Harvesting the Future: The Rise of Artificial Intelligence in Agriculture
The Game-Changing Effects of AI on Agriculture
Expectations are high for the artificial intelligence (AI) industry, as it's predicted to reach a value of $909 billion by 2030. AI, a software-based system that uses data inputs to make autonomous decisions, is reaching beyond research labs, thanks to improvements in machine learning. Growth in the sector, amplified by algorithms like OpenAI’s GPT-3 and greater computing power, will see it rise from $81 billion in 2022 to $909 billion by 2030, marking a 35% compound annual growth rate.
AI emerges as a critical player in the fourth agricultural revolution
While the full potential of artificial general intelligence (AGI) may still be years away, currently available AI possesses human-like capabilities, including interaction, motion, and decision-making. This AI's features are beneficial for precision farming, promoting efficient resource use by adapting to crop variability. AI's ability to analyze vast data from sensors and satellites aids farmers in managing crops or livestock more effectively1. In a sector challenged by variability, climate change, and geopolitical issues; AI plays a key role in tasks such as optimizing farming plans, evaluating climate risks, and managing diseases.
Generative AI: A compelling tool for the agriculture sector
The farming industry needs to leverage the opportunities presented by generative AI. As this form of AI becomes more precise and reliable, its implications will permeate various sectors and business operations. In agriculture, potential applications such as customer service chatbots and automated marketing content creation can greatly enhance business procedures. However, the utility of generative AI extends beyond just these functions. Innovative applications could integrate it with genetic mapping and AI's decision-making attributes, mirroring the process of drug discovery to generate new seed variants2. These novel seeds could provide critical solutions for farmers combating disease, extreme climatic conditions, and scarcity of resources and land.
The table below shows the specialist AI companies and the leading adopters of AI in the agricultural sector across advanced AI capabilities value chain
Challenges in Agriculture
Modern agriculture is facing numerous interconnected challenges while catering to an ever-growing global demand for food, fuel, and fiber. With the UN predicting the global population to reach 9.7 billion by 2050 and urbanization set to include 70% of this population, the strain on the sector is increasing. There is a pressing requirement for significant investments towards enhancing farming productivity to meet changing consumer needs. Simultaneously, the agricultural sector needs to adapt to the climate change effects, such as frequent extreme weather events and farming practices' environmental impacts. Technological interventions will be crucial in resolving these issues and enhancing agricultural productivity.
The agricultural land area has declined since 2010, but the undernourished population has grown In 2020, 9.3% of the world’s population was undernourished, the highest percentage since the start of the 2010s.
The Sequel of the Green Revolution
The 1960s Green Revolution, spurred by population growth, enhanced agriculture via high-yield crops and synthetic fertilizers, albeit at an environmental cost. The burgeoning demand for fertilizer strains makes ammonia supply crucial for their production. Amid escalating environmental effects and fertilizer demand, the fourth agricultural revolution emerges, propelled by AI and robotics aimed at elevating agricultural efficiency. These technologies, capable of autonomous harvesting and fertilizing, need a notable digital shift in agriculture and reliable rural connectivity.
According to a 2020 McKinsey report, merely 25% of U.S. farms employ connected equipment, primarily on outdated networks. Despite declining connectivity hardware costs, coverage remains a concern. Projections indicate 80% of rural areas globally will have connectivity by 2030, except in Africa where only a quarter might achieve coverage.
The biggest obstacles facing the AG sector
The farming sector is heavily impacted by climate change, contributing to it via greenhouse gas emissions exacerbated by human activities. Climate change has led to extreme weather and natural disruptions, impacting soil, water, and worsening droughts, notably in sub-Saharan Africa from 2010 to 2019, affecting 95.7 million more people. The 2022 IPCC Report noted potential massive crop losses in southern Europe with a 2°C temperature rise. The UN states agriculture causes 8.5% of greenhouse gas emissions, with 14.5% more from land use changes for food production. This leads to a reliance on fossil-fuel-based agrochemicals and deforestation for farmland, causing further environmental issues and forming a vicious cycle of agricultural and environmental challenges.
