Whether it is your favourite burger from McDonald’s, or the crispy nachos or your favourite shakes, the primary source is a farmer who lives far off in some village, working day and night to maximize his produce. With increased population and globalization, the demand for nutrition continues to increase around the world. This creates new economic pressures—and opportunities—for farmers. This puts responsibility on the agriculture industry to promote more sustainable practices. The emergence of rapidly evolving technologies, such as AI, offers agriculture players another powerful tool to meet these challenges head on and unlock greater efficiency and effectiveness throughout their businesses. Generative AI (gen AI) particularly has attracted the imaginations of agriculture leaders and has the potential to create significant change.
It has also brought to light the application of many other, long-existing approaches, such as analytical AI, with proven use cases and still relatively low levels of adoption. When combined, analytical AI and gen AI have the potential to unlock value across the value chain and across business operations. This article explains how companies in the $4 trillion global food production industry can strengthen their AI efforts by leveraging gen AI.
Applying gen AI in agriculture
Generally speaking, “gen AI” includes the applications that process large and variable sets of unstructured data to perform more than one task. Thus, gen AI can generate new ideas by identifying patterns in large unstructured data sets, particularly when it comes to complex tasks such as molecular research, marketing or agronomy, and code generation. The agriculture industry is set to be disrupted by AI and gen AI because of its high volumes of unstructured data, heavy reliance on labor, complex supply chain logistics, and long R&D cycles. Moreover, the sheer number of farmers who value customized offers and low-cost services make this industry prone to be disrupted by AI.
As an example, gen AI can develop testing scenarios by synthesizing millions of data points on weather, soil conditions, and pest and disease pressure, and analytical AI models can then simulate those scenarios. Using both technologies together has the potential to increase efficiencies, lower costs, and improve environmental impact for all agricultural players.
Key areas where AI plays a role in agriculture
Managing automated irrigation systems
AI algorithms combine with IoT (Internet of Things) sensors that monitor soil moisture levels and weather conditions, algorithms can decide in real-time how much water to provide to crops. An autonomous crop irrigation system is designed to conserve water while promoting sustainable agriculture and farming practices. AI in smart greenhouses optimizes plant growth by automatically adjusting temperature, humidity, and light levels based on real-time data.
Detecting leaks or damage to irrigation systems
AI plays an important role in detecting leaks in irrigation systems. By analyzing data, algorithms can identify patterns and anomalies that indicate potential leaks. AI provides real-time monitoring for early detection of leaks, preventing water waste together with potential crop damage. AI also uses weather data alongside crop water requirements to identify areas with excessive water usage.
Crop and soil monitoring
The wrong combination of nutrients in soil can seriously affect the health and growth of crops. AI helps identify these nutrients and to determine their effects on crop yield. Human observation comes with its limitations in the form of limited accuracy. AI analyzes soil conditions to gather accurate data necessary for combatting crop diseases. This plant science data is then used to determine crop health, predict yields, while flagging any particular issues.
Detecting disease and pests
AI detects diseases in crops as well. Using AI in agriculture projects to scan images to find mold, rot, insects, or other threats that threaten crop health. When combined with alert systems, these AI models help farmers to act quickly in order to exterminate pests or isolate crops to prevent the spread of disease. AI technology in agriculture has been used to detect apple black rot with an accuracy of over 90%. It can also identify insects like flies, bees, moths, etc., with the same degree of accuracy.
Monitoring livestock health
It may seem easier to detect health problems in livestock than in crops, in fact, it’s particularly challenging. Thankfully, AI for farming can help with this. For example, a company called CattleEye uses drones, cameras, and computer vision to monitor cattle health remotely. It detects atypical cattle behavior and identifies activities such as birthing
Sorting harvested produce
AI is not only useful for identifying potential issues with crops while they’re growing. It also has a role to play after the produce has been harvested. Traditionally, workers carry out most sorting processes manually; however, AI can sort produce more accurately.
Current challenges due to AI in agriculture
People generally think of AI as something that applies only to the digital world, with no connection whatsoever to physical farming tasks. This assumption is mostly due to a lack of understanding of AI tools. Most people don’t fully realize how AI in agricultural biotechnology works, especially those in non-tech-related sectors. This leads to slow AI adoption across the agricultural sector. Although agriculture has seen countless developments in its long history, many farmers are more familiar with traditional methods. A vast majority of farmers are unlikely to have worked on projects that involved AI technology. Thus, these challenges lead to a lower adoption rate of AI by farmers in their agricultural practices.
The success of human society is essentially dependent on the optimization of its agricultural systems. Traditional farming methods are becoming outdated, need for advanced technological solutions. Worldwide, the impact of automation on industries has always been considerable. Digital technology is now playing a huge role in transforming agriculture, and the impact of artificial intelligence (AI) in agriculture is set to be fruitful.
