Global population growth, expected to reach 10 billion by 2050, is putting significant pressure on the agricultural sector to increase crop production and maximize yields. To cope with the looming food shortage, two potential approaches have emerged: expand land use and adopt large-scale agriculture, or adopt innovative practices and leverage technological advances to increase the productivity of existing farmland.
Driven by many obstacles to achieving desired agricultural productivity – limited land, labor shortages, climate change, environmental problems and declining soil fertility, to name a few – the modern agricultural landscape is evolving, branching out in various innovative directions. Agriculture has come a long way from hand plows or horse-drawn machinery. Each season brings with it new technologies designed to improve efficiency and capitalize on the harvest. However, both individual farmers and global farms often miss the opportunities that artificial intelligence in agriculture can bring to their farming methods.
In this article we address the benefits that the application of artificial intelligence can bring to the agri-food sector by showing some examples of practical applications.
Benefits of AI in agriculture
Until recently, using the words AI and agriculture in the same sentence might have seemed an odd combination. After all, agriculture has been the backbone of human civilization for millennia, providing sustenance and contributing to economic development, while the more primitive AI emerged only a few decades ago. However, innovative ideas are being introduced in every field, and agriculture is no exception. In recent years, the world has witnessed rapid advances in agricultural technology, revolutionizing farming practices. These innovations are becoming increasingly essential as global challenges such as climate change, population growth and resource scarcity threaten the sustainability of our food system. The introduction of artificial intelligence solves many challenges and helps reduce many disadvantages of traditional agriculture.
The main benefits of applying artificial intelligence techniques in the agri-food sector are listed below.
Data-driven decision making
The modern world is all about data. Organizations in the agricultural sector are using data to gain insights into every detail of the farming process, from understanding every single acre of a field to monitoring the entire product supply chain to gaining in-depth insights into the yield generation process. AI-powered predictive analytics is already paving the way for farms. Farmers can collect and process more data in less time thanks to AI. In addition, AI can analyze market demand, predict prices, and determine optimal timing for planting and harvesting.
Artificial intelligence in agriculture can help explore soil health to gather information, monitor weather conditions, and recommend fertilizer and pesticide application. Agricultural management software can increase production and profitability, enabling farmers to make better decisions at each stage of the growing process.
Cost saving
Improving agricultural yields is a constant goal for farmers. Combined with artificial intelligence, precision agriculture can help farmers grow more crops with fewer resources. Artificial intelligence in agriculture combines the best soil management practices, variable-rate technology, and the most effective data management practices to maximize yields and minimize expenses.
The application of artificial intelligence in agriculture provides farmers with real-time crop information, helping them identify areas that need irrigation, fertilization or pesticide treatment. Innovative farming practices, such as vertical farming, can also increase food production while minimizing resource use. The result is reduced herbicide use, improved crop quality, higher profits and significant cost savings.
Automation impact
Farm work is hard, so labor shortages are nothing new. Fortunately, automation offers a solution without the need to hire more people. While mechanization transformed farming tasks that required superhuman sweat and draft animal labor into jobs that took only a few hours, a new wave of digital automation is once again revolutionizing the industry.
Automated farm machinery such as driverless tractors, smart irrigation, fertilization systems, IoT-powered agricultural drones, smart spraying, vertical farming software, and artificial intelligence-based greenhouse robots for harvesting are just a few examples. Compared with human agricultural workers, AI-driven tools are much more efficient and accurate.
Applications of artificial intelligence in agriculture
According to MarketsandMarkets, the artificial intelligence market in agri-food is expected to grow from $1.7 billion in 2023 to $4.7 billion by 2028.
Traditional agriculture involves several manual processes. Implementing AI models can have many advantages in this regard. By integrating technologies already adopted, an intelligent farming system can facilitate many tasks. AI can collect and process big data, determining and initiating the best course of action. Here are some common use cases of AI in agriculture.
Optimizing automated irrigation systems
AI algorithms enable autonomous crop management. When combined 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. Artificial intelligence in smart greenhouses optimizes plant growth by automatically adjusting temperature, humidity and light levels based on real-time data.
Detection of leakage or damage to irrigation systems
Artificial intelligence plays a crucial role in leak detection in irrigation systems. By analyzing data, algorithms can identify patterns and anomalies that indicate potential leaks. Machine learning (ML) models can be trained to recognize specific leak signatures, such as changes in flow or water pressure. Real-time monitoring and analysis enables early detection, preventing water wastage and potential crop damage.
