial histories, thereby fostering investment in advanced technologies and sustainable practices (Fuster et al., 2022; Sun et al., 2025; Singh and Mritunjay, 2024) [24,63,58] .

Carbon Markets

AI is opening up new avenues in climate-smart agriculture via carbon trading and payments for ecosystem services. It can be used to calculate estimates for emissions of greenhouse gases and track soil carbon sequestration as well as to quantify carbon credits by integrating satellite imagery, remote sensing, IoT sensors, field data, and predictive models. AI also enhances the business of carbon crediting, monitoring, verification, certification, and transparent credit trading. These applications can also promote conservation agriculture, agroforestry, cover cropping, and minimum tillage while generating additional sources of income to the farmers (Smith et al., 2020; Satapathy et al., 2026) [60,56] .

Source: https://net.fftc.org.tw/smartnetzero/news.php?act=view&id=326

Precision Input Management

Precision input management is a major economic application of AI in modern agriculture. AI uses IoT sensors, satellite images, drones, weather information, soil information, crop models and GIS to decide the amount, time and place of irrigation, fertilizer, pesticide and other

inputs to be applied. Variable Rate Technology (VRT) allows site-specific application at varying rates according to crop requirements and field variation while minimizing over-and under-application. Real-time surveillance facilitates prompt actions that contribute to improved resource-use efficiency, decreased production cost, minimized environmental impact, enhanced productivity and profitability, and sustainable development of agroecosystems (Botero-Valenciae et al., 2025) [9] .

Source: Zia-ur-Rehman et al., 2025 [76] 6. AI-Driven Farm Decision Support Systems AI-based Farm Decision Support System (FDSS) merges the aspects of artificial intelligence, agronomic, climatic, environmental and economic information to enable the farm management to be evidence-based. These sources of information are integrated by these systems to offer realtime recommendations on crop choice, sowing dates, irrigation scheduling, fertilizer application, pest and disease handling, harvesting, and selling. Unlike conventional advisory systems, AI-powered FDSS continuously learn from new data, improving the accuracy and reliability of recommendations over time (Eastwood et al., 2022) [21] . Modern Farm Decision Support Systems (FDSS) utilize machine learning, predictive analytics, cloud computing, and mobile applications to assess alternative management scenarios and recommend decisions that are best economically. The integration with Geographic Information Systems (GIS) and remote sensing also makes site-specific management feasible considering field heterogeneity. As a result, AI-based decision support systems can increase resource-use efficiency, decrease production costs, risk, farm profit and is environment friendly sustainable agricultural practices (Sishodia et al., 2020) [59] .

Integration with Precision Agriculture

Precision agriculture, based on the integration of GPS, GIS, remote sensing, drones, IoT devices, and Variable Rate Technology (VRT), constitutes the technological enabler for AI-based farm management. These datasets are processed by AI to track growth of crops, soil fertility, moisture status, nutrient availability, and occurrence of pests with fine spatial and temporal granularity. This allows a site-specific use of irrigation water, fertilizers, and pesticides based on the real needs of the crops instead of uniform field applications (Saha et al., 2025) [54] . Autonomous tractors, robotic sprayers, intelligent irrigation systems and harvesting machines guided by Artificial Intelligence (AI) contribute among others to greater operational efficiency by reducing labour dependency and enhancing resource utilization. Persistent watching and prediction-based investigation bring about early identification of crop stress, leading to timely actions that increase productivity and decrease the exposure to environmental contaminants. Hence, AI integration with precision agriculture contributes to economically efficient, climate-resilient and sustainable farming systems (Awais et al., 2025) [5] .

Climate-Smart Farm Decision Support

Climate variability has heightened unpredictability in agricultural production, thus making rational decision support indispensible for adaptation to climate. AI combines historical weather data, climate models, satellite observations and real-time data from sensors and ground stations to predict droughts, floods, heat stress, frost events, and pest outbreaks. These predictions allow farmers to modify planting dates, use climate-resilient crop cultivars, manage water efficiently and take timely crop protection measures (Mohan et al., 2025) [45] . AI plays a role in climate change mitigation through enhanced fertiliser recommendations, better irrigation management and energy use, which lead to lower emissions of greenhouse gases from agricultural production. Digital climate advisory services deliver climate-smart, site-specific advice that enhance farm resilience, increase resource-use efficiency and promote climate-smart agriculture development amid growing environmental variability (Pasha Mohammed et al., 2025) [48] .

