New standards steering digital agriculture

July 29, 2022 


The next wave of technological progress to sustain the world’s fastgrowing global population will capitalize on artificial intelligence (AI) and the Internet of Things (IoT) to improve the precision and sustainability of farming techniques.


AI, IoT, connected services and autonomous systems together enable farmers to make decisions at the level of a single square metre or individual plant or animal, rather than entire fields or all livestock. This precision allows wellinformed interventions that ultimately improve agricultural sustainability by helping farmers produce more, with less.


A new International Telecommunication Union (ITU) Focus Group dedicated to “AI and IoT for digital agriculture” will examine emerging cyberphysical systems as groundwork for standardization to stimulate their deployment for agriculture worldwide.


“The projection that our planet will host 9.7 billion people by 2050 necessitates significant technological progress to sustain so many lives,” said ITU Secretary-General Houlin Zhao. “This new focus group is the beginning of a global drive to ensure equitable access to the new capabilities emerging in agriculture with advances in digital technology.”


The focus group will work in close collaboration with the Food and Agricultural Organization of the United Nations (FAO), which mobilizes international efforts to defeat hunger and improve nutrition and food security. Under the group’s purview will be new capabilities to discern complex patterns from a growing volume of agricultural and geospatial data; improve the acquisition, handling, and analysis of these data; enable effective decision-making; and guide interventions to optimize agricultural production processes.


“New digital capabilities offer us a unique and immediate opportunity to transform food systems and accelerate impact towards zero hunger. The new focus group will significantly contribute towards these efforts, bringing together AI and IoT as key enablers behind new capabilities for digital agriculture.” Dejan Jakovljevic, Chief Information Officer and Director of FAO’s Digitalization and Informatics Division said.


The envisaged study aims to support global progress in areas such as precision farming, predictive analytics for smart farming, the optimization of cultivable acreage, remote cattle monitoring and management, agricultural robotics and greenhouse automation…

Global IoT in Agriculture Markets Report 2022: Revenues for Monitoring will Reach $7.31 Billion Globally by 2027

Dublin, June 16, 2022 (GLOBE NEWSWIRE)  --  The "IoT in Agriculture Market by Technology, Automation (Robots, Drones, and Smart Equipment), Sensor Types, Hardware, Software and Solutions 2022 - 2027" report has been added  to's offering.

This report assesses the technologies, companies, and solutions for IoT in agriculture. The report evaluates the overall marketplace and provides forecasts for sensors (and other devices), services, solutions, and data analytics globally, and regionally for the period 2022 to 2027. Forecasts include precision agriculture, indoor farming, livestock, and fisheries.


There is currently an acute need for greater agricultural efficiency and effectiveness in the week of the recent pandemic. Many agricultural commodities such as corn, soy, and cotton are in backwardation as of the publication of this report, which means that the current price of an underlying asset is higher than prices trading in the futures market. This is atypical for commodities as inflation generally tends to make their prices increase over time.


However, recent material and supply chain related shortages, coupled with an uptick in economic activity, has led to unbalanced supply and demand dynamics. This is reflected in the Bloomberg Agriculture Spot Index, which measures the price movements of agricultural commodities, which has risen from 227.38 on May 15th, 2020 to a high of 386.47 on April 23rd, 2021, representing a 70% increase during that time period.

While the aforementioned commodity price and supply challenges represent a more near-term acute issue, there remain longer-term structural market drivers for improvements in agricultural technologies. As the world population grows, so does the demand for food. The UN estimates that Earth will need to produce 70% more food by 2050 to support these growing populations. Complicating matters, natural resources are slowly being depleted and usable agricultural land is shrinking.

There is an ever-increasing need for intelligent and highly scalable agriculture solutions. Increasingly, the agriculture business is becoming controlled by companies that are not conventional agriculture experts. The publisher sees a shift from conventional agriculture to farm management. With this shift, software developers and predictive data analytics companies will take control of end-to-end agricultural operations.

Agriculture has transformed in the last few decades from small to medium farming operations to highly industrialized, commercial farming that is concentrated among a few large corporations. However, as various Internet of Things (IoT) technologies mature beyond the R&D phase and go into general production, costs for everything from drones/UAVs to sensors will continually decrease, making connected agriculture more accessible to smaller farms and third world countries.

With this agricultural transformation, farming operations are increasingly a highly mechanized and computer-driven operation. This allows corporations to treat agriculture like manufacturing in the sense that measurements, data, and control is very important to manage costs, maximize yields, and boost profits.

This shift in managing agricultural operations will bring various benefits to farming and livestock management, including enhanced crop quality and quantity, improved use of resources and farm equipment, real-time monitoring of farms, animals, and machines, automated irrigation systems, fertilizer spraying, and pest control.

