• Tue. Sep 12th, 2023


Automation and innovation

Is the Agricultural Sector a Fertile Area for Artificial Intelligence?

ByElliot Ernser

Nov 26, 2021

Artificial intelligence has quietly crept into the life and everyday life of the modern world. And it already occupies an important place in it. Few people realize when it managed to turn into a commonplace phenomenon. Now the role of artificial intelligence in business is huge.

Artificial intelligence (artificial intelligence) analyzes databases, offers consumers advertising, tracks sales and predicts the future. The role of machine learning is also growing. It is used in text analysis, image analysis, and personalization. IT technologies are being actively used in the agro-industrial complex.

Agribusiness in all countries is considered to be rather conservative in the introduction of high technologies. However, agriculture has started to use modern technologies, so there is hardly any sense to doubt the need for IT. In a few years, agricultural producers will wonder how they lived without artificial intelligence.

BI Intelligence Research predicts that global spending on smart agricultural technologies and systems including artificial intelligence and machine learning will triple by 2025, reaching $15.3 billion.

Artificial intelligence, machine learning and Internet technologies that provide real-time data to algorithms are significantly improving farm efficiency, crop yields and reducing food production costs. According to UN analysis, by 2050, the world’s population will increase by an additional 2 billion people. This requires a 60% increase in food production. Artificial intelligence and machine learning show just the potential to help achieve the expected food needs in 20-30 years.

Imagine that in a large agricultural holding of several tens of thousands of hectares, there are at least 40 major processes that need to be monitored, improved and controlled simultaneously. Understanding how weather, seasonal precipitation, bird and insect migration, fertilizer use for different crops, planting cycles, and irrigation cycles affect crop yields is an ideal task for machine learning. How successful a crop can be financially depends more than ever on a variety of great data. That’s why farmers, cooperatives and agricultural development companies are doubling down on the use of data-driven measures. And they’re also scaling up the use of artificial intelligence and machine learning to improve agricultural yields and quality.

So what does agribusiness AI have to offer? It turns out a lot.

Surveillance systems

AI-based video surveillance systems are already being used to monitor the field in real time. This helps to detect animal or human misbehavior and sends an alert immediately. Artificial intelligence and machine learning in this case reduce the risks of domestic and wild animals accidentally destroying crops. These smart technologies report unwanted guests and the possibility of robbery, for example, on a remote farm.

Given the rapid development of video analytics, which is based on AI and MN algorithms, everyone involved in agriculture can protect their fields and building perimeters. AI video surveillance systems are easily applicable for both large agribusinesses and individual small farms. Experts say that very soon, AI-based surveillance systems will be programmed to distinguish humans from cars.

Fields and drones

AI and MN improve yield predictions with real-time sensor data and visual analytics data from drones. The amount of data being collected by smart sensors and drones that stream real-time video is giving agricultural experts entirely new sets of information that have never been accessed before. Moisture, fertilizer and soil nutrient sensor data can now be combined to analyze the growth dynamics of each crop over time. MN is the ideal technology for combining powerful sets of information and providing recommendations to optimize yields. Drones have proven to be a reliable platform for collecting important data for agriculturalists. AI, MN, ground sensors, infrared imagery and real-time video analytics are all giving farmers new insights into how crop health and yields can be improved.

Now the UN, international agencies and major agricultural enterprises are using information from drones to improve pest management. Using infrared camera data from drones combined with sensors in the fields that monitor plant health levels, agribusinesses that utilize artificial intelligence can predict and detect pest infestations even before they happen.

Yield maps

Yield mapping is a new agricultural method. It relies on supervised machine learning algorithms to find patterns. Large datasets and their real-time insights are invaluable information for crop planning. In this way, it is possible to know the potential yield of fields before the growing season even begins. Using a combination of machine learning techniques to analyze 3D maps and drone data from soil color sensors, agriculture experts can now predict the potential yield of the land for a particular crop. To do this, a series of flights are performed to obtain the most accurate data set possible.

Machines instead of humans

Today, there is a shortage of workers in the agribusiness sector in some regions. This factor makes intelligent tractors, agrobotics and robotics based on artificial intelligence and machine learning a viable option for many agricultural businesses that are having difficulty finding workers. Large agricultural businesses and agribusinesses that can’t find enough employees are turning to robotics to cultivate hundreds of acres of land. Programming self-propelled robotic machinery, for example, to distribute fertilizer to each row of crops helps reduce operating costs and further increase field yields. The complexity of agricultural robots is rapidly increasing day by day.

Track and trace system

One of the pressing issues of our time is to improve the logistics and traceability of agricultural supply chains. It is about removing absolutely all obstacles on the way to the market of the freshest and safest possible products. The pandemic, by the way, accelerated the increased implementation of traceability throughout the agricultural supply chain in 2020. Its implementation continues this year. A well-established and managed product traceability system helps provide greater transparency and control in the supply chain and reduce inventory shrinkage. A modern traceability system can distinguish between the destinations of shipments and the containers where shipments enter. The most advanced tracking systems rely on advanced sensors to provide more detailed information about the status of each shipment. RFID and IoT sensors are becoming more prevalent in manufacturing. Walmart has launched a pilot project to see how RFID can optimize tracking in a distribution center and increase efficiency 16 times over manual methods.

Artificial Intelligence and pesticides

One of the most common applications of AI and machine learning in modern agricultural production is the task of optimizing and correctly combining different kinds of pesticides. As well as limiting their application to only those fields that need treatment. Such a system helps reduce costs while increasing crop yields. How does it work? Using smart sensors combined with visual data streams from drones, artificial intelligence programs in agriculture can now identify the most infested areas of crop land. Using supervised machine learning algorithms, the AI then determines the optimal pesticide mix to reduce the threat of further pests spreading and infecting healthy crops.

Animals and machines

One of the fastest growing applications of artificial intelligence and machine learning in agriculture is in animal studies. Specifically, monitoring the health of livestock, including vital signs, daily activity levels, and the amount of food consumed. Understanding how each livestock species responds to diet and housing conditions is critical to understanding how to better maintain and treat animals in the long term. Machine learning and artificial intelligence are being used to understand what makes cows satisfied and happy every day.This factor is important for producing more milk.For many farms that rely on cows and livestock, this field is opening up a whole new understanding of how farms can be more profitable.

IT technology is being actively adopted in agro today.AI and MN are opening up vast opportunities for farmers around the world. The software segment is growing every year and these opportunities need to be capitalized on. Digital technology helps in improving the efficiency of the agro sector for specific farms. At the government level, it enables successful agri productivity management.

Yield prediction models, real-time data analysis, video surveillance systems, artificial intelligence for irrigation and weed control are the key trends in the agri market.

All of these things are working and producing results today.