Land and labor availability
Around 38% of the world's land is used for agriculture, split between cropland and livestock grazing, as per the FAO. With the global population nearing 8.5 billion by 2030, the demand for food and strain on land will rise. Urban expansion will encroach on agricultural land, lessening fertile soil availability and agricultural capacity. This land competition is driving up land prices, with some farmers selling for non-agricultural uses. Climate change-induced urbanization is drawing people to cities, causing a labor gap in agriculture. The expansion of farmland, often at forests' expense, exacerbates climate change, as forests are crucial for carbon sequestration and biodiversity. The 2021 UN COP26 saw 141 countries vow to halt deforestation by 2030. To address these issues, agriculture must enhance efficiency to produce more food on less land with fewer workers. Technologies like genetic engineering and new farming systems like vertical farming are emerging to meet this challenge.
Food security is a crucial geopolitical concern for nations. In 2020, 10% of the world faced hunger, per FAO, despite enough food globally, as noted by UNEP, due to distribution inefficiencies, with a third wasted. COVID-19 exacerbated hunger, impacting 161 million more people. Geopolitical unrest, like the Russia-Ukraine conflict, has sparked resource nationalism, driving up global food prices and causing a food and fertilizer crisis, affecting 400 million people reliant on Ukrainian food supplies. Food can be a geopolitical leverage, notably impacting food-importing developing nations. Despite having 65% of the world's remaining uncultivated arable land, Sub-Saharan Africa's weak policies may lead to $110 billion in food imports by 2025, as projected by the African Food Development Bank Group.
Plant and livestock diseases significantly impact food and biofuel production and quality, with poor handling practices exacerbating post-harvest losses. FAO notes 40% of global crops, costing $220 billion, are lost to pests annually. Climate change accelerates pest spread, like the fall armyworm affecting crops, causing $9.4 billion annual losses in Africa in 2021. Global trade, which has tripled since 2010, spreads half of emerging plant diseases, necessitating the WTO's monitoring of agricultural trade for international disease control. Past outbreaks like the Karnal bunt fungus led to major trade bans and revenue losses. Livestock diseases, like African swine fever in China, caused massive culling, with a 21% pork production drop between 2018 and 2019. Improved disease monitoring and control via emerging agricultural technologies are vital.
Pressure on limited resources
The FAO states agriculture consumes 70% of human freshwater, mainly for irrigation, which is projected to rise by 11% by 2050 due to population growth. This irrigation reliance, especially in productive regions, strains water systems and alters moisture and rainfall patterns, affecting crops. Commercial fishing has tripled overfished stocks since the 1970s, per FAO, though sustainable practices are aiding recovery. Modern agriculture's dependence on fossil fuels for operations and agrochemical production ties food prices to energy costs, as seen in the UK post-Ukraine conflict. This emphasizes the need to reduce fossil fuel reliance in agriculture.
Agriculture significantly affects the environment by degrading soil and reducing biodiversity.
Deforestation destabilizes soil, increasing landslide risks.
Livestock overgrazing and heavy machinery use cause soil erosion and compaction, affecting 61%–73% of European agricultural soils, per the IEEP.
Monocropping depletes soil nutrients, necessitating more fertilizers, while tillage disrupts soil structure. The EU proposed a Soil Health Law in 2023 to improve soil health by 2050.
Pesticides and fertilizers cause pollution, leading to biodiversity loss and eutrophication in water systems. Bioaccumulation from these chemicals poisons wildlife and humans, with 25 million people affected by pesticide poisoning annually, as stated by UNEP.
Land clearing for agriculture fragments habitats, reduces biodiversity, and threatens ecosystem services like pollination, which is crucial for 85% of European crops. Without pollinators, crop production could decline by 25%–32%, warns the IEEP.
Lack of market transparency
Information asymmetries in agriculture arise when parties, like those trading commodities, have unequal access to crucial market information. This issue, more pronounced in agriculture due to its oligopolistic nature with few retailers dominating, disadvantages smaller stakeholders like farmers, impacting their operational decisions. Large trading firms and retailers, having better market insight, exploit this to dictate commodity demand, control prices, and decide what and how farmers should produce. A study highlighted by The Guardian and Food Water Watch revealed that four firms controlled a significant market share of 79% of 61 top grocery items, while farmers received merely 15 cents for every dollar spent by consumers at supermarkets, indicating a systemic inequity disadvantaging farmers.
The Game-Changing Role of AI in Agriculture
The table below serves as a guide on where agricultural companies should allocate resources within the AI value chain. It recommends investing in areas marked green and exploring opportunities in areas marked yellow to optimize value and explore potential benefits in the agricultural sector.