The implementation of combined AI and IoT solutions for agriculture will provide a substantial lift for both operational efficiency and effectiveness. These Artificial Intelligence of Things (AIoT) solutions will transform the interpretation and use of IoT data from a largely human-based activity to one that is primarily machine-oriented.

This will lead to fewer errors and savings in operational costs such as data analytics visualization for the sake of human viewing, interpretation, and decision-making. For example, the Artificial Intelligence of Things (AIoT) Solutions: AIoT Market by Application, Service, and Industry Vertical 2022 - 2027 report Identifies a $2.38 billion global opportunity for AIoT solutions in agricultural

Select Report Findings:

 - The largest IoTAg application by revenue is monitoring, which will reach $7.31 billion globally by 2027

 - The fastest growing IoT solution area by revenue is automation, which will grow at a CAGR of 64.1% through 2027

 - The market for smart sensor systems will reach $2.93 billion globally by 2027, growing at a CAGR of 67.1% through 2027

 - The global market for agriculture drones will reach $1.39 billion by 2027 with an average price of $1,250 per unit for UAVs

 - Intelligent solutions for aquaculture operations will $1.2 billion globally by 2027, which we see as a significantly underserved market

 - The implementation of combined AI and IoT solutions for agriculture will provide a substantial lift for both operational efficiency and effectiveness monitoring alone.

AI and the future of our food

By Erin Blakemore

February 28, 2022 at 9:00 a.m. EST


Robots. Drones. Artificial Intelligence


All three are touted as potential saviors for farmers, and are already being deployed on large farms, where they assist with such tasks as managing crops, milking cows and helping farmers make decisions about their land.


At least that’s the message of a new analysis in Nature Machine Intelligence. When an international group of researchers examined AI in agriculture, they found a variety of possible risks — and they’re urging farmers to consider them before it’s too late.


“So far no one seems to have asked the question, ‘Are there any risks associated with a rapid deployment of agricultural AI?,' ” said Asaf Tzachor, a researcher at the Centre for the Study of Existential Risk at the University of Cambridge, in a news release.


The potential benefits are huge. Increases in farm productivity could help feed the approximately 2.4 billion people around the world who experience food insecurity and malnutrition and revolutionize the way farmers use their land


That could come at a cost. The analysis points out potential flaws in the agricultural data that fuels AI-powered systems and the possibility that autonomous systems could place productivity over the environment. That could lead to inadvertent errors causing overfertilization, dangerous pesticide use, inappropriate irrigation or erosion, risking crop yields, water supplies and soil. And wide-scale crop failures could exacerbate food insecurity.


Cybersecurity is another potential failure point. The researchers said cyberattacks could disrupt entire food systems. The more reliant farm systems are on intelligent machines, the more disruption could be created if they malfunction or are destroyed.


Then there are people — and without inclusive technology, the researchers warn, AI could simply increase inequities that already exist in farming. As big farmers profit, small-scale farmers in the global South, for instance, might be locked out of farming gains altogether.


Potential solutions mentioned by the researchers include data sharing, citizen input and digital “sandboxes” where developers can forecast potential failure points for farm AI.


“Technological modernization in farming has achieved much,” the researchers write. But irresponsible developers could “ignore and thereby perpetuate drivers of nutritional insecurity, exploitation of labor, and environmental resources depletion.”


machine intelligence


Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities

Asaf Tzachor - 1,2 ,  Medha Devare - 3,4,  Brian King - 3, Shahar Avin - 1 and Seán Ó hÉigeartaigh - 1,5 


Global agriculture is poised to benefit from the rapid advance and diffusion of artificial intelligence (AI) technologies. AI in agriculture could improve crop management and agricultural productivity through plant phenotyping, rapid diagnosis of plant disease, efficient application of agrochemicals and assistance for growers with location-relevant agronomic advice. However, the ramifications of machine learning (ML) models, expert systems and autonomous machines for farms, farmers and food security are poorly understood and under-appreciated. Here, we consider systemic risk factors of AI in agriculture. Namely, we review risks relating to interoperability, reliability and relevance of agricultural data, unintended socio-ecological consequences resulting from ML models optimized for yields, and safety and security concerns associated with deployment of ML platforms at scale. As a response, we suggest risk-mitigation measures, including inviting rural anthropologists and applied ecologists into the technology design process, applying frameworks for responsible and human-centred innovation, setting data cooperatives for improved data transparency and ownership rights, and initial deployment of agricultural AI in digital sandboxes.