AI for Smallholder Farming

Smallholder farmers contribute significantly to the world's food production, but they are often constrained by a lack of technical knowledge, access to finance, and extension services. AI-based mobile apps and digitized advisories platforms are filling these gaps, offering cost-effective, realtime advisories on crop management, weather, pest diagnosis, fertilizer application and market prices in local languages. These innovations are designed to empower smallholders to make decisions based on data, without needing to be highly technical (Fabregas et al., 2019) [22] . AI also promotes financial inclusion through digital credit scoring, crop insurance, and farmer-centric mobile-banking services. Weather extremes and pest outbreak early warning systems mitigate production risks, and market intelligence supported by AI enables farmers to identify good marketing opportunities. With the growth of digital infrastructure, AI has the potential to bring about substantial improvements in productivity, income and livelihood security for smallholder farmers (Quarshie et al., 2025) [51] .

Challenges and Limitations

However, the application of artificial intelligence (AI) in agricultural economics is limited due to a number of technical, economic, and infrastructural barriers, despite its great promise. Adoption is also limited by high cost of investment in equipment that incorporates AI, by poor internet connectivity, by unreliable electricity, and by insufficient digital infrastructure, especially for small farmers in developing countries. Further, artificial intelligence systems require data of high quality, which is standardized, and available in near real-time; however, data on agricultural production is often fragmented, inconsistent and heterogeneous when obtained from different sources, such as satellites, Internet of Things (IoT) sensors, weather stations, and market databanks, which negatively affects the precision of decision support based on artificial intelligence (Junior et al., 2024) [31] . Limited digital literacy, lack of technical knowledge, insufficient extension services and farmer education also impede the pace of ai adoption. Concerns about data privacy, cybersecurity, transparency of algorithms, interoperability, and data ownership also decrease user confidence and responsible use. Tackling these issues through enhanced digital infrastructure, capacity building, explainable AI, robust data governance, and enabling policies will be key to realizing the broad-based and sustainable utilization of AI in agricultural economics.

Ethical, Legal and Policy Issues

The growth of AI in agriculture has raised significant ethical, legal, and policy issues. Ethical deployment of AI is also an issue of transparency, fairness, accountability and inclusiveness, to make sure that the benefits of technology reach all farmers, no matter the size of the farm, where it is located, or the status of the farmer. Algorithmic bias resulting from unrepresentative datasets may generate inaccurate recommendations for certain regions or farming systems, potentially widening existing inequalities in agricultural development (Jobin et al., 2019) [30] .

Data governance is another critical concern. With sensors, drones, machinery, and farm management platforms, farmers are producing an ever-growing wealth of digital information, yet clear guidelines on data ownership, privacy, sharing, and commercial use are still lacking in several countries. Developing secure data protection structures is necessary to protect farmers rights while promoting responsible innovation and collaboration with technology developers, researchers, and government bodies. Increasing use of autonomous machinery, AI-assisted advisory systems and automated decisions also poses legal challenges in terms of liability, responsibility and regulatory control. Governments need to establish standards for the validation, quality assurance, certification, and safety of AIbased applications in agriculture to enable reliable and transparent use in the field. Investments from the public shoulder in digital infrastructure, open-data schemes, research collaboration, and farmer capacity enhancement will further accelerate equitable AI adoption and support sustainable agriculture development (Birner et al., 2021) [8] .

