Artificial Intelligence in Agribusiness

In the realm of cutting-edge technology, one term stands out, capturing the imagination and shaping the future of industries across the globe: Artificial Intelligence (AI). As society continues to advance rapidly, AI has emerged as a transformative force, revolutionizing the way we live, work, and interact. From self-driving cars to virtual assistants, AI has seamlessly integrated into our daily lives, enhancing efficiency, enabling innovation, and opening up a world of new possibilities. Artificial Intelligence, in its simplest form, refers to the development of intelligent machines capable of performing tasks that typically require human intelligence. Through the power of AI, machines are not just mimicking human actions, but are also learning, adapting, and making autonomous decisions based on vast amounts of data. This intersection of intelligent machines and data analytics has laid the foundation for groundbreaking advancements in various sectors, ranging from healthcare and finance to manufacturing and transportation.

One of the key drivers behind the rise of AI is its ability to process and analyze massive amounts of data at an unprecedented speed. With the exponential growth of digital information, traditional data processing methods have become insufficient, leading to the need for intelligent systems that can extract meaningful insights from the data deluge. AI algorithms, powered by machine learning and deep learning techniques, are designed to tackle this challenge, enabling us to harness the immense potential hidden within the vast oceans of data. The applications of AI are far-reaching and continue to expand with each passing day. In healthcare, AI is transforming diagnostics, drug discovery, and personalized medicine, leading to more accurate diagnoses and improved patient outcomes. In finance, AI-powered algorithms are revolutionizing trading, risk assessment, fraud detection, and customer service, streamlining operations and enhancing financial security. In manufacturing, AI-driven automation is optimizing production processes, improving quality control, and enabling predictive maintenance, resulting in increased productivity and reduced costs.

Table of Content

• Artificial Intelligence and Future of Agribusiness
• Artificially Intelligent Agribusiness and Sustainable Development Goals (SDGs)
• Why Adopting AI is such a Challenge for Farmers
• What is AI?
• How AI can be useful in Agriculture
• The Role of AI in the Agriculture Information Management Cycle
• Applications of AI Technologies
• Challenges in Scaling AI
• Problems Farms can face while Adopting AI
• How AI should be combined with other Technologies
• Reality vs Expectations of Artificial Intelligence for Sustainable Farming
• Conclusion

Moreover, AI is reshaping the way we interact with technology. Natural Language Processing (NLP) and computer vision have paved the way for virtual assistants, chatbots, and voice-controlled devices, enabling seamless human-machine communication. From voice commands to facial recognition, AI has transformed our smartphones, smart homes, and workplaces, making them more intuitive and responsive to our needs. However, the rise of AI also poses unique challenges and considerations. Ethical concerns regarding data privacy, algorithmic biases, and job displacement need to be addressed. It is crucial to strike a balance between the potential benefits of AI and the ethical implications associated with its deployment. Collaboration among governments, businesses, and researchers is essential to develop robust frameworks and guidelines that ensure responsible and transparent use of AI technologies.

In this blog post, we will delve deeper into the fascinating world of AI, exploring its various applications, breakthroughs, and implications. We will examine how AI is transforming industries, improving decision-making processes, and redefining human-machine interactions. Join us on this journey to discover the boundless potential of AI and its role in shaping the future of the Agribusiness Industry.

Artificial Intelligence and Future of Agribusiness

Artificial Intelligence can change the future of agriculture, but we need to help farmers implement it the right way. Can you imagine an industry that involves more risk than agriculture? You reap what you sow, they say. But what they forget to add is “if you’re lucky.” When the weather strikes or crops get affected by disease, farmers can hardly talk about yields. Or when a global pandemic hits, all of a sudden it gets harder to manage various processes because most are not digital. At the same time, the global population is growing, and urbanization is continuing. Disposable income is rising, and consumption habits are changing. Farmers are under a lot of pressure to meet the increasing demand, and they need a way to increase productivity. Thirty years from now, there will be more people to feed. And since the amount of fertile soil is limited, there will also be a need to move beyond traditional farming. We need to look for ways to help farmers minimize their risks, or at least make them more manageable. Implementing artificial intelligence in agriculture on a global scale is one of the most promising opportunities.