For more than a century, technological innovation has been the main route to increasing agricultural productivity. New plant varieties and chemical formulations for nutrient management and pest control have improved farm productivity and profitability. With an estimated 2 billion people afflicted by food insecurity, including some 690 million malnourished people and 340 million children suffering micronutrient deficiencies1, advanced technologies, such as AI and its subset, ML, promise further substantial benefits for agricultural intensification and food and nutritional security2. ML may support, and in several instances enable, rapid plant phenotyping, monitoring of farmlands, in situ assessment of soil composition, disease diagnosis and surveillance, facilitation of automation and bundling of agro-chemical application, weather forecasting, yield prediction, decision support systems (DSS) with real-time agronomic advice, and new methods for post-harvest handling and traceability. However, technological modernization in agriculture has also contributed to ecological degradation, including water and land contamination, and soil erosion, which may ultimately undermine food security3–5. Moreover, prioritization of a small number of plant varieties has resulted in the loss of over 75% of crop genetic diversity6. In some instances, agricultural industrialization has increased human suffering, including via exposure to detrimental chemicals7, and social exploitation8. In other instances, mechanization in farming has moved in lockstep with land consolidation9, as owners of small and fragmented parcels often lacked the means to invest in advanced machinery and compete with large landholders who exploited economies of scale. Increase in farm sizes and mechanization carried considerable benefits for labour efficiency, agricultural output and profitability10,11, yet has also resulted in displaced labour, wage loss, and detrimental changes to rural landscapes and communities - 12,13.

These are not failures of technology as such, but rather failures to anticipate and account for the impacts of technology. Comprehensive risk assessment and technology governance frameworks may help to avoid future pitfalls, and exacerbation of current predicaments, in the widespread and rapid diffusion of agricultural AI. To anticipate problems and advance mitigation actions, in this Perspective we first analyse systemic risks in data management, AI and ML design, and wide-scale system deployment. Within data management, we pay particular attention to issues of data findability, accessibility and interoperability. Within AI and ML design, we highlight the dynamics through which models may compromise ecosystems as well adversely affect smallholders’ identity, agency and ownership rights. When considering deployment at scale, we identify risks that could leave growers and agrifood supply chains open to cascading accidents and cyberattacks. On the basis of this analysis, we outline a set of proposals to mitigate envisioned risks, building on frameworks of responsible research and innovation, data cooperatives, and hybrid cyber-physical spaces for low-risk deployment of experimental technologies. We highlight the main benefits of these approaches and techniques, and how they might be adapted to AI in agriculture.


AI risks for farms, farmers and food security

The study of AI risks is relatively new, and concerns associated with bias, inequality, privacy, safety or security play out differently in different domains. In global agriculture, a safety-critical system of high consequence for human development, we consider three types of risks: (1) risks relating to data, including acquisition, access, quality and trust; (2) risks emerging from narrow optimization of models and unequal adoption of technology during design and early deployment of ML systems; and (3) risks associated with deployment at scale of ML platforms.

41Centre for the Study of Existential Risk, University of Cambridge, Cambridge, UK. 2School of Sustainability, Reichman University (IDC Herzliya), Herzliya, Israel. 3Platform for Big Data in Agriculture, CGIAR, Cali, Colombia. 4International Institute for Tropical Agriculture, CGIAR, Ibadan, Nigeria. 5Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK. ✉e-mail:;

Risks relating to data acquisition, access, quality and trust.

Agricultural data ranges from the molecular to the landscape scale, and spans domains from agronomy and plant breeding to remote sensing and agricultural finance. National and international agricultural research institutions collect copious amounts of data, which could in principle support ML models. However, these data are too often not discoverable, interpretable or reusable.

CGIAR, a global consortium of agricultural research institutes, has in recent years espoused FAIR (findable, accessible, interoperable and reusable) data principles. Although there is progress in increasing findability through standardization, syntactic and semantic interoperability remains elusive due to lack of common data formats and structure protocols, as well as disordered or unused standards.

Reliability and relevance of agricultural data are additional concerns. A decades-long focus on staple crops such as wheat, rice and corn has outweighed research efforts concerning crops of crucial importance to the poorest producers and subsistence farmers, including quinoa, cassava and sorghum14.

Similarly, the people and practices at the centre of Indigenous farming systems are often under-represented in data, despite their contribution to local food security and dietary diversification - 15,16.

For instance, typical agricultural datasets have insufficiently considered polyculture techniques, such as forest farming and silvopasture. These techniques yield an array of food, fodder and fabric products while increasing soil fertility, controlling pests and maintaining agrobiodiversity17. Partial, biased or irrelevant data may result in poorly performing agricultural DSS, thereby eroding smallholders’ and Indigenous farmers’ trust in digital extension services and expert systems, eventually compromising food security.


Risks from narrow optimization and unequal adoption. While optimizing for yield, past agricultural technologies contributed to new pest complexes, loss of biodiversity and pollution3,4. These risks are broadly known, yet may be difficult to avoid if agriculture is further intensified through AI, and yield is prioritized over ecological integrity.

