Digital agriculture gains ground with drones, AI, and connected machines.

Although telematics is not a new technology, it still presents numerous challenges for farmers who choose to invest in and benefit from the system. Currently, the largest landowners are more willing to make this investment, while smaller producers tend to be a little slower to adopt this technology, at least until they understand its benefits.

With the continuous development of technology, making it an increasingly popular and accessible tool, the agricultural sector is rapidly equipping itself with the growth of more companies seeking to offer solutions in this field, such as John Deere and Trimble, which compete in this market.

Challenges such as increasing signal coverage area, upload speeds, and the level of security needed to protect information must be overcome so that connectivity allows all the technology that has been developed to be used to its full potential.

Digital technologies not only increase efficiency in agriculture and livestock farming, enabling sustainable environmental and animal management, but are also rapidly becoming a requirement for producers to remain competitive in the market.

Digital farming

According to a study by the United Nations Global Compact and PA Consulting Group, digital agriculture is the use of new and advanced technologies, integrated into a single system, to enable farmers and other stakeholders within the agricultural value chain to improve food production.

Most farmers today make decisions such as the amount of fertilizer to apply based on a combination of rough measurements, experience, and recommendations. Once a course of action is decided upon, it is implemented, but the results are not typically seen until harvest time.

In contrast, a digital agriculture system gathers data more frequently and accurately, often combined with external sources (such as weather information). The resulting combined data is analyzed and interpreted so that the farmer can make more informed and appropriate decisions.

Digital agriculture has the potential to transform how we produce the world's food, but the approach is still very new and costs are high. The technologies used include sensors, communication networks, unmanned aerial systems, artificial intelligence, robotics, and other advanced machinery, and often rely on the principles of the Internet of Things. This integrated system offers new insights that improve the ability to make decisions and subsequently implement them. The estimated digital agriculture market size in 2021 was US$15 billion. 

Drones 

Our biggest obstacle is the vast expanse of cultivated land and the low efficiency in monitoring it. But drones – the market offers a wide variety of options at a much more affordable cost – can be used to assist in this regard at all stages of the production system, from soil analysis and preparation, planting, spraying and harvesting, at any time of day.

After planting, the farmer's main objective is to ensure the health of the crop, and for this, monitoring is essential. One of the most recent developments helps to assess the "health" of the plant, as it is able to locate outbreaks of pests and diseases in the crop through the use of "visible light" and infrared sensors that make it possible to identify the different reflectance of plants in relation to "green light" and NIR in comparison with others. Experts comment that spraying via drones can be four to five times faster than with traditional machines (self-propelled sprayers).

With all its applications, it's safe to say that this technology is already taking agriculture to a new level of high technology, allowing decisions to be made in real time. That said, one of the main concerns is not the drone's flight speed or its flexibility, but the type and quality of data it can provide. 

Finally, according to the latest study by PwC on the commercial applications of this technology, the global market for services and businesses using drones is valued at over US$127 billion (including current businesses and activities that may be replaced in the very near future), US$32.4 billion in Agriculture (soil and drainage analysis, crop monitoring) and US$13.0 billion in Transportation (goods delivery).

autonomous vehicles

In the 80s, with the beginning of Precision Agriculture, the concept of autonomous tractors began to be addressed with the purpose of increasing the efficiency of the production system and reducing costs in the field. Unfortunately, these vehicles are not yet commercially approved in many countries, which does not prevent manufacturers from continuing to develop these new technologies. 

Many of the sensors and control systems incorporated into autonomous agricultural machinery and equipment are already used in self-driving cars. A large portion of tractors sold in the US, Europe, and parts of Brazil already include GPS-based guidance systems, giving farmers the opportunity to put this technology to work for them.  

The precision and quality of planting, sensors that collect data from the soil, plants, and weather conditions, and improved nighttime work are some of the many benefits that, together, reduce the workload and effort of operators, assisting in the management and control of various tasks within the agricultural production system. 

The technologies to enable fully autonomous tractors are already available, but what are the obstacles to the commercial launch of these solutions? One of the main concerns is the level of trust in this solution, as there are currently no defined standards or legislation to protect the user against potential accidents. For now, autonomous vehicles still require humans to monitor their speed and performance. Soon, innovations in agricultural equipment will allow for complete remote control of these operations, resulting in a significant increase in productivity for producers in large-scale agricultural operations.

Machine learning

Machine learning for agriculture uses algorithms to analyze data, learning to make determinations without human intervention. These algorithms are fed decades of field data, climatological information, productivity data, etc. – far more than any human can analyze – and from this, they create a probability model.  

Rapid diagnosis of pests and diseases can be the key to successful control. Traditionally, disease identification was done visually, an inefficient process with a high chance of error. With the use of new software, computers, and smartphones, it is now possible to diagnose and classify pathologies using databases, assessing the level of infestation and even recommending appropriate management practices.

Since one of the fundamental goals of modern agriculture is the development of inputs that reduce diseases and pests, machine learning is the technology that can make more precise improvements in this process, helping to create, for example, more efficient, adaptable, and productive seeds.

The possibilities for improvements in machine learning are endless. Machine learning is increasingly proving its theories on a larger scale, making predictions in real time and with a greater degree of accuracy. 

It is already possible to develop other ways of using nutrients, conserving water, and using energy more efficiently.

Augmented Reality

As the population grows and new ways of consuming food emerge, agricultural tasks become increasingly fundamental and challenging. Augmented Reality (AR) can help farmers in various ways: crop inspection, finding pests and diseases, including their species – offering appropriate ways to deal with each one.

Imagine for a moment pest control – each type of insect “should” (in quotes) have a specific control method, as many of them are essential for the well-being of the ecosystem. With the use of AR (Augmented Reproductive Technology), it is possible to treat your farm differently, using appropriate protocols for each situation, thus improving your entire production system, which at the end of the cycle is measured by the quality/quantity of your harvest.

For agriculture, this is a very new topic and needs to be explored. There are already companies in Brazil, especially FLEX Interativa, a leader in this market, which will soon expand its operations to applications in agribusiness. The possibilities for application are numerous; let's keep an eye on it.

The time is not far from our reality when these technologies will take over the management of the entire agricultural operation, constantly analyzing crops, making decisions about applications, harvesting, etc., bringing about a significant change in the way we do agriculture.

*Per Marco Lorenzo Cunali Ripoli, from Bioenergy Consulting