Artificial intelligence also incorporates weather data along with crop water requirements to identify areas of excessive water use. By automating leak detection and providing alerts, AI technology improves water efficiency by helping farmers conserve resources.
Crop and soil monitoring
The wrong combination of nutrients in the soil can seriously affect crop health and growth. Identifying these nutrients and determining their effects on crop yield with AI allows farmers to easily make necessary changes.
While human observation is limited in its accuracy, computer vision models can monitor soil conditions to gather accurate data needed to combat crop diseases. This plant science data is then used to determine crop health, predict yields and flag any particular problems. Plants initiate artificial intelligence systems through sensors that detect their growing conditions, triggering automatic adjustments to the environment.
In practice, artificial intelligence in agriculture and farming has been able to precisely track the growth stages of wheat and the ripening of tomatoes with a speed and accuracy that no human can compare.
Disease and pest detection
In addition to detecting soil quality and crop growth, computer vision can detect the presence of pests or diseases. This is done by using artificial intelligence in agricultural projects to scan images and detect mold, rot, insects or other threats to crop health. Together with warning systems, this helps farmers act quickly 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 more than 90 percent accuracy. It can also identify insects such as flies, bees, moths, etc. with the same degree of accuracy. However, researchers first had to collect images of these insects in order to have a training dataset of adequate size with which to train the algorithm.
Monitoring the health of livestock
It might seem easier to detect health problems in livestock than in crops; in fact, it is particularly challenging. Fortunately, artificial intelligence for agriculture can help with this. For example, a company called CattleEye has developed a solution that uses drones, cameras and computer vision to monitor cattle health remotely. It detects atypical cattle behaviors and identifies activities such as calving.
CattleEye uses artificial intelligence and ML analytics solutions to determine the impact of diet and environmental conditions on cattle and provide valuable insights. This knowledge can help farmers improve cattle welfare to increase milk production.
Smart application of pesticides
By now, farmers are well aware that pesticide application is ripe for optimization. Unfortunately, both manual and automated application processes have significant limitations. Manual pesticide application offers greater precision in targeting specific areas, although it can be slow and difficult work. Automated pesticide spraying is faster and less labor intensive, but often lacks precision and leads to contamination of the environment.
Drones with artificial intelligence offer the best advantages of each approach while avoiding its disadvantages. Drones use computer vision to determine how much pesticide to spray on each area. While still in its infancy, this technology is rapidly becoming more accurate.
Performance mapping and predictive analysis
Yield mapping uses ML algorithms to analyze large datasets in real time. This helps farmers understand the patterns and characteristics of their crops, enabling better planning. By combining techniques such as 3-D mapping, data from sensors, and drones, farmers can predict soil yields for specific crops. Data are collected in multiple drone flights, enabling increasingly precise analyses through the use of algorithms.
These methods make it possible to accurately predict future yields of specific crops, helping farmers know where and when to sow seeds and how to allocate resources for the best return on investment.
Automatic weeding and harvesting
Similar to how computer vision can detect pests and diseases, it can also be used to detect weeds and invasive plant species. When combined with machine learning, computer vision analyzes the size, shape, and color of leaves to distinguish weeds from crops. These solutions can be used to program robots that perform robotic process automation (RPA) tasks, such as automatic weeding. In fact, such a robot has already been used effectively. When these technologies become more affordable, both weeding and harvesting could be performed entirely by intelligent robots.
Selection of harvested products
Artificial intelligence is not only useful for identifying potential problems in crops during their growth. It also has a role to play after the harvest of produce. Most sorting processes are traditionally done manually, but artificial intelligence can sort produce more accurately.
Computer vision can detect pests and diseases in harvested crops. In addition, it can sort produce according to its shape, size and color. This allows farmers to quickly separate produce into categories, for example, to sell to different customers at different prices. Traditional manual sorting methods, on the other hand, can be labor-intensive.
Surveillance
Security is an important part of running a farm. Farms are a common target for thieves because it is difficult for farmers to monitor their fields around the clock. Animals are another threat, whether it is foxes breaking into the chicken coop or the farmer’s livestock damaging crops or equipment. When combined with video surveillance systems, computer vision and artificial intelligence can quickly identify security breaches. Some systems are even advanced enough to distinguish employees from unauthorized visitors.