Expert systems and autonomous machines could improve the working conditions of farmers, relieving them of manual, routine tasks18. However, without deliberate and inclusive technology design, socioeconomic inequities that currently pervade global agriculture, including gender, class and ethnic discriminations19,20, and child labour21 will remain external to ML models applied in agriculture. This is no minor concern; over 98 million children work in farming, fishing, forestry and livestock, in an intensity that deprives them of their childhood and development opportunities22. Agronomic expert systems that remain agnostic to agricultural labour inputs, namely disadvantaged communities and children in employment, will ignore and thereby might sustain their exploitation.

Furthermore, small-scale farmers who cultivate 475 of approximately 570 million farms worldwide and feed large swaths of the so-called Global South23 are particularly likely to be excluded from AI-related benefits. Marginalization, poor Internet penetration rates24 and the digital divide25 might prevent smallholders from leveraging such advanced technologies, widening the gaps between commercial farmers and subsistence farmers. The dissemination of AI is also likely to raise concerns around the potential effects on farmers’ work, identity, agency and ownership rights, including of intellectual property26. In such circumstances, there are clearly risks that large and small farmers will profit unequally, and smallholders get locked into proprietary systems they do not fully understand27.


Risks from deploying AI and ML at scale. Adoption and use of earlier successive waves of technologies for agricultural 

intensification tended to be led by larger commercial farms with more capital to invest and ability to harvest marginal gains in productivity over larger areas28. This increases the likelihood that commercial farmers may be the first to harvest the benefits of AI-driven productivity, with the potential of widening the divide between large farmers and smallholders.

Concomitantly, as AI becomes indispensable for precision agriculture, we can expect an increasing reliance of commercial farmers on a small number of easily accessible ML platforms, such as TensorFlow and PyTorch. Under these conditions, farmers will bring substantial croplands, pastures and hayfields under the influence of a few common ML platforms, consequently creating centralized points of failure, where deliberate attacks could cause disproportionate harm.

In particular, these dynamics risk expanding the vulnerability of agrifood supply chains to cyberattacks, including ransomware and denial-of-service attacks, as well as interference with AI-driven machinery, such as self-driving tractors and combine harvesters, robot swarms for crop inspection, and autonomous sprayers29. The 2021 cyberattack on JBS30, the world’s largest meat processor, foreshadows potential risks that come from the introduction of digital technologies into agrifood supply chains. A 2021 ransomware attack on NEW Cooperative, which provides feed grains for 11 million farm animals in the United States31, further emphasizes this emerging cyber-crime landscape.

Rapid diffusion of intelligent machines in multi-component, multi-agent systems, such as agriculture, may exacerbate nondeliberate, accidental risks as well32. For instance, if monocultures— where a single genotype of a plant species is cultivated on extensive lands—are irrigated, fertilized and inspected by the same suites of algorithms, a model error or poorly calibrated sensors may lead to excessive fertilization and soil microbiome degradation, at the risk of large-scale crop yield failures. Furthermore, unanticipated, cascading system failures have been shown to arise when the interactions between intelligent agents, specifically in human–machine hybrid systems32, happen faster than humans are able to respond33. As digital tools begin to permeate all aspects of agriculture, and agrifood supply chains, the risk of such ‘flash crashes’ of the type seen in other domains may increase.

Governance mechanisms Data stewardship, ownership and cooperatives. We identify the need for FAIR data frameworks and improved standards for transparency, ownership rights and oversight, across all phases of the agricultural data value chain, including data generation, acquisition, storage and analysis.

Specifically, farmers sharing information on soil type, composition and nutrient availability, land surface phenology, choice of crops, amount of fertilizer used, crop rotations, historical crop yield records and actual yield should all follow open-science data-sharing requirements, specifying the repository and dataset.

Addressing ownership issues by democratizing data access and use via standards-compliant repositories is likely to be a foundational aspect of this approach, enabling more open, multi-stakeholder science and technology development34.

In this context, data-stewardship tools that facilitate agricultural data lakes are essential as global agriculture contends with a deluge of data from multiple sources of varying types. These tools must protect farmers’ proprietary rights, ensure data can be trusted, determine how data can be used and enable effective data mining.

The use of industry standards such as ontologies and controlled vocabularies in data lakes should support data mining across disciplines, heterogeneities and sources that differ in modality, granularity, structure and scale35. For example, CGIAR’s Platform for Big Data in Agriculture provides tools and workflows36 to generate FAIR data with support from several platform-mediated communities of practice and

including the development and use of ontologies to improve semantic interoperability. Data cooperatives, or platforms owned and controlled by their members, are a recent model and potential response to the need for more transparent and democratic governance of farm and farmers’ data. Several examples in the United States include the Ag Data Coalition (ADC) and the Grower Information Services Cooperative (GiSC).

Some data cooperatives such as ADC offer secure data repository solutions where farmers can store their data and decide which agencies or research entities to share it with. Others, such as GiSC, offer ‘data pools’ with shared data resources and analytics services to provide peers with improved insight into their farming practices.