Future Research Directions

Future research should be directed towards the establishment of robust, explainable and scalable AI systems that integrate agronomic, environmental, climatic and economic data in decision-support platforms. Advances in Generative AI, LLMs and multimodal AI will bring more personalized and interactive advisory services, farmers will get location-specific recommendations through natural language interfaces. Enhancing the multilingual capabilities of AI systems will also contribute to the strengthening of digital extension services, especially in developing countries (Zhu et al., 2025) [75] . Converging AI with digital twins, Internet of Things (IoT), 5G, edge computing, robotics, and blockchain will result in increasingly automated and smart farm systems. Digital twins will be used to model crop growth, resource use and economic performance under alternative management practices for strategic planning and climate adaptation. Traceability enabled through blockchain combined with AI analytics will enhance the transparency of the supply chain, food safety and consumers trust (Casino et al., 2021) [11] . Future AI research should also focus on climate-smart agriculture, carbon accounting, ecosystem services valuation, and sustainable resource management. Sophisticated predictive models for climate risk, greenhouse gas emissions, soil carbon sequestration, and biodiversity will underpin environmentally responsible policies for agriculture. Moreover, the combination of AI with socioeconomic models will enhance at the farm level investment planning, rural development and agricultural policy analysis (Junior et al., 2024) [31] . Collaborative research between universities, government agencies, private technology companies, extension organizations, and farmers will be critical to creating accessible, transparent, and producer-focused AI applications. Standardized datasets, open-source AI frameworks, and international cooperation will further accelerate innovation while ensuring equitable access to digital agriculture technologies worldwide.

Conclusion

Artificial intelligence is revolutionizing agricultural economics towards a predictive and data-informed science