Agriculture companies should particularly focus investment on AI’s decision-making and motion capabilities
There are five main categories of advanced AI: human-AI interaction, decision-making, motion, creation, and sentience. This summary discusses how these AI capabilities can be utilized in the agricultural sector.
Human-AI Interaction: This facilitates improved efficiency in agriculture. AI's computer vision can be used for tasks like identifying weeds or observing crop distribution. Conversational platforms, like chatbots, assist in various stages of the agriculture value chain by answering questions and speeding up operations.
Decision-making: AI’s decision-making abilities leverage data from various sources, such as on-site sensors and drones, to enhance the efficiency of decisions made in agriculture. These capabilities can be applied to farm planning, crop and disease management, and other aspects, especially at the production stage, improving crop quality and yield.
Motion: AI's motion capabilities are transforming agriculture, with a significant impact coming from autonomous vehicles. John Deere's prototype self-driving tractor is an example of this transformative change.
Creation (Generative AI): Generative AI, which has gained attention since the release of OpenAI’s ChatGPT, has the potential to disrupt all businesses, including agriculture. It's already being used in tasks like designing new seed varieties at the pre-production stage.
It is important to note that the sentience category of AI is currently considered far from being achieved. The detailed insights on these AI capabilities can be found in the AI Value Chain section of the report.
AI can solve Agriculture’s main challenges
1. AI in Addressing Climate Change and Environmental Degradation:
AI facilitates precision agriculture which is a method focused on minimizing waste and pollution by prescriptive application of water, pesticides, and fertilizers. This method combats overuse of agrochemicals that harm the environment. By analyzing vast amounts of data related to soil, weather, and crops, AI helps in informed decision-making that enhances productivity, reduces costs, and cuts down on agrochemical waste. Companies like Syngenta and John Deere utilize AI in precision agriculture to selectively spray agrochemicals, providing farmers with real-time insights. Such insights can further refine farm planning to reduce environmental impacts.
2. AI in Addressing Geopolitics and Market Transparency:
The traditional agricultural supply chain often places farmers at a disadvantage due to information asymmetry, where traders and retailers might exploit them. With the aid of AI-enhanced agricultural management information systems, farmers now have direct access to vital information on market demand trends, supply, and even weather patterns. This could potentially bypass some intermediaries and allow farmers to directly sell to consumers. AI's decision-making and generative abilities can provide recommendations, crucial alerts on weather extremes, and on-demand advice. By integrating AI into applications, even smallholder farmers can access this knowledge, promoting fairer distribution in the farming industry.
3. AI in Addressing Land and Resource Availability:
Agriculture faces challenges with limited land, labor, and resource availability. AI boosts decision-making by analyzing farm sensor data, satellite imagery, and weather forecasts to maximize crop yields and minimize waste. With land issues, AI promotes urban agriculture, including vertical farming and hydroponics in controlled settings. For water scarcity, AI can devise optimal irrigation strategies. Moreover, with a declining workforce in agriculture, AI-enabled machinery reduces the labor dependency, ensuring sustained or even increased output.
4. AI in Tackling Agricultural Diseases:
Pests and diseases result in substantial crop losses. Traditional methods for disease detection are costly and slow. AI-based crop diagnostic apps rapidly identify diseases, pests, or deficiencies. Combined with genetic mapping, AI expedites the creation of disease-resistant seed variants, transforming disease management and saving time and labor.
5. AI in Reducing Spoilage and Waste:
Unpredictable weather events caused by climate change pose significant challenges. India, for instance, saw a 15% harvest reduction in 2022 due to drought. AI aids in crop management against such adversities by providing insightful decisions based on vast data. Tools like Gooey.AI and Farmer.CHAT give farmers timely advice on weather-related crop management, pest control, and more, ultimately reducing crop losses.
AGCO, a major agricultural machinery manufacturer from the US, collaborated with Persistent, a global tech firm, to introduce a conversational AI platform aimed at enhancing customer service for AGCO's vast dealer network spanning 140 countries.
The AI-driven conversational platform, developed using tools from Kore.ai, aids in swiftly addressing routine queries from AGCO’s machinery dealers.
With the chatbot handling the bulk of common questions, AGCO's customer support team can focus on more intricate and high-value concerns.
The chatbot boasts multilingual capabilities, conversing in English, German, and French.
Training and Flexibility:
Persistent ensured that AGCO’s team was trained in using Kore.ai and its associated low-code software. This training allows the platform to be regularly updated with new query scenarios.