Similar approaches are being tested in emerging economies. For example, Digital Green is developing FarmStack, a data-sharing platform and peer-to-peer data-sharing standard for farmers in India, with offerings like those of ADC. Yara and IBM are collaborating to enable farmers to securely share data and determine who uses the data and how37. A key challenge emerges from the tension between a democratized access to data and data monetization. On one hand, if ML systems profit from data contributed by farmers, farmers should be fairly compensated for generating these data. Furthermore, monetization should incentivize growers to share more and better-structured data.

On the other hand, various AI systems provide benefits without financial gains, and would be limited if the cost of access to data is prohibitive. One option to consider is a licensing structure that differentiates between commercial and non-commercial use of data. Another alternative is to share data only amongst groups who all stand to benefit from sharing, such as smallholders in polyculture systems. Data cooperatives could provide a governance structure for exploring different options and making decisions that align with farmers’ best interests.


Responsible innovation. The risks delineated above emphasize the need to develop agricultural AI systems and services with sensitivity to context, giving consideration to prospective social and ecological ramifications, and 

placing the data owner at, or close to, the centre of design efforts. Table 1 adapts a responsible research and innovation approach38 to agricultural AI and suggests interventions in the public and private sectors to ensure anticipatory, reflexive, inclusive and responsive development. For instance, anticipatory design of agricultural AI would involve considering and assessing safety concerns beyond data privacy. These might include unsustainable use of chemical inputs, or overexploitation of agroecosystems. Reflexive AI development should invite deliberative collaborations of rural anthropologists, applied ecologists, ethicists and data scientists in co-creating new ML models that safeguard biodiversity and are context sensitive, ensuring AI ethical principles are translated into practice39. Inclusive, participatory, human-centred design should value agricultural paradigms other than industrial farming, including Indigenous knowledge systems.

Civil-society frameworks and fora that give voice to vulnerable and marginalized communities, and circulate their concerns, can support these aims.


Staged, risk-aware deployment in digital sandboxes.

We suggest that initial deployment of AI for agricultural purposes take place in low-risk hybrid cyber-physical spaces, which we refer to as ‘digital sandboxes’, where multiple stakeholders can be engaged in rapid and supervised prototyping and piloting of novel ML techniques, and associated technologies. In such cyber-physical space, models and machines could be assessed under local and closely monitored circumstances. This model is not entirely new.

It has precedence, for instance, in biotechnology frameworks governing and enforcing biosafety protocols in genetic, genomic and genetically modified organism research40. Digital sandboxes that report on possible failures of nascent technologies would ensure that experimental practices such as autonomous pest and pathogen diagnosis and control systems are precise as well as safe and well-secured.

At the same time, anonymizing data relating to failed deployment attempts and sharing it with agricultural AI communities will allow lessons to be learned and accelerate safe and secure innovations.

The Hands Free Hectare project (https://www.handsfreehectare. com/) at Harper Adams University in the UK, where autonomous precision agriculture interventions are tested and validated, is one example of such a cyber-physical space in a European context; the AI Lab at Makerere University ( in Kampala, Uganda, where ML anticipates the spread of plant diseases, demonstrates how the approach works in an African context. This approach has several ancillary benefits. For instance, digital sandboxes that operate in open-science partnerships that link public, private and non-profit institutions can create the context for prototyping AI applications safely. They can also help inform rules and regulations for rolling applications out responsibly. Whereas regulatory rigidity may prevent prototyping of novel ML techniques, government agencies could give special, interim exemption to experimentation and learning spaces such as digital sandboxes before developing targeted, customized regulatory frameworks. Moreover, multi-stakeholder approaches to experimentation and learning, such as digital sandboxes, can create opportunities to apply responsible innovation principles in technology design.



Widespread deployment of AI in agriculture is both valuable and expected. Nonetheless, the history of technological modernization in agriculture strongly suggests that a focus on increased productivity carries potential risks, including intensifying inequality and ecological degradation. Agricultural AI must avoid the pitfalls of previous technologies, and carefully navigate and ameliorate their predicaments by implementing comprehensive risk assessments and anticipatory governance protocols. From data collection and curation to development and deployment, general principles of responsible and participatory AI should be tailored to the distinct challenges facing agriculture, at local and global scales. Failure to do so may ignore and thereby perpetuate drivers of nutritional insecurity, exploitation of labour and environmental resources depletion. Previous mis-steps notwithstanding, technological modernization in farming has achieved much. Past successes, too, should inform and inspire the use of agricultural expert systems and intelligent machines. Accordingly, it is essential that a balanced approach towards innovation is practiced, and that risk assessments and responsible research and development procedures do not stifle innovation in a system so fundamental to human wellbeing. Finally, the emerging risk landscape discussed here is also applicable to agricultural systems that provide non-food products; a similar approach should therefore be considered in the production of fibres, fuels, pulp, paper, oils, resins, cosmetics, rubber and plastics.  (Received: 20 August 2021; Accepted: 2 January 2022; Published online: 23 February 2022)