AI can potentially change the way we see agriculture, enabling farmers to achieve more results with less effort while bringing many other benefits. However, AI is not a technology that works independently. As the next step on the way from traditional to innovative farming, AI can supplement already implemented technologies. Agribusinesses need to know that AI isn’t a panacea. However, it can bring tangible benefits to small everyday things and simplify the lives of farmers in many ways.

So how can we use Artificial Intelligence for sustainable farming?, what are the opportunities of AI in farming and how can AI help us tackle existing challenges?. Business models utilizing artificial intelligence can help meet rising global demand for food and support a more inclusive and sustainable food system by:

1. Enhancing the resilience of farming methods; 
2. Reducing the cost of quality inputs and services to underserved farmers; and
3. Improving market access to facilitate smallholder farmer integration into regional and global supply chains.

Although nascent in emerging economies, applications for artificial intelligence in agribusiness will proliferate as farmers’ access to the Internet and adoption of smart devices increases across low-income countries.

Artificially Intelligent Agribusiness and Sustainable Development Goals (SDGs)

Meeting the food and agriculture related Sustainable Development Goals (SDGs) in a sustainable and inclusive manner is daunting. Progress toward these SDGs over the past decade has been hampered by an increase in global hunger, with 820 million people hungry today. The challenge is to make food systems more efficient to meet the needs of the one-in-ten people who suffer from chronic hunger, to meet the projected doubling of global food demand by 2050 (from 2009), and to sustainably adjust to increased demand for animal-based products. Yet current production practices in many food systems result in up to a third of food being wasted. Private sector investment is critical to addressing these pain points, with UNCTAD estimating that the food and agriculture SDG investment gap is almost $200 billion per year, through 2030.5 Investing in new technologies will be critical to meeting these challenges to agriculture. The term “agtech” describes the application of technology especially software and connected hardware to the agriculture value chain. Agtech innovations have significant development potential, as they can help farmers use scarce resources such as water, and external inputs like agrichemicals, more efficiently, effectively, and sparingly. Agtech can also help farmers access the more advanced inputs they need to increase output, such as machinery and finance, and allow them to trace the origin and quality of their produce. In recent years, artificial intelligence (AI) technologies  including machine learning, natural language processing, robotics, and computer vision have been incorporated into agtech business models. Applications of AI for use in alternative credit scoring or ‘smart’ farm equipment, for example, have the potential to reduce the cost of serving smallholder farmers across the agriculture ecosystem, improve the efficient and sustainable use of resources, and overcome market asymmetries that prevent farmers from accessing regional and global value chains. The use of AI technologies has become commercially feasible for agtech in recent years through advances in big-data analytics, increased computing power, and cloud-based storage, as well as cost reductions in satellite imagery, remote sensors, and other hardware (including smart phones), and the increased affordability and availability of mobile connectivity. Despite these advantages, there are barriers to replicating and scaling AI technology, particularly in emerging markets. First, smallholder farmers often lack the skills and capital needed to utilize new technologies. Second, there are gaps in the agronomic data needed to teach AI systems, particularly given the diversity of farmland and crop varietals. Third, there are limits to the adoption of AI technology in markets where IT infrastructure is inadequate or the infrastructure needed to connect farmers to supply chains including roads, cold storage, and freight capacity suffers from underinvestment. Yet the potential of AI to help meet the critical challenges of hunger and climate change creates a clear incentive for development finance institutions (DFIs) such as IFC to invest in demonstrating the feasibility of these technologies in emerging markets.

Why Adopting AI is such a Challenge for Farmers

Farmers tend to perceive AI as something that applies only to the digital world. They might not see how it can help them work the physical land. This is not because they’re conservative or wary of the unknown. Their resistance is caused by a lack of understanding of the practical application of AI tools. New technologies often seem confusing and unreasonably expensive because AgriTech providers fail to clearly explain why their solutions are useful and how exactly they should be implemented. This is what happens with artificial intelligence in agriculture. Although AI can be useful, there’s still a lot of work to be done by technology providers to help farmers implement it the right way

What is AI?