References

1. Achille AA, Velamuri V. Exploring the influence of digital farming technology on agricultural knowledge and advice networks: a case study of RiceAdvice application in Nigeria. The Journal of Agricultural Education and Extension. 2026:1-38.
2. Aijaz N, Lan H, Raza T, Yaqub M, Iqbal R, Pathan MS. Artificial intelligence in agriculture: advancing crop productivity and sustainability. Journal of Agriculture and Food Research. 2025;20:101762.
3. Antle JM, Jones JW, Rosenzweig CE. Next generation agricultural system data, models and knowledge products: introduction. Agricultural Systems. 2017;155:186-190.
4. Assimakopoulos F, Vassilakis C, Margaris D, Kotis K, Spiliotopoulos D. Artificial intelligence tools for the agriculture value chain: status and prospects. Electronics. 2024;13(22):4362.
5. Awais M, Wang X, Hussain S, Aziz F, Mahmood MQ. Advancing precision agriculture through digital twins and smart farming technologies: a review. AgriEngineering. 2025;7(5):137.
6. Basha SA, Krishna VSB, Shanker SS, Sravya R, Shivalingam N, Dharnasi P. AI-powered price prediction for agriculture markets. International Journal of Engineering & Extended Technologies Research (IJEETR). 2026;8(2):551-556.
7. Benami E, Jin Z, Carter MR, Ghosh A, Hijmans RJ, Hobbs A, et al. Uniting remote sensing, crop modelling and economics for agricultural risk management. Nature Reviews Earth & Environment. 2021;2(2):140159.
8. Birner R, Daum T, Pray C. Who drives the digital revolution in agriculture? A review of supply-side trends, players and challenges. Applied Economic Perspectives and Policy. 2021;43(4):1260-1285.
9. Botero-Valencia J, García-Pineda V, Valencia-Arias A, Valencia J, Reyes-Vera E, Mejia-Herrera M, et al. Machine learning in sustainable agriculture: systematic review and research perspectives. Agriculture. 2025;15(4):377.
10. Carolan M. Who and what gets recognized in digital agriculture: agriculture 4.0 at the intersectionality of (dis)ableism, labor, and recognition justice. Agriculture and Human Values. 2024;41(4):1465.
11. Casino F, Kanakaris V, Dasaklis TK, Moschuris S, Stachtiaris S, Pagoni M, et al. Blockchain-based food supply chain traceability: a case study in the dairy sector. International Journal of Production Research. 2021;59(19):5758-5770.
12. Choi JW, Hidayat MS, Cho SB, Hwang WH, Lee H, Cho BK, et al. Recent trends in machine learning, deep learning, ensemble learning, and explainable artificial intelligence techniques for evaluating crop yields under abnormal climate conditions. Plants. 2025;14(18):2841.
13. De Clercq D, Nehring E, Mayne H, Mahdi A. Large language models can help boost food production, but be mindful of their risks. Frontiers in Artificial Intelligence. 2024;7:1326153.
14. Dhaka VS, Meena SV, Rani G, Sinwar D, Ijaz MF, Woźniak M. A survey of deep convolutional neural networks applied for prediction of plant leaf diseases. Sensors. 2021;21(14):4749.
15. Dhal SB, Kar D. Transforming agricultural productivity with AI-driven forecasting: innovations in food security and supply chain optimization. Forecasting. 2024;6(4):925-951.
16. Dhal S, Wyatt BM, Mahanta S, Bhattarai N, Sharma S, Rout T, et al. Internet of Things (IoT) in digital agriculture: an overview. Agronomy Journal. 2024;116(3):1144-1163.
17. Dipu K, Tyagi N, Khan R. Explainable AI-based crop price prediction model for robust stakeholder decisionmaking. In: 2026 IEEE 6th International Conference on Computing, Power, and Communication Technologies (IC2PCT); 2026 May. IEEE; 2026. p. 1-8.
18. Ditcharoen S, Sirisomboon P, Saengprachatanarug K, Phuphaphud A, Rittiron R, Terdwongworakul A, et al. Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy. Artificial Intelligence in Agriculture. 2023;7:35-43.
19. Domingues ARM. Competências digitais nos locais de trabalho: uma revisão sistemática da literatura [master's thesis]. Porto: Instituto Politécnico do Porto; 2022.
20. Duguma AL, Bai X. How the internet of things technology improves agricultural efficiency. Artificial Intelligence Review. 2024;58(2):63.
21. Eastwood CR, Dela Rue B, Edwards JP, Jago J. Responsible robotics design: a systems approach to developing design guides for robotics in pasture-grazed dairy farming. Frontiers in Robotics and AI. 2022;9:914850.
22. Fabregas R, Kremer M, Schilbach F. Realizing the potential of digital development: the case of agricultural advice. Science. 2019;366(6471):eaay3038.
23. Farh MM, Arvydas R, Adas M, Viktor G. Recent advancements in the additive manufacturing of fiberreinforced polymeric composites. Reviews on Advanced Materials Science. 2026;65(1).
24. Fuster A, Goldsmith-Pinkham P, Ramadorai T, Walther A. Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance. 2022;77(1):5-47.
