The Role of AI in Modern Agriculture

Imagine a farmer getting up early to check insights from a smartphone app that just informed him which crops are thirsty, which soil patches require fertilizers, and where pests might be hiding. He would not be going into the fields with a sickle in hand.

Does that sound futuristic? Indeed, it is already taking place, and artificial intelligence (AI) is the reason for this.

Welcome to the future of farming—where data meets dirt, and machines are learning how to help us grow our food smarter, not harder.

We at PSSPL have a strong interest in how technology may be used to address pressing issues in the real world. And one of the most fascinating fields where AI-driven innovation is planting the seeds of change is agriculture, with all its intricacies.

Why Use AI in Farming? What’s so important?

Let’s begin by discussing the issue. Farmers are under pressure to produce more food with fewer resources as the world’s population rises, arable land is decreasing, and climate change is interfering with traditional farming practices.

According to the Food and Agriculture Organization, in order to feed the 9.3 billion people on the planet by 2050, reports says we will need to produce 60% more food. It might be difficult to do it using a farming-as-usual strategy given the difficulties facing the business right now. Furthermore, this would increase the significant burden we currently place on our natural resources.

Artificial intelligence can help us in this situation. The market for artificial intelligence in agriculture is projected to increase from $1.7 billion in 2023 to $4.7 billion by 2028, underscoring the critical role that cutting-edge technology play in this industry.

AI’s advantages in agriculture

Until recently, it might have sounded odd to combine the terms artificial intelligence and agriculture. After all, whereas even the most basic AI just appeared a few decades ago, agriculture has been the foundation of human society for millennia, serving as a source of food and fostering economic growth.

However, new concepts are being introduced in many sectors of the economy, including agriculture. Rapid advances in agricultural technology have revolutionized farming techniques worldwide in recent years.

As the sustainability of our food system is threatened by global issues like population increase, climate change, and resource scarcity, these technologies are becoming more and more important.

Many problems are resolved and many of the drawbacks of conventional farming are lessened with the introduction of AI.

Impact of automation

Labor shortages have long existed since agricultural work is difficult. Fortunately, automation offers an alternative to hiring additional staff.

Agricultural tasks that required superhuman sweat and draft animal labor were reduced to a few hours of work by mechanization, but a new wave of digital technology is once again transforming the industry.

Examples include IoT-powered agricultural drones, driverless tractors, smart irrigation, fertilization systems, smart spraying, vertical farming software, and AI-based greenhouse robots for harvesting. AI-driven tools are significantly more accurate and efficient than any human farm worker.

Data-driven choices

Data is everything in the modern world. Data is used by organizations in the agricultural sector to gain detailed insights into every aspect of farming, from comprehending each acre of a field to tracking the entire supply chain for product to acquiring profound insights into the process of yield development.

Predictive analytics driven by AI is already opening doors for agribusinesses. With AI, farmers can collect and process more data faster.

AI is also capable of forecasting pricing, analyzing market demand, and identifying the best periods to plant and harvest. In agriculture, artificial intelligence can be used to monitor weather, gather information on soil health, and suggest fertilizer and pesticide applications.

Farm management software helps farmers make better decisions at every step of the crop cultivation process, increasing both yield and profitability.

Savings

Farmers are always looking to increase farm productivity. Precision farming, when paired with AI, can help farmers produce more crops using less resources.

AI in farming maximizes yields while decreasing costs by combining the best data management techniques, variable rate technology, and soil management techniques.

Farmers can determine whether regions require pesticide treatment, fertilization, or irrigation by using real-time crop insights from AI applications in agriculture.

In addition to increasing food output, innovative agricultural techniques like vertical agriculture can use fewer resources. leading to significant cost savings, improved harvest quality, increased earnings, and a decrease in the usage of pesticides.

Artificial Intelligence Applications in Agriculture

According to MarketsandMarkets, the AI in agriculture market is projected to grow from USD 1.7 billion in 2023 to USD 4.7 billion by 2028.