AI is the science and engineering of making machines intelligent, especially intelligent computer programs. AI is, therefore, a series of approaches, methods, and technologies that display intelligent behavior by analyzing their environments and taking actions with some degree of autonomy to achieve specific objectives. AI techniques have seen rapid progress over the past decade, supported by an evolution in machine learning, improvements in computing power and data storage, and upgraded energy and communications infrastructure. The use of AI in agriculture is nascent but expanding As the distribution of venture capital into AI technologies closely tracks total venture capital flows, the latter can be used as a proxy for interest in AI. Venture capital flows to agtech firms have increased rapidly in the past five years, with the majority received by firms headquartered in the United States. Agtech firms using AI make up a growing share of these flows, receiving $6.7 billion in venture capital flows over the past five years, including $1.9 billion in 2018 alone.9 These flows illustrate that the feasibility of using AI in agtech has been demonstrated. Although nascent, investors are showing interest in directing funds to emerging market businesses that use AI. China has seen around 200 agtech venture capital deals, and the number of deals in India and Brazil is growing. In Asia and Africa, where smallholder farmers dominate the agricultural sector, agtech startups are focused on connecting farmers to larger buyers or cooperatives. Alternatively, markets like Brazil, where there are a number of significant larger holdings, are seeing more startups focused on precision farming. AI technologies can improve food system performance Improvements in the productivity and sustainability of the global agricultural system, as well as higher returns on investment for farmers, are critical to meeting the SDGs. In emerging markets, losses of food take place throughout the production, post-harvest handling, storage, and processing stages. The U.N.’s Food and Agriculture Organization estimates that annual food losses that occur from farm to fork are as much as one-third of annual global food production, or about 1.3 billion tons. In addition, emerging market agriculture sectors suffer from low total factor productivity growth, as the main contributors to productivity in low-income countries since the 1960s have been increased agricultural inputs and land use. Currently, the yield gap the difference between a crop’s potential yield and actual yield exceeds 50 percent in most low-income countries, and 76 percent in Sub-Saharan Africa. Another critical development challenge is that agriculture both contributes to and will be fundamentally affected by climate change. Land use, including agricultural practices, deforestation for arable land, and the forestry industry, account for 28 percent of net greenhouse gas emissions, while climate change affects the availability of, access to, and stability of the global food system. The challenge in meeting food demand and transporting food across markets sustainably cannot be solved through business-as usual farming practices.

The adoption and diffusion of AI technology and precision agriculture into agtech business models holds promise for addressing these challenges. For instance, AI applications in financial services, knowledge, and capital can help improve the cost efficiency of agribusinesses by using inputs “intelligently” and increasing the quality of outputs. AI applications also hold promise for improving the sustainability of farming practices by reducing fertilizer and pesticide use, enhancing the accuracy of pest and disease detection, and facilitating the automated grading of crops. Additionally, combining data on soil characteristics, weather, and other climactic factors and interpreting them through machine learning software helps planting, the management of farm operations, and harvesting. And AI technology can help enhance the transparency of global food supply chains, minimize food loss, and facilitate the monitoring of food quality standards. AI technologies are transforming agtech business models Extending products and services to underserved farmers. Farmers need access to credit and insurance to expand their businesses and manage risk, and to enhance the resilience of their operations. Many emerging market farmers lack access to affordable financial products because of the significant time and cost required to price their risk and collateral, as well as the difficulty of serving farmers in rural and remote areas. Technological advancements in satellite weather data collection and the wider adoption of mobile technology has dramatically reduced these costs, facilitating the extension of financial products to farmers in emerging economies. Machine learning platforms are increasingly being employed by lenders to generate credit scores or price new products to help farmers access the microloans and insurance needed to upgrade their inputs to production, and this includes farmers without traditional collateral or bank accounts. In general, these platforms allow farmers to upload photos of crops and pests or disease, which are processed alongside satellite, geospatial, and other data sources, to estimate a farmer’s collateral and/or to make estimates of the farmer’s individual financial health and creditworthiness.

How AI can be useful in Agriculture

Agriculture involves a number of processes and stages, the lion’s share of which are manual. By complementing adopted technologies, AI can facilitate the most complex and routine tasks. It can gather and process big data on a digital platform, come up with the best course of action, and even initiate that action when combined with other technology.