25. Gerasymchuk N, Vialets O, Dashchenko H, Dzemishkevych I. Intelligent decision support systems in precision agriculture and resource conservation. Financial and Credit Systems: Prospects for Development. 2026;2(21):195-205.
26. Hossain MS, Rahman M, Rahman A, Kabir MM, Mridha MF, Huang J, et al. Automatic navigation and International Journal of Advanced Biochemistry Research https://www.biochemjournal.com self-driving technology in agricultural machinery: a state-of-the-art systematic review. IEEE Access. 2025.
27. Ivanov D. The Industry 5.0 framework: viability-based integration of the resilience, sustainability, and humancentricity perspectives. International Journal of Production Research. 2023;61(5):1683-1695.
28. Jaboob A, Durrah O, Chakir A. Artificial intelligence: an overview. In: Engineering applications of artificial intelligence. 2024. p. 3-22.
29. Jha K, Doshi A, Patel P, Shah M. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 2019;2:1-12.
30. Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nature Machine Intelligence. 2019;1(9):389-399.
31. Junior MRB, de Almeida Moreira BR, dos Santos Carreira V, de Brito Filho AL, Trentin C, de Souza FLP, et al. Precision agriculture in the United States: a comprehensive meta-review inspiring further research, innovation, and adoption. Computers and Electronics in Agriculture. 2024;221:108993.
32. Kanyepe J, Chibaro M, Morima M, Moeti-Lysson J. AI-powered agricultural supply chains: applications, challenges, and opportunities. In: Integrating agriculture, green marketing strategies, and artificial intelligence. 2025. p. 33-64.
33. Karanam SR, Pradeep Kumar AB, Yandrapati PB, Tangudu N, Peddada N, Bollipelly PG. A systematic review: how computer vision is transforming agriculture in economic growth. In: International Conference on Artificial Intelligence and Knowledge Processing; 2023 Oct. Cham: Springer Nature Switzerland; 2023. p. 229-244.
34. Khan S, Marwat SNK, Khan MA, Ahmed S, Gohar N, Alim ME, et al. A self-decoupling technique to realize dense packing of antenna elements in MIMO arrays for wideband sub-6 GHz communication systems. Sensors. 2023;23(2):654.
35. Khanna M, Atallah SS, Heckelei T, Wu L, Storm H. Economics of the adoption of artificial intelligencebased digital technologies in agriculture. Annual Review of Resource Economics. 2024;16(1):41-61.
36. Khin O, Lee S. Integrating crop types, yield products, and price forecasting with explainable AI for agricultural APIs. Crops. 2026;6(4):67.
37. Khwidzhilli RH, Ijatuyi EJ. Leveraging digital tools for sustainable precision in Agriculture 3.0-5.0: a scoping review of trends, benefits, and challenges. Frontiers in Sustainable Food Systems. 2026;10:1768902.
38. Knizia D, Yuan J, Meksem K, Kassem MA. Harnessing machine learning for transformation in agricultural sciences: a review. Journal of Artificial Intelligence, Machine Learning, and Bioinformatics. 2025:16-28.
39. Ladhar R, Sharma S, Tangirala S, Gupta N, Azeem A, Jain A, et al. AI-based market intelligence systems for farmer collectives: a case study from India. ACM Journal on Computing and Sustainable Societies. 2023;1(1):1-32.
40. Learning D, Goodfellow I, Bengio Y, Courville A. ECE49595CV: computer vision fall 2023 course information course number and title: ECE49595CV (003) computer vision CRN: 24034. 2023.
41. Li M, Basit A, Javed A, Song X, Ge Y. Generative artificial intelligence and ChatGPT in agriculture supply chain management: a systematic literature review and future research agenda. British Food Journal. 2026;128(13):121-141.
42. Stillwater (OK): Oklahoma State University; 2025.
43. Mahmood A, Javaid M, Nadeem M, editors. Improving crop quality and enhancing sustainability in agriculture. CRC Press; 2025.
44. Miller T, Mikiciuk G, Durlik I, Mikiciuk M, Łobodzińska A, Śnieg M. The IoT and AI in agriculture: the time is now -a systematic review of smart sensing technologies. Sensors. 2025;25(12):3583.
45. Mohan RJ, Rayanoothala PS, Sree RP. Next-gen agriculture: integrating AI and XAI for precision crop yield predictions. Frontiers in Plant Science. 2025;15:1451607.
46. Olutumise AI. A systematic review of the economic impact of artificial intelligence on agricultural productivity, sustainability, and rural livelihoods. Discover Agriculture. 2026;4(1):70.
47. Panda SP. AI in decision support systems. AI in Decision Support Systems. 2021.
48. Pasha Mohammed S, Deepika J, Sritharan N, Ravichandran V, Prasanthrajan M, Kannan P. A systematic literature review on artificial intelligence in transforming precision agriculture for sustainable farming: current status and future directions. Plant Science Today. 2025;12(2):1-13.
49. Pauw K, Ecker O, Thurlow J, Comstock AR. Measuring changes in diet deprivation: new indicators and methods. Food Policy. 2023;117:102471.