Traditional farming involves a number of manual processes; implementing AI models can have many benefits in this regard. An intelligent agriculture system can facilitate many tasks by complementing already-adopted technologies.

AI can gather and process big data, while determining and initiating the best course of action. Here are some common use cases for AI in agriculture:

Improving automated irrigation systems

Autonomous crop management is made possible by AI systems. Algorithms can determine how much water to provide crops in real time when paired with IoT (Internet of Things) sensors that track soil moisture levels and meteorological conditions.

Water conservation and sustainable agricultural methods are the goals of an autonomous crop irrigation system.

By using real-time data to automatically change temperature, humidity, and light levels, artificial intelligence (AI) in smart greenhouses maximizes plant development.

Detecting disease and pests

Computer vision can identify pests or illnesses in addition to crop growth and soil quality. In agriculture projects, AI is used to scan photos for insects, mold, rot, and other crop health hazards.

This, when combined with alert systems, enables farmers to take prompt action to eradicate pests or isolate crops to stop the spread of disease.

Apple black rot may be detected with over 90% accuracy using AI technologies in agriculture. With the same level of precision, it can also recognize insects such as flies, bees, moths, etc.

To get the required size of the training data set to train the algorithm with, researchers had to first gather pictures of these insects.

Monitoring of crops and soil

The health and development of crops can be significantly impacted by an improper nutrient mix in the soil. AI’s ability to recognize these nutrients and assess how they affect crop productivity enables farmers to quickly make the required corrections.

Computer vision models can monitor soil conditions to collect precise data required to battle agricultural diseases, whereas human observation is restricted in its accuracy.

The health of the crops is then assessed, yields are forecasted, and any specific problems are noted using this plant science data. Through sensors that identify their growing conditions, plants initiate AI systems that cause autonomous environmental alterations.

Monitoring livestock health

Although it would appear simpler to identify health issues in cattle than in crops, it might really be more difficult. Fortunately, agricultural AI can assist with this.

For instance, a business named CattleEye has created a system that remotely monitors the health of cattle using cameras, drones, and computer vision. It recognizes activities like birthing and detects abnormal cattle behavior.

CattleEye provides useful insights by utilizing AI and ML technologies to assess the effects of environmental factors and food on animals. With this information, farmers may enhance the health of their livestock and boost milk production.

Applying pesticides intelligently

Farmers are already well aware that there is an opportunity to optimize the use of pesticides. Unfortunately, there are significant drawbacks to both automated and manual application processes.

Although it may be labor-intensive and slow, manually applying pesticides allows for greater precision in addressing particular regions. Although automated pesticide spraying is faster and requires less work, it frequently lacks accuracy, which can contaminate the environment.

Drones with AI capabilities combine the finest features of each strategy without sacrificing any of its disadvantages.

The amount of insecticide that should be sprayed on each area is determined by drones using computer vision. Even while this technology is still in its infancy, it is getting increasingly accurate.

Automatic harvesting and weeding

Computer vision may be used to identify invasive plant species and weeds, much as it can identify diseases and pests.

Computer vision uses the size, shape, and color of leaves in conjunction with machine learning to differentiate crops from weeds. Robots that perform robotic process automation (RPA) activities, like autonomous weeding, can be programmed using such systems. 

Indeed, there has already been successful employment of such a robot. As these technologies become more widely available, intelligent bots may eventually perform both crop harvesting and weeding.

Monitoring

An essential component of farm management is security. Because it’s difficult for farmers to keep an eye on their fields all day, farms are frequently the target of burglaries.

Another danger comes from animals, such as foxes sneaking into the chicken coop or a farmer’s own livestock destroying crops or machinery.

Computer vision and machine learning, when paired with video surveillance systems, may detect security breaches in real time. Certain systems are even sufficiently sophisticated to differentiate between authorized personnel and unapproved guests.