The Role of AI in the Agriculture Information Management Cycle

Combining artificial intelligence and agriculture can be beneficial for the following processes:

• Analyzing market demand. AI can simplify crop selection and help farmers identify what produce will be most profitable.
• Managing risk. Farmers can use forecasting and predictive analytics to reduce errors in business processes and minimize the risk of crop failures.
• Breeding seeds. By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions.
• Monitoring soil health. AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients.
• Protecting crops. AI can monitor the state of plants to spot and even predict diseases, identify and remove weeds, and recommend effective treatment of pests.
• Feeding crops. AI is useful for identifying optimal irrigation patterns and nutrient application times and predicting the optimal mix of agronomic products.
• Harvesting. With the help of AI, it’s possible to automate harvesting and even predict the best time for it.

Applications of AI Technologies

• Decision support - On-farm analytical support and software for farm operations. Predictive analytics can be a real game-changer. Farmers can collect and process significantly more data and do it faster with AI than they would otherwise. Analyzing market demand, forecasting prices, and determining the optimal time for sowing and harvesting are key challenges farmers can solve with AI. That said, AI can also gather soil health insights, provide fertilizer recommendations, monitor the weather, and track the readiness of produce. All of that enables farmers to make better decisions at every stage of the crop cultivation process.
• Cost saving - One particular farm management approach precision agriculture can help farmers grow more crops with fewer resources. Precision agriculture powered by AI could become the next big thing in farming. Precision farming combines the best soil management practices, variable rate technology, and the most effective data management practices to help farmers maximize yields and minimize spending. AI can provide farmers with real-time insights from their fields, allowing them to identify areas that need irrigation, fertilization, or pesticide treatment. Also, innovative farming practices like vertical agriculture may help increase food production while minimizing the use of resources. The result is reduced use of herbicides, better harvest quality, higher profits, and significant cost savings.
• Marketplaces - Online markets for purchasing farming inputs & for selling crops
• Logistics & infrastructure - Digital management and optimization of farm produce to markets
• Financial services - Credit scoring facilitating lending and insurance for farmers
• Livestock solutions - Traceability, increased yield, management of nutrition, disease and breeding
• Irrigation & water tech - Low-cost ‘smart’ irrigation through integrated sensor data
• Robotics & Equipment - Using satellite and weather data and computer vision to optimize deployment of agrichemicals.
• Automation - Agricultural work is hard, and labor shortages in this industry are nothing new. Farmers can solve this problem with the help of automation. Driverless tractors, smart irrigation and fertilizing systems, smart spraying, vertical farming software, and AI-based robots for harvesting are some examples of how farmers can get the work done without having to hire more people. Compared with any human farm worker, AI-driven tools are faster, harder, and more accurate.

