March 14, 2023
What is the role of artificial intelligence in solving the global food crisis and other agricultural problems? To what extent can artificial intelligence be utilized to alleviate the global issue of food inequality?
Despite producing enough food to feed the world's population, nearly one billion people still suffer from hunger and malnutrition because of food wastage, climate change, and other factors. Moreover, with the global population projected to reach 9.7 billion by 2050, the pressure is mounting on the agricultural industry to produce more food while using fewer resources and reducing its environmental impact.
Fortunately, the integration of artificial intelligence (AI) in agriculture has the potential to transform food systems and help address the global food crisis. By analyzing data from various sources, AI can help farmers make data-driven decisions, optimize resource usage, and reduce environmental impact. For example, the World Economic Forum has reported that AI integration in agriculture could bring about a 60% decrease in pesticide usage and a 50% reduction in water usage.
In India, a country with one of the most prominent Agtech startups, enhancing 15 agriculture datasets, such as soil health records, crop yields, weather, remote sensing, warehousing, land records, agriculture markets, and pest images, could lead to a $65 billion opportunity, according to research conducted by NASSCOM and McKinsey.
In this article, we'll explore how AI is being used in agriculture, from predicting crop yields to improving soil health, and how it can pave the way for a more sustainable and food-secure future.
There are many approaches to leveraging AI to enhance efficiency and productivity in agriculture. We've gathered a few examples to illustrate some of them.
Analyzing market demand is a crucial aspect of modern agriculture. AI can help farmers select the best crop to grow or sell. Descartes Labs is a New Mexico-based company that offers an AI-powered platform to help farmers evaluate market demand. The company develops machine learning algorithms to analyze satellite imagery and weather data, providing valuable insights on optimal planting times and the best crops to grow. By analyzing data patterns, Descartes Labs can predict the market demand for specific crops and help farmers maximize their profits.
Through forecasting and predictive analytics, farmers can minimize the risk of crop failures. For example, lntello Labs is a startup company in India that uses artificial intelligence (AI) to help farmers analyze the quality of their produce and reduce food loss.
The company develops software application products that use AI and computer vision algorithms to analyze fruits and vegetables and provide insights on quality, ripeness, and size. These AI tools can also detect defects and diseases in crops, enabling farmers to take preventive measures before the crops are affected.
By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions. With the help of AI, scientists can identify the best-performing plant varieties and crossbreed them to create even better hybrids.
Yes, the process of creating hybrids has been used in the agricultural industry for many years. However, gathering genomic information of seeds through AI technologies like that of Seed-X can help to speed up the process and increase the likelihood of success.
AI systems can conduct chemical soil analyses and estimate missing nutrients accurately. One example is AI-powered hardware and software built by Agrocares, a Dutch agritech company.
One of their products, Nutrient Scanner, collects data from soil samples and provides farmers with accurate estimates of missing nutrients and overall soil status. This allows farmers to adjust their fertilizer application and irrigation practices to ensure optimal crop growth and reduce environmental impact.
In addition to this, AgroCares provides farmers with customized recommendations for soil management, helping them to maintain the health of their soil in the long term.
AI can monitor the state of plants to spot and predict diseases, identify and remove weeds, and recommend effective treatment of pests. For example, a precision agriculture startup called Taranis uses computer vision and machine learning to analyze high-resolution images of crops, providing plant insights to identify signs of stress or disease. Their AI-powered technologies can detect and classify diseases and pests with high accuracy. It can also suggest the most effective treatment for pests, reducing the need for broad-spectrum insecticides that can harm beneficial insects and lead to pesticide resistance.
Estimating crop growth and maturity is a tedious and challenging task for farmers, but AI can handle the job quickly and precisely. Through AI-powered hardware such as sensors and image recognition tools, farmers can detect and track crop changes to obtain accurate predictions on when crops will reach optimal maturity. Studies have found that using AI to predict the maturity of crops resulted in a higher accuracy rate than the accuracy rate achieved by human observers. This increased accuracy can bring significant cost savings and higher profits for farmers.