1. The State of Food Security and Nutrition in the World 2020: Transforming Food Systems for Affordable Healthy Diets (FAO, IFAD, UNICEF, WFP, WHO, 2020). 2. Cole, M. B., Augustin, M. A., Robertson, M. J. & Manners, J. M. The science of food security. NPJ Sci. Food 2, 14 (2018). 3. Matson, P. A., Parton, W. J., Power, A. G. & Swift, M. J. Agricultural intensification and ecosystem properties. Science 277, 504–509 (1997). 4. Quinton, J. N., Govers, G., Van Oost, K. & Bardgett, R. D. The impact of agricultural soil erosion on biogeochemical cycling. Nat. Geosci. 3, 311–314 (2010).


5. Singh, R. B. Environmental consequences of agricultural development: a case study from the Green Revolution state of Haryana, India. Agricult. Ecosyst. Environment 82, 97–103 (2000). 6. The State of the World’s Plant Genetic Resources for Food and Agriculture (FAO, 2010). 7. Semchuk, K. M., Love, E. J. & Lee, R. G. Parkinson’s disease and exposure to agricultural work and pesticide chemicals. Neurology 42, 1328–1328 (1992). 8. Campbell, J. In Topical Research Digest: Human Rights and Contemporary Slavery 131–141 (Univ. Denver, 2008). 9. Nguyen, H. Q. & Warr, P. Land consolidation as technical change: economic impacts in rural Vietnam. World Dev. 127, 104750 (2020). 10. Nilsson, P. The role of land use consolidation in improving crop yields among farm households in Rwanda. J. Dev. Stud. 55, 1726–1740 (2019). 11. Du, X., Zhang, X. & Jin, X. Assessing the effectiveness of land consolidation for improving agricultural productivity in China. Land Use Policy 70, 360–367 (2018). 12. Schmitz, A., & Moss, C. B. Mechanized agriculture: Machine adoption, farm size, and labor displacement. AgBioForum 18, 278–296 (2015). 13. Wilde, P. Food Policy in the United States: An Introduction (Routledge, 2013). 14. Tadele, Z. Orphan crops: their importance and the urgency of improvement. Planta 250, 677–694 (2019). 15. Lugo-Morin, D. Indigenous communities and their food systems: a contribution to the current debate. J. Ethn. Food 7, 6 (2020). 16. Akinola, R., Pereira, L. M., Mabhaudhi, T., de Bruin, F. M. & Rusch, L. A review of indigenous food crops in Africa and the implications for more sustainable and healthy food systems. Sustainability 12, 3493 (2020). 17. Jose, S. Agroforestry for ecosystem services and environmental benefits: an overview. Agrofor. Syst. 76, 1–10 (2009). 18. 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. Artif. Intell. Agricult. 4, 58–73 (2020). 19. Palacios-Lopez, A., Christiaensen, L., & Kilic, T. How much of the labor in African agriculture is provided by women? Food Policy 67, 52–63 (2017). 20. Alkon, A. H., & Agyeman, J. (eds) Cultivating Food Justice: Race, Class, and Sustainability (MIT Press, 2011). 21. Edmonds, E. V. & Pavcnik, N. The effect of trade liberalization on child labor. J. Int. Econ. 65, 401–419 (2005). 22. Child Labour in Agriculture (International Labor Organization, 2021). 23. Lowder, S. K., Skoet, J. & Raney, T. The number, size, and distribution of farms, smallholder farms, and family farms worldwide. World Dev. 87, 16–29 (2016). 24. Mehrabi, Z. et al. The global divide in data-driven farming. Nat. Sustain. 4, 154–160 (2021). 25. Hennessy, T., Läpple, D. & Moran, B. The digital divide in farming: A problem of access or engagement? Appl. Econ. Persp. Policy 38, 474–491 (2016). 26. Klerkx, L., Jakku, E., Labarthe, P. A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS Wageningen J. Life Sci. 90–91, 100315 (2019). 27. Wolfert, S., Ge, L., Verdouw, C. & Bogaardt, M. J. Big data in smart farming–a review. Agric. Syst. 153, 69–80 (2017). 28. Levins, R. & Cochrane, W. The treadmill revisited. Land Econ. 72, 550–553 (1996). 29. Sontowski, S. et al. Cyber attacks on smart farming infrastructure. In 2020 IEEE 6th Int. Conf. on Collaboration and Internet Computing (CIC) 135–143 (IEEE, 2020). 30. Cyber-attack hits JBS meat works in Australia, North America. Reuters (1 June 2021) 31. Sharma, A. $5.9 million ransomware attack on farming co-op may cause food shortage. Ars Technica 2021/09/5-9-million-ransomware-attack-on-farming-co-op-may-causefood-shortage/ (21 September 2021) 32. Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019). 33. Johnson, N. et al. Abrupt rise of new machine ecology beyond human response time. Sci. Rep. 3, 2627 (2013). 34. Gold, E. R. The fall of the innovation empire and its possible rise through open science. Res. Policy 50, 104226 (2021). 35. Majumdar, J., Naraseeyappa, S. & Ankalaki, S. Analysis of agriculture data using data mining techniques: application of big data. J. Big Data 4, 20 (2017). 36. CGIAR GARDIAN Data Ecosystem (CGIAR Platform for Big Data in Agriculture, 2021). 37. Yara and IBM. IBM (accessed 18 August 2021). 38. Stilgoe, J., Owen, R. & Macnaghten, P. in The Ethics of Nanotechnology, Geoengineering and Clean Energy 347–359 (Routledge, 2020). 39. Theodorou, A. & Dignum, V. Towards ethical and socio-legal governance in AI. Nat. Mach. Intell. 2, 10–12 (2020).