For example, financial services providers can leverage a greater number of new and existing data points to price new insurance contracts and potentially trigger a payout for a predefined event such as below-average rainfall. Precision agriculture, alongside machine learning, has the potential to reduce agricultural insurance premiums by defining risks and improving risk assessment tools, and allows farms to move more quickly to prevent crop losses. WorldCover is an early example of an intermediary helping to connect farmers with insurance products. WorldCover uses AI to assess satellite, weather station, and agronomic data to determine the risk of weather events, and is working on smart contracts that use blockchain to trigger automatic payouts. The automatic disbursement of payouts via nonbank payments providers like M-Pesa allows farmers without bank accounts to obtain insurance. Such automatic payouts through nonbank providers reduces the frictions involved with farmers adopting financial products like crop insurance, which they need to enhance their resilience to climate shocks. Farmers in emerging markets often also lack timely, reliable, and sufficiently granular weather, pest, and market data to choose an appropriate crop to sow, and to realize the yields needed to service existing loans. Platforms using machine learning algorithms assess farm-level data against publicly available data sources to generate real-time business analytics that help farmers manage production risk, increase the effective use of inputs to generate cost savings, and optimize planting and harvest times to increase yields and farm-gate prices. The algorithm is refined as data is added through each crop cycle to improve the accuracy of farm-management recommendations. In India, for example, B2B platform CropIn allows agribusinesses to upload unstructured data into a smartphone app (farmer information on planting, varietals used, photos of crops). Machine learning algorithms can combine unstructured data with a range of other data sources to generate real-time advice on risk management, sales, warehousing, and sustainable farm practices. The app can be used offline with analytics applied once the device is connected again. Data from the app can also be used to generate credit risk assessments for access to finance, and for farm-to-fork traceability and quality control for access to global value chains. CropIn’s weather algorithms currently provide course correction advisory services for 5,000 farmers’ plots. Increasing market access through the reduction of trade frictions and facilitated adoption of new technologies will help smallholder famers integrate into regional and global supply chains. In emerging markets, agricultural supply chains are often inefficient or incomplete, with many smallholder farmers excluded from regional markets due to complex regulatory arrangements, outsized market power of commission agents, a lack of access to quality inputs, and a lack of knowledge of available markets, prices, and standards. AI can help address these market failures by improving traceability to prove the origin and quality of produce, which is needed to secure supply contracts and access markets. For example, Stellapps has developed a digital platform that collects data on cows and food safety and cooling equipment owned by small farmers in India; it uses the data to generate recommendations for the farmer on actions that will improve nutrition, growth, and milk production. The company also digitalizes the milk procurement process, including volume and quality measures, which can be done reliably onsite with Stellapps equipment. Stellapps monitors dairies’ chillers to provide assurance that quality is maintained throughout the supply chain. Ultimately, dairies can provide traceability in their supply chain and access premium milk markets. The company enhances the value of these data monitoring applications through its machine learning algorithm to assign each farmer a credit score, or ‘mooScore,’ based on their personal data and cow data (health, nutrition, fraud proofing), which allows lenders to expand their financing of farmers. Stellapps’ business model has been designed to scale, with the SmartMoo suite of apps currently touching over two billion liters of milk annually. AI-enabled platforms also give smallholder farmers the information they need to connect directly to buyers of their produce, reducing food waste and increasing farm income. IFC and TLcom Capital Partners invested $10 million in Kenyan company Twiga Foods in 2018. In 2019 the company raised an additional $30 million in a round led by Goldman Sachs. Twiga’s platform aggregates market participants, facilitating a more efficient matching of buyers and sellers in Africa’s large but fragmented fruit and vegetable market. For farmers, the platform enables access to a fairly-priced, transparent, mobile marketplace. For vendors, the benefit is increased reliability in sourcing highquality produce. Twiga’s system has reduced post-harvest losses to 5 percent, compared to 30 percent in informal markets where farmers typically sell produce. Twiga is now increasing the value of its platform for users by integrating AI to expand its service offering, recently partnering with IBM to design a machine learning algorithm to provide small-scale vendors with a credit score to disburse loans. Through an eight-week pilot, more than 220 loans were processed with an average size of around $30, which allowed vendors to increase the size of the orders they handled by 30 percent and increased profits for each retailer by 6 percent, on average. Another example of how AI can help make the food and agricultural supply chain more transparent and efficient, as well as reduce food waste, is Indian company Intello Labs, which is digitalizing the process of grading and tracking the quality of agricultural produce. Intello Labs’ technology system uses deep AI algorithms (convolutional neural networks) to identify produce defects and track