The number of artificial intelligence applications in agribusiness increases every year, proving that AI is precisely the kind of technology that enhances the results of companies operating in this field, adding much more efficiency, productivity, automation, and safety to agrarian operational routines.

If you imagined robots working in the field, take it easy. We are not talking about that just yet! However, the concept of artificial intelligence in agribusiness is similar, as it uses machines to perform faster and more precise processes than the human mind could execute. 

In this post, we highlight some of the main AI initiatives in the field, demonstrating how these solutions boost results and the quality of rural operations, promoting innovation in modern agriculture. Check it out!

Artificial Intelligence in Agribusiness

Many associate artificial intelligence only with robots replacing human work. This is a more tangible representation of the technology, but it is not the only one possible, as the concept is based on machines that function in a similar way to human thinking.

Put simply, AI is a solution that brings together different types of technological resources, such as algorithms, computer language, learning systems, logic, science, and mathematical formulas that, together, simulate certain human capabilities, such as environmental perception, reasoning, and analysis of factors for decision-making.

In the agribusiness sector, in particular, there is a need to operate in a sustainable and scalable manner to meet the high demand characteristic of rural activities.

With the use of Artificial Intelligence in this sector, it is possible to achieve significant and efficient results. Once operational processes are optimized and there is access to important business data, managers can think and act in a more tactical and strategic way.

How is AI Revolutionizing Agribusiness?

Just like in all areas of industry and the economy, digital transformation has arrived for the agribusiness universe. According to a survey by the Brazilian Commission of Precision Agriculture (CBAP), 67% of rural properties working with agriculture in Brazil have already adopted some type of technology in their processes.

One of these innovations is Artificial Intelligence, which connects and streamlines processes. After all, it can provide weather forecasts, automate manually performed activities, identify low and high productivity zones, manage fertility, pests, and soil compaction, analyze the origin and processing of animals, guide cuts, sort in slaughterhouses, distribute raw materials for cold cuts and manufactured goods, among others.

Rural operations are among the most delicate and technical in the productive sector, as they require the exchange of many skills, especially in logistics, agronomy, and management. Given this reality, we present the AI solutions that contribute the most to productivity in the field:

Precision Agriculture

Precision agriculture is a way of managing agribusiness processes that use machines, devices, and software to help achieve better productivity. In this sense, the entire production process is considered, from soil analysis, fertilization, planting, and harvesting, to the processing and commercialization of products. 

The differentiator of precision agriculture is that as much data as possible is collected through various techniques, and the best interpretation method is applied to base decision-making on this information. One example is the optimization of fertilizer application. 

With the scientific development of pest analysis, it is possible to know what threats are active at each stage of the crop, and, in this way, apply the correct input in the correct quantity. This helps the producer save money and use a healthier approach, exposed to fewer chemical or biological products. 

Use of Drones

The use of drones for tasks involving inspection and monitoring is no longer new to the market, as many companies rely on this tool to control their operations, as frequently occurs in the logistics sector. Now, these flying machines are also being used in the field to transmit images from all points of the property.

Thus, drones can be used to monitor the plantation by zoning and reduce the large-scale application of defensive substances. If a pest is identified in one area of the crop, the agrochemical only needs to be applied to the region requiring treatment, which prevents the risk from spreading to the rest of the plantation.

Monitoring

Even though drones are used in monitoring and even surveillance missions, it is also worth highlighting IoT devices, which are continuously diagnosing the quality of a series of soil and environment characteristics.

In agribusiness, Machine Learning is being used to monitor crops, predict harvests, detect diseases in plantations, and optimize the use of inputs. In addition, algorithms are contributing to the analysis of climate data, helping producers make proactive decisions in the face of adverse conditions.

Computer Vision

Considered one of the most applied models of AI, computer vision has several uses in the agricultural sector, such as herd tracking and feed management. It is based on machines that process real-world images using intelligence programmed into a computer. Therefore, it works on identifying and interpreting information that is not always visible to the human eye.

A common example is the use of computer systems to classify thermal comfort in dairy cattle. Through images indicating the temperature of specific parts of the animal's body, such as the forehead, rib, eye region, and flank, a temperature histogram can be prepared, with the maximum, average, and minimum of each area of the cows. This allows you to keep them always comfortable, healthy, and productive.

Another example is the analysis of images captured by drones and satellites. With advanced machine learning algorithms, these images can be analyzed to identify pests, diseases, water stress, and other conditions capable of affecting crop productivity.

This analysis can also generate detailed maps of the cultivated area. Mapping allows for counting the number of plants in a given area, distinguishing between the different types of crops present. In this way, it is possible to identify and create weed control strategies.

Autonomous Vehicles

Although they are still being tested, prototypes of autonomous tractors equipped with AI are already seen as a highly innovative solution for agribusiness. This is because the vehicle is developed to map out work routes in an area where it is raining and move to another one that is dry, and can be monitored remotely by employees.

Another innovation is intelligent harvesters that can be programmed according to the harvest needs of a given raw material. In addition, we must mention vehicles that map the property and help locate areas of deforestation or with excessive pesticide application that need to be restored.

Genetic Improvement

When deciding what to grow, producers can choose from a wide variety of seeds that may be more or less productive in the face of drought, rain, or pests. 

With the help of Big Data, precise reports on the indexes of the grains planted in the previous harvest are obtained, thus helping the manager select the seeds most appropriate for the property's conditions.

Another extremely important technological resource is the combination of AI with submillimeter imaging, which detects possible anomalies in the crop through thematic maps. This procedure guides the team regarding nutritional deficiency of the seeds and the presence of weeds, giving them insights to prevent problems and treat the plantation at a granular level, a practice that eliminates the risk of lost harvests due to disease infestation.

Sensors

The installation of sensors is a viable and efficient alternative for collecting information on daily operations, such as equipment activation, hydraulic pressure, machine speed, and fuel level.

Thanks to Artificial Intelligence, sensors collect data and send it to a system capable of projecting scenarios and delivering real-time recommendations, allowing the manager to act in advance and handle unfavorable situations even before they happen, eliminating or reducing negative consequences for their activity.

Sensors can also indicate when equipment requires maintenance, avoiding accidents, and even optimized routes to transport production, to prevent waste of fuel and time.

In addition, sensors are capable of analyzing animal breath, identifying abnormalities and signaling issues in their diet composition. Some sensors infer bacterial growth and others show how much each animal weighs without using scales, making it possible to calculate and predict the profitability of each individual animal.

Revolutionizing Agribusiness with the AWS Cloud

When speaking of adopting AI in field activities, expectations are that tools, equipment, and applications deliver increasingly efficient results. However, some unforeseen events, such as low quality of wireless connection in rural areas and climate shifts affecting the environment, can hinder operations.

To support mobility for accessing solutions in the field, Amazon Web Services offers AWS IoT Core. The service ensures communication between the cloud and mobile devices in a simple, secure, and scalable way. With AWS IoT Core, developers can focus on the application without spending much time on security routines or scalability. In addition, AWS IoT Core allows connecting billions of IoT devices and routing trillions of messages to AWS services without managing infrastructure.

Furthermore, AWS offers a wide range of services aimed at AI, providing a robust platform to satisfy unique needs, from building and training machine learning models with SageMaker Studio to implementing advanced AI solutions with Amazon Bedrock and Amazon Q.

If you want to know more and discover how to experience all the benefits AWS offers, talk to the experts at CodeBit now. 

CodeBit is an AWS Advanced Tier Services partner and can help with the best solutions for your company!

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