Check out three solutions for predictive action in agribusiness.

Being prepared for what will happen in the future is one of the best ways to avoid problems, especially when it comes to agribusiness. Due to the impact that external elements have on agricultural operations, rural producers have sought to plan their activities in advance for centuries, considering several signs that relate to the weather forecast. No wonder meteorology has evolved significantly over the years, bringing countless gains to the field.

However, predictability in agribusiness today is no longer restricted to this issue alone. Although the analysis of climatic conditions and phenomena remains extremely important for entrepreneurs and rural producers, technological advances and increasing access to data have allowed the emergence of several other prediction-oriented solutions.

"There is much to be expected from technology in this direction for the coming years. With sensors, integration technologies and intelligent data analysis, we will achieve increasingly predictability, contributing to efficient planning and execution of agricultural operations," explains Bernardo de Castro, President of Hexagon's Agriculture division, which develops and provides technological solutions for rural and forestry areas.

Check out three ways technology is enabling predictive actions and collaborating with agribusiness:

Machine monitoring

The automation of the agricultural machinery of plants and farms involves the installation of a series of sensors that record the activities of this equipment second by second, collecting hundreds of information on the operations performed in real time.

Through technologies such as Big Data and Artificial Intelligence, this data is cross-referenced and generates scenario projections and anticipation of problems. A very common example is the assessment of the risk of machine failure based on constant monitoring of the situation of such equipment. Thus, it is possible to trigger preventive maintenance actions in the fleet, based on machine behaviour data.

"When we do not have this control, it may happen that one of the harvesters breaks in the middle of an agricultural process. The tractor and trailer that accompany it will also stop, as will its operators, who will not be able to work until the mechanic arrives - which may take a while. All this time and resource is wasted and ends up generating a cascade effect in the following days," says Bernardo.

In Hexagon's Agriculture division, products such as HxGN AgrOn Machine Monitoring use sensors to measure variables of agricultural machines and analyse their data. In case maintenance is required, just schedule the activity in advance, making a shorter stop or even allocating reserve machines on the scheduled date.

In addition to avoiding this unplanned emergency situation, preventive actions prevent the increase of damage to machinery, expanding its performance and ensuring maximum use of the service life of the equipment.

Fighting diseases and pests

Just as it is possible to monitor machines, it is also important to look at the crop as a whole. Today, with the help of technologies such as sensors, drones and satellite images, for example, this observation helps in the automatic identification of points of attention.

The analysis is also performed with innovations such as AI, which allows the combination of images and information to obtain diagnoses in real time. "With the data, it is possible to identify signs of diseases and pest infestations in plants, and even simpler problems, such as nutritional deficiency," explains the president of Hexagon's Agriculture division.

This care allows early action to combat these threats, preventing the problem from spreading throughout the plantation or impairing the productivity of that crop.

Allocation of hall-out

Another example of how technology can provide predictability to the field is the dynamic identification of resource allocation opportunities, which allows logistical optimisation in a series of stages of agricultural production, such as transhipment, loading and transportation.

For sugarcane harvesting operations, for example, systems help intelligently allocate loading (haul-outs) and transportation (trucks) resources, according to information collected in real time and forecast calculations of new resource demands.

A haul-out can thereby be allocated automatically and in advance to a future point where a particular harvester will demand it. Similarly, trucks are automatically dispatched to harvesting fronts according to the status and forecast of the production pace of each front.

"This type of product, such as the HxGN AgrOn Haul-Out Dynamic Allocation, synchronises the cutting rhythm of the harvesters with the movement of the overflows, indicating the ideal time for the displacement of these tractors," explains Bernardo de Castro. "This prevents the harvester from interrupting its work and further reduces the waiting time for a new haul-out to continue the operation," he adds.