Once traditional, Brazilian agribusiness is undergoing a very rapid technological revolution. At the heart of this transformation, AI in agriculture emerges as a key player, marking a significant shift in farming practices.
With the advancement of AI, agribusiness companies are heavily investing – either alone or in partnership with research institutes – in developing solutions to automate processes and daily activities on farms, fields, and other segments.
Do you know the most interesting applications of AI in agriculture?
We invited Jayme Barbedo, a researcher and supervisor of the Scientific Computing, Information Engineering, and Automation Research Group at Embrapa Agricultura Digital, to discuss the topic. Good reading!
What is the importance of Artificial Intelligence in agriculture?
AI is becoming increasingly integrated into daily life and various sectors, including agriculture.
Farms generate a lot of information every day. Recording, entering data into spreadsheets, and analyzing it is a task that requires time, attention, and expertise. With AI, this is starting to change.
New digital tools and algorithms, trained and fed with vast amounts of data, are making field management and operations more efficient, precise, and sustainable.
Jayme Barbedo notes that several digital technologies are already applied in the field. “We have automatic milking and automatic weed detection in crops for targeted action. These technologies are already a reality,” he states.
Furthermore, with the development of generative AI and large language models (e.g., ChatGPT), a whole new universe of applications has opened up across all sectors, including agriculture.
Artificial intelligence (AI) innovations are reshaping animal health diagnostics
However, Embrapa's researcher highlights that these technologies still need to evolve. “We must not lose sight that they are in the hands of companies that may introduce biases in the responses to serve their own interests.”
Therefore, even though there are very powerful technological options, it's important for countries like Brazil to invest in developing national technologies that use reliable sources and meet the true interests of their populations.
6 main applications of AI in agriculture
With the ability to process large volumes of data, learn complex patterns, and make decisions, AI can radically transform how agricultural activities are performed.
Consequently, there are many applications of AI in agribusiness. With extensive experience in the field, Jayme Barbedo highlights the six most interesting ones. Check it out!
1 - Advanced geospatial data analysis
Increasing numbers of satellites are generating high-resolution spatial, temporal, and spectral data. However, analyzing these data and extracting useful information is still very manual.
According to the Embrapa researcher, this is attracting many research groups. “They are developing techniques for automatic and objective analysis of this data,” he says.
In agriculture, practical applications include:
Detecting stresses in crops
Such measures help speed up decision-making and take action to address problems.
“Given the interest in this subject, I believe there will be a proliferation of such tools in the coming years,” adds Barbedo.
2 - Real-Time Crop Monitoring
Besides satellite monitoring, various sensors and techniques based on the data they collect are being developed for crop monitoring.
The generated data includes images of the crops, weather variables, pest information from smart traps, soil sensing, etc.
According to Barbedo, monitoring can be done in two ways:
Static, with sensors installed at strategic points on the property
Dynamic, using drones, agricultural machines, and soil robots
There are various technologies aimed at crop monitoring, some already used in practice, but many still need further development to handle the huge challenges posed by the agricultural environment.
3 - Automation of repetitive tasks
Automation of repetitive tasks has been a major driver of industrial development over the last two centuries. This automation has been growing in both scope and sophistication.
In agriculture, there are many examples of sophisticated machinery capable of planting and harvesting autonomously, especially for grains.
However, in areas like fruit farming, where careful harvesting is needed to avoid damaging the product, Barbedo notes that this has been a challenging problem to solve.
“There are robots capable of harvesting fruits without causing damage, but they still have several limitations that are gradually being overcome in research,” states the researcher.
Other repetitive activities such as transportation, packaging, and sorting of products are also being automated, and with the rapid development of AI-based technologies, this trend is expected to intensify in the coming years.
4 - Optimization of irrigation and fertilization
Irrigation and fertilization are still often done subjectively and based on a set of information that does not always reflect the real needs of the crops.
With the advancement and reduction in cost of field sensors, farmers now have access to a vast amount of data that, if properly utilized, can lead to near-ideal management.
However, this is not a trivial task and can be challenging even with models developed through careful scientific research. “This is where AI tends to be more useful,” says Barbedo.
Well-trained AI models with high-quality data can implicitly learn all patterns related to the problem being addressed, providing responses very close to the ideal without the need to model each parameter explicitly.
In recent years, there has been a rapid proliferation of such technologies, but not all producers are willing to adopt them. “Training and convincing are still necessary,” suggests the researcher.
5 - More precise application of inputs
As mentioned earlier, agricultural machines equipped with devices to detect and eliminate weeds automatically and locally are already available.
The goal is to develop similar technologies for diseases, pests, and nutrition, taking AI in agriculture to a new level.
However, according to the Embrapa researcher, the major challenge today is generating enough data to represent the variety of conditions encountered in practice. Once this issue is resolved, advancements will be rapid.
“Once this problem is solved, new technologies offering sufficient robustness to handle real-world crops should emerge quickly.”
6 - Harvest and weather forecasting
Crop forecasting models based on weather conditions have existed for a long time and contribute to agriculture in various ways.
However, with the development of AI in agriculture, more variables are being incorporated into these models, including satellite images and other information previously inaccessible due to the limitations of conventional models.
“With these advances, the accuracy of crop forecasting models is increasing rapidly, and this is a trend that is expected to continue in the coming years,” affirms Jayme Barbedo.
Given these numerous applications, it is clear that the use of AI in agriculture has the power to push agribusiness beyond the limits of what is currently possible.
As we explore and implement future AI trends, we have the opportunity to create a more efficient, precise, and sustainable agricultural sector.