the movement of produce across the supply chain. The company’s data-driven system has been designed to improve customer-level quality of produce, reduce food loss along the supply chain, and achieve better prices for producers by removing human subjectivity from the grading process and moving that process further upstream and closer to the farmer. The company has also designed the system for implementation across delivery modes mobile applications, fixed cameras on the processing line, cloud or edge computing so that it is cost effective and can be scaled. Intello Labs is currently trialing their system with a few companies in India and the United States. Enhancing the sustainability of agricultural farming methods. There are also a number of ways that businesses are using AI to improve agricultural sustainability, both in terms of environmental impact and return on investment at the farm gate. First, AI can improve on-farm management of pests and diseases. Cameroon-based start-up Agrix Tech has developed an application that helps farmers with low levels of literacy manage these issues. Farmers can upload photos of infected crops on their phones. Machine learning and translation technology is then applied to the images to provide pest and disease management advice in the local dialect. Importantly, the app can be used offline, as the AI technology does not require Internet connectivity, which is limited in parts of rural Cameroon. Second, applications using AI allow farmers to access more advanced or ‘smart’ machinery, which helps optimize the use of capital inputs. Hello Tractor operates in Nigeria, Kenya, Mozambique, Bangladesh, and Pakistan, offering tractor services between farmers so that owners of compact tractors can maximize their investment. The platform takes on the risk of payment, delivery, and asset security, creating an additional income stream for farmers. The business recently partnered with IBM to use machine learning to help farmers predict crop yields that, combined with advanced analytics and blockchain technology, can be mined to develop a credit score for loans. Forecasted weather data from The Weather Company, an IBM business, remote sensing data (e.g., satellite), and IoT data from tractors will also be incorporated into the app to help smallholder farmers know when to cultivate, the quality of their farm cultivation, what to plant, and the appropriate fertilizer using remote sensing and IoT data. If successful on a large scale, applications like Hello Tractor could help farmers simultaneously access the equipment and knowledge needed to improve the efficiency of their farming practices and may facilitate the expansion of their area under cultivation. Third, digital technologies including AI are being leveraged for so-called precision farming, in which intelligence from both farmers and public data sources is assessed by algorithms in order to use inputs to production water, land, pesticides, fertilizer, and nutrition more effectively and increase the farmer’s return on investment. Intelinair is an American agtech business that gathers high-resolution aerial images, temperature readings, humidity measurements, rainfall, soil samples, terrain type, equipment utilized, planting rates, applications, and other data points, and applies hyperspectral analysis, computer vision, and deep learning to identify patterns and build a complete and precise situational representation of every monitored field for the entire growing season. As the machine learning system trains on new data, it becomes stronger, smarter, and more effective. The system can, for instance, identify abnormal crop conditions before the human eye can detect them. The intelligence generated by the system is delivered to farmers via smart alerts that allow them to make proactive, real-time decisions in their fields. Minimizing the cost of inputs should improve the sustainability and resilience of farm-gate returns. Although precision agriculture is dominant in higher income economies like the United States, there are new applications by agtech businesses in emerging markets with large-scale production systems. For example, Agrosmart is a Brazilian company offering its technology platform to farmers in Latin America, the United States, and Israel. Agrosmart consolidates millions of data-points from field sensors and satellites, and applies machine learning and other AI technologies to improve farming performance, reduce environmental impact, and deliver intelligence across the agriculture value chain. The company offers several packages of services to producers of various sizes for an annual subscription fee. Each package delivers different information to the client, such as weather forecast, irrigation advice, and soil conditions. The system also eliminates the need for Internet or cellular coverage in the field to send the data to the Internet. With the insights provided, producers can become more efficient, reducing labor time and water and energy consumption, while increasing yield. Users also benefit from a connected and monitorable supply chain. Precision farming is now being extended, from applications that provide insights and information to farmers, to on-farm robotics equipment that acts autonomously. For example, India-based robotics company TartanSense is building a semiautonomous rover to traverse cotton farms with a downward facing camera capturing images of plants and weeds. When AI algorithms running on the system detect weeds, the rover automatically sprays them with agrichemicals through precision nozzles. This can increase farm income by reducing labor required to pull weeds. It can also minimize chemical residue levels from broadcast spraying in the soil and reduce health risks to operators traditionally women from residue from traditional spraying methods. AI-enabled applications like TartanSense’s spraying system are increasingly being trialed and rolled out in conjunction with pest and disease detection, pruning, harvesting, and crop grading.