The AI conversational platform operates around the clock, substantially enhancing AGCO's customer service.
Remarkably, the chatbot efficiently handles 80% of all dealer inquiries.
The implementation has also led to operational efficiencies, as evidenced by a 20% reduction in ticket volume for AGCO's customer support team.
While plant breeding has been pivotal to crop enhancement for thousands of years, Bayer has taken a modern approach by integrating AI into the process.
Bayer utilizes a combination of genetic mapping, machine learning, and generative AI to simulate traditional plant breeding techniques.
Instead of merely relying on visible characteristics (phenotypes) of plants, Bayer conducts comprehensive genomic sequencing.
Using AI, the vast genetic data and myriad genetic combinations are analyzed. This empowers the identification of optimal breeding strategies.
Generative AI's Role:
Generative AI's capabilities, which are also used by pharmaceutical firms to forecast drug target structures and generate potential molecules, enable Bayer to recommend new plant varieties.
Results & Benefits:
The AI-aided approach allows Bayer to rapidly introduce thousands of seed variants tailored to specific needs.
These seeds can be engineered to handle production stage challenges such as adverse weather conditions, climate change ramifications, diseases, and more.
Catering to market preferences, plants can also be customized for size, taste, or color, addressing consumer needs and potentially reducing waste.
Cargill has introduced the Galleon Broiler Microbiome Intelligence Service, which employs AI and an expansive microbiome database to enhance the health of poultry flocks.
Importance of Gut Health:
The well-being of the gut microbiome, consisting of bacteria, viruses, and other microorganisms, is crucial for broilers and farm animals.
An imbalance or infection in the microbiome leads to health issues, compromising poultry production.
How It Works:
Traditionally, poultry health decisions were based on anecdotal insights.
Producers provide Cargill with cloacal swab samples from their flocks.
These samples are analyzed in conjunction with Cargill's extensive microbiome database, which was amassed over ten years, incorporating global data and results from 100 research trials.
The analysis identifies critical biomarkers that give insights into the flock's gut health.
After data analysis, the system, using statistical analysis, machine learning, and AI-driven decision-making, produces a report offering recommendations to rectify any identified health concerns.
Producers receive clear guidelines on managing health problems. A Cargill specialist breaks down the findings, ensuring producers fully grasp the recommendations. This approach assists in handling disease management and ensuring food safety.
In December 2022, CNH Industrial procured a 10% ownership in Stout Industrial Technology, a U.S. start-up specializing in AI-integrated agricultural equipment.
Stout's Flagship product:
The Smart Cultivator by Stout epitomizes the integration of AI in farming equipment.
CNH Industrial aims to enlarge its mechanical weeding portfolio by distributing this equipment.
Features and Functionality:
The Smart Cultivator is a part of the See&Act machinery suite, performing operations based on sensor-acquired data.
Utilizing its unique computer vision technology, it employs image recognition, positioning, and navigation to differentiate crops from undesired weeds.
Upon identification, it nurtures crops while eradicating weeds.
Impact of AI Integration:
The incorporation of AI tremendously boosts agricultural processes, decreasing labor requirements and augmenting efficiency.
Each Smart Cultivator can process up to two acres within an hour continuously.
Its AI algorithm can identify individual weeds and plants with a remarkable accuracy of 99.99%.
In 2022, Syngenta Asia Pacific formed an alliance with PEAT to leverage their mobile crop advisory app, Plantix, aiming to benefit 500,000 smallholder farmers in Asia.
Specialization of PEAT:
PEAT specializes in image recognition using computer vision technology.
Utilization of Syngenta's Resources:
Plantix integrates Syngenta's vast database from the Cropwise Grower app, which contains information on over 50 crops and 500 diseases.
This partnership is set to impact five countries, covering a substantial 750,000 hectares of land that produce essential cash crops such as cotton, rice, corn, and wheat.
Functionality and Features of Plantix App:
The app offers instant identification of plant ailments and suggests solutions.
A farmer simply captures a photograph of the affected crop. Within a brief span (under five seconds), the app can pinpoint the disease or pest, boasting a 93% accuracy rate.
Images are geotagged, enabling early alerts concerning nearby pests or diseases.
Specifically designed for rural farmers, the app operates offline in areas with patchy internet, offering common diagnoses and remedies.
Its database is region-centric, containing details on local crops and diseases. All this data is made accessible in local dialects, including an impressive nine languages just in India.
Patent Trends in AI for Agriculture
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