50. Prakash MC, Saravanan P. Crop insurance premium recommendation system using artificial intelligence techniques. International Journal of Professional Business Review. 2023;8(4):17.
51. Quarshie PT, Antwi-Agyei P, Fraser ED. Climate-smart agriculture practices for sustainable food systems and rural sustainable livelihoods nexus in sub-Saharan Africa: a conceptual review and analytical framework. 2025.
52. Redouane K, Fikri Y, Garine R, Nusrat S, Veernapu K, Batumalay M, et al. An overview of artificial intelligence-driven intelligent agronomy for sustainable productivity and agri-finance transformation. ES Food and Agroforestry. 2025;22:1938.
53. Renda A. AI and the transformation of agricultural work: economic, social, and environmental implications. In: Handbook of artificial intelligence at work. Edward Elgar Publishing; 2024. p. 96-118.
54. Saha S, Kucher OD, Utkina AO, Rebouh NY. Precision agriculture for improving crop yield predictions: a literature review. Frontiers in Agronomy. 2025;7:1566201.
55. Saiz-Rubio V, Rovira-Mas F. From smart farming towards agriculture 5.0: a review on crop data management. Agronomy. 2020;10(2):207.
56. Satapathy S, Mina KK, Saha P, Shekhar S, Jhariya MK, Banerjee A, et al. Application of artificial intelligence to evaluate carbon credits in agriculture. In: Carbon incentives, plans, and policies in agriculture. Academic Press; 2026. p. 205-219.
57. Sharma S. Precision agriculture: reviewing the advancements, technologies, and applications in precision agriculture for improved crop productivity and resource management. Reviews in Food and Agriculture. 2023;4(2):45-49.
58. Singh R, Mritunjay M. AI-driven credit risk assessment in agriculture: a case study of Indian commercial banks. International Journal of Innovations in Science, Engineering and Management. 2024:118-125.
59. Sishodia RP, Ray RL, Singh SK. Applications of remote sensing in precision agriculture: a review. Remote Sensing. 2020;12(19):3136.
60. Smith P, Soussana JF, Angers D, Schipper L, Chenu C, Rasse DP, et al. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Global Change Biology. 2020;26(1):219-241.
61. Storm H, Baylis K, Heckelei T. Machine learning in agricultural and applied economics. European Review of Agricultural Economics. 2020;47(3):849-892.
62. Sun F, Meng X, Zhang Y, Wang Y, Jiang H, Liu P. Agricultural product price forecasting methods: a review. Agriculture. 2023;13(9):1671.
63. Sun P, Jia Y, Shi Y, Ren J, Li Z, Li X. Artificial intelligence credit risk assessment model based on MLP-hybrid clustering. Complexity. 2025;2025(1):3308222.
64. Talaviya T, Shah D, Patel N, Yagnik H, Shah M. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture. 2020;4:58-73.
65. Tantalaki N, Souravlas S, Roumeliotis M. Data-driven decision making in precision agriculture: the rise of big data in agricultural systems. Journal of Agricultural & Food Information. 2019;20(4):344-380.
66. Tyagi AK, Tiwari S. Blockchain and digital twin applications in smart agriculture. New York: CRC Press; 2025. p. 29-47.
67. van Evert FK, Dia K, van Oort PA, Henning C, Ziesmer J. From innovation to impact: exploring the economic potential of digital twins in European and African agriculture. In: Moving the technology frontiers in African agrifood systems. AKADEMIYA2063; 2026. p. 250-274.
68. Verdouw C, Tekinerdogan B, Beulens A, Wolfert S. Digital twins in smart farming. Agricultural Systems. 2021;189:103046.
69. Vuppalapati C. Machine learning and artificial intelligence for agricultural economics: prognostic data analytics to serve small scale farmers worldwide. Springer Nature; 2021.
70. Weersink A, Fraser E, Pannell D, Duncan E, Rotz S. Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics. 2018;10(1):19-37.
71. Werulkar J, Pande M. Integrating AI, IoT, and blockchain for real-time crop insurance claim automation. In: 2025 International Conference on Sustainable Technologies for Humanity and Smart World (HSWTech); 2025 Sep. IEEE; 2025. p. 1-5.
72. Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big data in smart farming: a review. Agricultural Systems. 2017;153:69-80.
73. Yang F, Zhang D, Zhang Y, Zhang Y, Han Y, Zhang Q, et al. Prediction of corn variety yield with attributemissing data via graph neural network. Computers and Electronics in Agriculture. 2023;211:108046.
74. Zheng Y, Xu Z, Xiao A. Deep learning in economics: a systematic and critical review. Artificial Intelligence Review. 2023;56(9):9497-9539.
75. Zhu H, Qin S, Su M, Lin C, Li A, Gao J. Harnessing large vision and language models in agriculture: a review. Frontiers in Plant Science. 2025;16:1579355.
76. Zia-ur-Rehman M, Usman M, Ahmed R, Ahmad A, Akram T, Maqsood A, et al. Precision input management for reducing resource wastage and enhancing production efficiency. In: Agriculture toward net zero emissions. Academic Press; 2025. p. 283-304.