Challenges in Scaling AI

The focus of AI technology today is on larger farming operations with the aim of reducing input costs. Yet there are technical difficulties in building reliable AI systems that can be applied across different terrains. On-farm conditions are continuously changing, even across the same plot. For example, different crops vary in their physical characteristics due to differences in rainfall or soil quality (among other factors), which makes it difficult for machine learning systems to identify and provide actionable advice, even for the same crops. Additionally, the seasonality of the agricultural sector increases the time needed for machine learning algorithms to learn and prove their value, creating a time-lag to replication. Adoption will therefore be slow until the return-on-investment for applications using AI is proven in each market context. At the smallholder level, agtech companies need to understand the needs, capacity, and constraints of emerging market smallholders in order to design and distribute appropriate solutions. This requires stakeholders like government, agtech companies, and other capacity building institutions to undertake hands-on training in the use of digital technology with farmers across emerging markets so that they are equipped to use AI applications as they become more cost-effective. Barriers to adoption exist beyond the technology itself, with challenges in data supply, data use, and a lack of enabling infrastructure. Like companies in other industries that compile large data sets, agtech companies are increasingly ensuring that their data security practices and systems are ISO 27001 and EU Global Data Protection Regulation (GDPR) compliant, with supporting policies and data recovery plans in place. Empowerment of farmers who provide the data that the technology depends on is also critical and begins with obtaining the informed consent of farmers to collect their data, including any third-party use arrangements. Additionally, farmers cannot realize the benefits of digital technologies without improvements in complementary infrastructure, including the telecommunications, transport, and irrigation required to support AI innovations. DFI’s have a role to play in supporting agtech businesses AI applications have yet to penetrate the poorest frontier countries. Agtech is now at an inflection point, however, with venture capital flows increasing to emerging markets in recent years. The cost of enabling technologies continues to fall, which allows scalable product and service companies to be built. This cycle leads to product adoption and further capital flows. DFIs like IFC have a role in demonstrating the feasibility of AI use in agtech to catalyze these flows, as well as in addressing enabling barriers such as farmer training. IFC invests in and provides advisory support across the agribusiness supply chain from farm to retail to help boost production, increase liquidity, improve logistics and distribution, and expand access to credit for small farmers. Since 2014, IFC has invested over $60 million in agtech related companies and has made more than 10 indirect agtech investments through accelerators, seed funds, and venture capital funds. Agtech is increasingly becoming a centerpiece of IFC’s Manufacturing, Agribusiness & Services (MAS) advisory work with IFC clients. That includes supporting clients to deploy and effectively use farmer information service tools like CropIn and Farm Force that leverage local weather monitoring stations and unaccompanied aerial vehicles (UAVs) to capture appropriate spatial and temporal data. Application of these tools at various nodes in a client’s production and distribution chains enables improved decision making, increased access to inputs, finance and markets, and improved visibility of production across the chain. IFC is also working to reduce barriers companies face in adopting agricultural technologies. In 2018, IFC held its inaugural Global Agribusiness Conference in Amsterdam, which focused on the challenges faced by smallholder farmers, as well as solutions to promote sustainable development, reflecting the need to ensure all stakeholders benefit as value chains become increasingly complex and global. The conference brought together companies whose supply chains rely on smallholder farmers, organizations offering products and services, investors, donors, and government officials from around the globe. In addition, IFC and the World Bank held the first Digital Disruption in Agriculture Forum in Addis Ababa in May 2019. The purpose of the Forum was to help identify digital tools and service providers that can benefit the mainly smallholder farmers in Ethiopia, helping professionalize agribusiness and improve livelihoods. The use of digital technologies in agriculture remains limited in many lowincome countries, and conferences like these provide an opportunity for learning, networking, and knowledge sharing that can help identify promising digital products and services to improve smallholder agriculture practices and yields. Building on interest in the first digital forum, IFC and the World Bank arranged a second forum in Ethiopia, scheduled for April 2020. This conference has been postponed due to the outbreak of coronavirus and will be held later this year.

Problems Farms can face while Adopting AI

Considering the benefits of artificial intelligence for sustainable farming, implementing this technology may look like a logical step for every farmer. However, there are still some serious constraints.