Integrating sensors and AI systems enables farmers to accurately monitor how much water and nutrients are available in the soil. Using sensors in soil monitoring could involve deploying devices that measure various parameters like soil moisture, temperature, pH levels, and nutrient content. These sensors send information back to AI systems which then analyze it and provide instructions to farmers on how best to manage their crops based on what they find out about the soil conditions.
For example, the AI system might identify areas of the field where the soil is too dry or too moist and provide recommendations on when and how much water to apply to optimize crop growth. Similarly, the system might detect nutrient deficiencies in the soil and provide advice on the suitable types and amounts of fertilizer to use to improve yields.
Farmers can use AI-powered systems to detect insects and plant diseases more quickly than humans. For example, an AI-powered system could detect an infestation of aphids on a crop of strawberries, send the data back to the farmer's mobile phone, and then suggest what action should be taken next. If a pesticide application is needed, the system could even automate it through a connected sprayer.
Weed or pest control can be automated with AI technologies. With the help of computer vision, weeding robotics is said to be remarkably precise, resulting in a 90% reduction in pesticide usage. Based on data analytics, these tools can calculate how much pesticide is needed for each field based on data about its history, soil status, or crop type.
Blue River Technology has disrupted traditional weed control methods with its flagship product, the "See and Spray" machine. Using computer vision and machine learning, the device can distinguish between crops and weeds and then apply herbicide only where needed. This can be cost-effective.
Chatbots can be used as an interface between farmers and their customers or distributors. Farmers can use these conversational agents to answer questions about products or services offered, order supplies, and check inventory levels.
Chatbots are also useful for managing databases of information about crops and soil conditions. They act like virtual farm assistants for executing farm tasks. Chatbots like Microsoft's FarmVibes.Bot provides farmers with personalized advice and recommendations based on data. The platform uses natural language processing and machine learning algorithms to understand farmers' queries and provide real-time insights on weather, market prices, and other agricultural information. It's currently being used by over half a million sub-Saharan African farmers.
Artificial Intelligence (AI) in agriculture is poised to grow significantly in the coming years, as it has the potential to revolutionize the sector by improving crop yields, reducing waste, and increasing efficiency. According to a report by MarketsandMarkets, the AI in agriculture market is predicted to experience explosive growth, with the market size expected to grow from $2.35 billion in 2020 to $10.83 billion by 2025 at a Compound Annual Growth Rate (CAGR) of 35.6% during the forecast period.
Collecting and analyzing large amounts of data is among the most notable advantages of AI in agriculture for farmers. This will lead to more informed decision-making and improved crop yields, essential for addressing the global food security challenge.
Farmers can also use AI to monitor soil conditions, crop growth, and climate changes. As a result, they will be able to detect diseases early and take the necessary preventive measures before a crop is destroyed. AI will also continue to aid in forecasting weather changes, allowing farmers to plan their activities better and to take advantage of the optimal planting season.
Furthermore, AI can also help to reduce waste and resource usage. For example, farmers can use AI to optimize the amount of fertilizer and water used on their crops, leading to a more sustainable and environmentally friendly practice. This optimization will reduce the risk of soil and water contamination, which is an increasing concern today.
While the benefits of AI in agriculture are numerous, the reality is that most farmers worldwide, particularly smallholder farmers, lack the necessary resources to implement these technologies. Smallholder farmers typically have limited access to technical training, which makes it difficult for them to operate AI systems effectively. Many also lack the financial resources needed to purchase the equipment and software required for AI-based farming.
The adoption of AI in agriculture must be inclusive, considering the needs and limitations of smallholder farmers, who make up a significant portion of the global agricultural workforce. Initiatives that provide access to training and funding for smallholder farmers to implement AI-based farming practices can help bridge the divide. With this, farmers at all levels can benefit from emerging technologies that the world needs to secure our food system's future.
Based mostly in rural areas, smallholder farmers might not have access to experts who can deliver helpful advice to aid modern farming practices. At Jiva, we are happy to be part of a growing movement that is helping to bring more transparency and education to smallholder farmers through AI-powered advice.
Jiva's AI-powered apps, Jiva and AgriCentral, can provide targeted advice to farmers at scale. They’re already helping farmers achieve more sustainable and productive farms.