Nature Machine Intelligence | VOL 4 | February 2022 | 104–109 |

Global AI in Agriculture Industry Outlook, 2026 - Revenue and CAGR% Breakdown by Product, Application, and Country

January 14, 2022 05:53 ET | Source: Research and Markets

Dublin, Jan. 14, 2022 (GLOBE NEWSWIRE)  --  The "Artificial Intelligence in Agriculture Market - A Global and Regional Analysis: Focus on Product, Application, and Country Analysis and Forecast, 2020-2026" report has been added to's offering.

The global artificial intelligence in agriculture market is expected to reach $6,655.1 million by 2026, with a CAGR of 30.56% during the forecast period 2021-2026. The growth rate in the market is because of the increased awareness of artificial intelligence-based solutions in some regions of the world.

Impact of COVID-19

The supply chain for the majority of the industries across the globe got impacted due to the COVID-19 pandemic, including the artificial intelligence in agriculture industry. During the COVID-19 outbreak, the supply chain of the agriculture industry was disrupted. During the COVID-19 pandemic, agricultural production was hindered for farmers, which led to a decrease in their revenue generation. This has left the farmers in no position to invest in modern agricultural equipment.

By Farming Type

The farming type segment in the application for artificial intelligence in agriculture market is expected to be dominated by field farming. The majority of the farmers around the world still engage in traditional farming, which is expected to drive artificial intelligence in agriculture market in the forecast period.

By End Use

The end use segment in the application of artificial intelligence in agriculture market is expected to be dominated by crops, fruits, vegetables, and other plants. The primary focus of all farmers around the world is to increase agricultural production, which is expected to increase the adoption of artificial intelligence products in the agricultural industry.

By Product

The global artificial intelligence in agriculture market in the product segment is expected to be dominated by software products. The high market share and growth potential associated with software products in the agriculture industry is expected to drive the global artificial intelligence in agriculture market.

By Region

North America generated the highest revenue of $598.7 million in 2020, which is attributed to the technological advancements in the North America region. In the region, government support along with technological advancement has helped in the growth of the market. The region is expected to witness high growth of CAGR 29.88% during the forecast period 2021-2026.

Recent Developments

 - In May 2021, Robert Bosch unveiled its new artificial intelligence based IoT platform to keep real time track of energy consumption, electrical parameters in healthcare, agriculture, and other sectors.

 - In November 2020, Deere & Company introduced AutoPath for more accurate row guidance throughout the season.

 - In November 202, CLAAS KGaA mbH launched the cloud-based DataConnect system that enables the exchange of machinery data between CLAAS, 365FarmNet, and Deere & Company.

 - In September 2020, Agrivi launched Agrivi Farm Fleet that enables farmers to have real-time access to their fuel usage and machinery.

Key Questions Answered in the Report

 - What is the estimated global artificial intelligence in agriculture market size in terms of revenue for the forecast period 2021-2026, and what is the expected compound annual growth rate (CAGR) during the forecast period 2021-2026?

 - What are the key trends, market drivers, and opportunities in the market pertaining to artificial intelligence in agriculture?

 - What are the major restraints inhibiting the growth of global artificial intelligence in agriculture market?

 - What kinds of new strategies are being adopted by the existing market players to strengthen their market position in the industry?

 - What is the competitive strength of the key players in the artificial intelligence in agriculture market based on an analysis of their recent developments, product offerings, and regional presence?

 - How is the competitive benchmarking of the key artificial intelligence in agriculture companies in the agriculture market based on the analysis of their market coverage and market potential?

 - How much revenue is each segment expected to record during the forecast period, along with the growth percentage?

 - What is the type of players and stakeholders operating in the market ecosystem of artificial intelligence in agriculture, and what is their significance in the global market? , October 11 , 2021

AI in Agriculture: Sustainable AI Farming 2021

September 27, 2021 Artificial intelligence (AI) in agriculture has transformed the way the world’s farming operations work by giving food producers significantly improved access to data about their operations. 