• Lengthy technology adoption process. Farmers need to understand that AI is only an advanced part of simpler technologies for processing, gathering, and monitoring field data. AI requires a proper technology infrastructure for it to work. That’s why even those farms that already have some technology in place can find it difficult to move forward. This is also a challenge for software companies. They should approach farmers gradually, giving them simpler technology first, such as an agriculture trading platform. Once farmers get used to a less complicated solution, it will be reasonable to step it up and offer something else, including AI features.
• Lack of experience with emerging technologies. The agricultural sector in developing countries is different from the agricultural sector in Western Europe and the US. Some regions could benefit from artificial intelligence agriculture, but it may be hard to sell such technology in areas where agricultural technology is not common. Farmers will most likely need help adopting it. Therefore, tech companies hoping to do business in regions with emerging agricultural economies might need to take a proactive approach. In addition to providing their products, they’ll have to provide training and ongoing support for farmers and agribusiness owners who are ready to take on innovative solutions.
• Privacy and security issues. Since there are no clear policies and regulations around the use of AI not just in agriculture but in general, precision agriculture and smart farming raises various legal issues that often remain unanswered. Privacy and security threats like cyberattacks and data leaks may cause farmers serious problems. Unfortunately, many farms are vulnerable to these threats.

How AI should be combined with other Technologies

As we’ve mentioned, AI can’t exist without other technologies already in place such as big data, sensors, and software. Likewise, other technologies need AI for them to function properly. For example, in the case of big data, the data itself is not particularly useful. What actually matters is how it’s processed and whether it’s relevant. The time, the place, and the selection criteria all determine whether AI recommendations based on a set of data are going to be helpful. That’s why it’s also important to have good data engineers and data analysts to make AI technology work. Let’s talk about the uses of artificial intelligence in farming in more detail.

• Big data for informed decision-making. The real goal of producing and collecting data is putting it to use. In farming, data analytics can result in massive productivity increases and significant cost savings. By combining AI with big data, farmers can get valid recommendations based on well-sorted real-time information on crop needs. This, in turn, will take away the guesswork and enable more precise farming practices such as irrigation, fertilizing, crop protection, and harvesting.
• IoT sensors for capturing and analyzing data. Farmers can use IoT sensors and other supporting technology (e.g. drones, GIS, and other tools) to monitor, measure, and store data from fields on a variety of metrics in real time. By combining AI farming tools with IoT devices and software, farmers can get more accurate information faster. Better data means better decisions and less time and money spent on trial and error.
• Automation and robotics for minimizing manual work. Artificial intelligence combined with autonomous tractors and IoT can solve one of the most common problems in farming: a shortage of labor. These technologies are also potentially cost-effective because they’re more accurate and thus reduce errors. Taken together, AI, autonomous tractors, and IoT are the key to precision agriculture. Another less common but rapidly growing technology is robotics. Agricultural robots are already being used for manual work, such as picking fruits and vegetables and thinning lettuce. The advantages of robots over farmworkers are considerable. They can work longer, are more precise, and are less prone to error.

Reality vs Expectations of Artificial Intelligence for Sustainable Farming

The benefits of AI in agriculture are undeniable. Smart farming tools and vertical farming systems can perform small, repeatable, and time-consuming tasks so farm workers can use their time for more strategic operations that require human intelligence. However, it’s important to realize that unlike a tractor, one can’t just buy AI and start it. AI is not something tangible. It’s a set of technologies that are automated through programming. Artificial intelligence is essentially a simulation of thinking; it’s learning and problem-solving based on data. AI is just the next step in the development of smart farming, and it needs other technology to actually work. In other words, to reap all the benefits of AI, farmers first need a technology infrastructure. It will take some time, possibly even years, to develop that infrastructure. But by doing so, farmers will be able to build a robust technology ecosystem that will stand the test of time. For now, technology providers need to think about a few things: how to improve their tools, how to help farmers address their challenges, and how to easily and understandably convey that machine learning helps solve real struggles, such as reducing manual work. The future of AI in agriculture is bound to be fruitful.

Conclusion

Conclusively, Agribusinesses are leveraging AI to create new cost-effective business models and to provide the information they need to reach small farmers in emerging markets. Agtech firms using AI for now mostly with large-scale farmers have just begun to demonstrate their viability and are looking to scale their innovations. With growing deal activity and size, there will be plenty of opportunities for private sector investors to mobilize their capital toward meeting the agriculture-related SDGs. However, governments, investors, and industry will need to collaborate in addressing data, infrastructure, and human capital gaps before commercial AI integration is feasible at a larger scale.

For enquiries, Contact:

Tobi Fatunmbi

email: tobi.measureit@gmail.com

mobile number: +2348163554014

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