AI provides farmers with real-time insights about crop conditions, livestock activity, and the locations of their farm machinery. Looking ahead, many scientists believe AI in agriculture will play a pivotal role in increasing food production globally, particularly in areas where food insecurity is the norm. 


By 2050, the world’s population will grow by 2 billion people, according to United Nations (UN) data on population and hunger. The world will require a 60% increase in food production to keep the global population fed. Advances in AI and machine learning (ML) in agriculture are powering innovations that have the potential to improve food production supply chains in more affordable, sustainable ways. 


Spending on AI technology will grow from $1 billion in 2020 to $4 billion in 2026, a compound annual growth rate (CAGR) of 25.5%, according to Markets & Markets.  

AI applications in agriculture tend to focus on one or more of four primary goals, according to the ITRex Group.


 - Yield improvement

 - Cost reduction

 - Profit increase

 - Alignment with sustainable farming practices



5 examples of AI in agriculture

In his Forbes article, “10 Ways AI has the Potential to Improve Agriculture in 2021,” Louis Columbus addresses a range of successful AI applications in the industry:


1. Drone data is helping producers optimize the use of pesticides

Intelligent sensors, combined with visual data streams from drones, use AI to detect areas most infected with pests. This data helps farmers optimize the right mix of pesticides and allows them to zero in on only the field areas that need treatment. The result, Columbus says, is a reduction in overall costs and an increase in yields, two key drivers fueling AI in agriculture adoption. 



2. Linear AI programming is enabling farmers to conserve more water

AI can help farmers locate irrigation leaks, optimize irrigation systems, and measure the effectiveness of crop irrigation approaches. Conserving water is becoming increasingly vital as the world’s population grows and drought conditions become more widespread and impactful. Using water efficiently can significantly impact a farm’s profit and contribute to the global effort to conserve water. Columbus says linear AI programming is being used to calculate the optimal amount of water a specific field or crop needs to reach the desired yield level. 



3. IoT sensors are providing real-time insights into previously untraceable data sets

Farmers today have access to IoT sensors that can keep track of virtually every aspect of food production — a huge technological leap over agriculture methods from even a few years ago. It’s now possible for farmers to track data about soil moisture and nutrient levels to analyze crop growth patterns over time. Columbus points to a specific branch of AI — machine learning — as the key to using IoT sensor data to arrive at data-driven predictions about potential crop yields. 



4. AI-powered yield mapping is improving crop-planning accuracy

Yield mapping is an agricultural technology that uses supervised machine learning algorithms to uncover patterns hidden within large-scale data sets that can be used for crop planning. Columbus notes this technique involves the collection of drone flight data, combined with IoT sensor data, to make predictions about potential crop yields before the vegetation cycle has begun.



5. AI-enhanced livestock monitoring is improving animal health and increasing profits

Being able to monitor livestock at a high level gives producers an edge over competitors who have yet to invest in AI-enhanced agriculture technology. Farmers, Columbus says, can monitor food intake, activity levels, and vital signs to develop a better understanding of the optimal conditions for better milk or meat production. Real-time health insights also allow farmers to quickly separate livestock infected with contagions from healthy animals as well as promptly address injuries and unexpected livestock behaviors.  




Company News:

AIFarm Ltd becomes a member of the

International Society of Precision Agriculture

September 27, 2021 AIFarm Ltd (Nasdaq OTC and OTC Markets Groups: AIFM, WKN:A2PYQH).

A company that develops new innovative applications for agriculture (so-called smart farming) announced today that it has been accepted as a member of the International Society of Precision Agriculture (ISPA). ISPA is a non-profit organization that supports its members and new to develop technical and scientific applications in agriculture. ISPA is a worldwide network and has members in many different countries. It organizes regular conferences for its members in the respective countries. Members can exchange information about new developments and technologies with each other or present themselves at the conferences.


AIFarm Ltd has great confidence in the concept and strength of the ISPA network. ISPA is the leading Internet platform for companies in the field of smart farming. As a member of ISPA, AIFarm Ltd becomes part of a global network and has the opportunity to network with other companies in the industry to exchange information on the latest smart farming technologies.


More information about the ISPA can be found here:


AIFarm Ltd. is an agricultural technology company that develops applications and services for agriculture to ensure that crop yields are increased through the use of new technologies such as drones or robots and web-based evaluation, Cultivated areas can be used more efficiently and the use of fertilizers is reduced. This not only serves to save costs, but also leads to a reduction in pesticides, the avoidance of water pollution and lower CO 2 emissions. The technology is aimed at agricultural businesses, greenhouse operators or aqua farms. The market for this is worldwide. AIFarm Ltd has a particular focus on Asia; meanwhile AIFarm Ltd. is established in the US and Canada too.