The search for expanding the supply and quality of food in the world is recurrent year after year, motivated by the additional population predicted for the coming decades, with signs of an increase of two billion over the next 30 years. In the context of agriculture, Brazil is currently responsible for cultivating more than 64 million hectares, including cereals, legumes and oilseeds, an area 10% larger than in 2015 and 26,6% larger than in 2010.
However, the limits observed for food production arise from the restriction of expanding land use, considering the process sustainable, economically viable and socially ethical. Furthermore, we can mention the increase in investment and farming costs, which makes the sector more competitive, and also the demand from consumers regarding the physical and sanitary quality of the food produced.
Therefore, using effective technological tools can facilitate the handling and management of resources, aiming to assist in decision making, in order to guarantee greater productivity and profitability for the farmer. In this context, we closely monitor major launches and trends in mechanization and new technologies available, aimed at strategies with great perspectives and technological development. In some countries and certain regions of Brazil, these processes have been implemented, making mechanization an important ally for farmers who seek to combine quality and high operational efficiency.
This direction is based on the trajectory of agriculture, transcribed from the perspective of industrial evolution, briefly presented in Figure 1.
The development of global agricultural production occurred thanks to the use of four essential technologies, namely: genetics in seeds, agricultural pesticides, mineral fertilizers and mechanization. Thus, all the others developed so far aim at detailed management of the activity and/or increasing the efficiency of each stage of production in which these four main ones are involved.
In the current scenario of global agriculture, mechanization has been guided by the paradigm that the increase in productivity with the reduction of specific costs will come, in the vast majority of situations, due to the structural increase in equipment. However, the increase in performance solely desired in the growth of machines presents limitations imposed by agricultural systems and also by the embedded technologies themselves. Therefore, understanding these requirements in the design and use of equipment appears as a viable alternative in the pursuit of this objective.
New agricultural machinery projects are already being guided, no longer by magnitude, but rather by multitude, that is, the sharing and/or segmentation in the execution of a function, previously intended for just one, aiming to distribute information and/or workloads . This situation can be achieved, basically, through remote data sharing and the use of multiple devices that perform a planned function on a smaller scale.
To do this, we have embedded sensor networks in agricultural machines, making it possible to connect to a data system that will allow measuring a series of parameters, such as determining their behavior in operation, observing details of fuel consumption, speed of operation and even results. of all machine work. This data is collected remotely, and needs to be transmitted to a storage center, where it will be monitored and evaluated by a control base. The technology used for this process can be called telemetry, which is the transfer and use of data from one or more remote machines, allowing instant communication via computer network. The elements that make up a telemetry system can be aggregated by a set of hardware connected to sensors embedded in the machines, such as inductive, capacitive temperature, relative humidity, oil pressure and position sensors, controlled by specific software. It is therefore possible to monitor, control and measure the activities carried out by these machines or a fleet that are connected.
Furthermore, trends in the use of information technologies (IT) are diversifying the way of producing in Brazil and around the world, where we have come to recognize the machine no longer as a necessary aid tool in agricultural operations, but as a means of aiding decisions. , autonomously. In this decision process, the rural producer, who previously needed to rely on his experiences and intuitions for each decision, now relies on accurate information in real time, because when we think about sowing and harvesting operations, for example, any decision with the lowest error rate in the shortest response time can determine the exponential success factor.
Precisely for these reasons, in recent years we have managed to reach the pinnacle of mechanization in terms of terrestrial sensors, and we are on the way to achieving the same in the use of satellite tracking systems, seeking greater comfort and quality of life by reducing working hours. Machine operators have now started to promote the use of digital tools and other devices that are being inserted in this new rural environment, aiming to collect data relating to the most distinct variables that influence productivity in this very volatile environment and making it possible to predict or correct some difficulties that already exist. predicted, such as ideal soil characteristics, climate variation and pest incidence.
With the increase in the size of the machines, the insertion of on-board technologies aimed to assist the operator in visualizing, regulating or monitoring activities, through large information panels, lights and warnings inside the cabin, which required the operator's intervention to decide and carry out decision making in adjustment or regulation. Therefore, the demands of the multitude in current agriculture have evolved the conception of embedded technology to new proportions, through a learning experience in the term machine, where it itself can translate this information obtained, in the shortest possible time and make a decision, being able to have as through artificial neural networks.
This machine learning, known as machine learning, can act in the order of automatic regulation, or even decide which levels are tolerable, check whether these are being respected and adjust the machine in real time, thus immediately reducing losses. The great benefit of this is to remove the responsibility for decision-making based on the operator's opinion, and promote the use of all data collected to compose a decision structure supported by technical criteria.
In addition to the issues previously highlighted, the development of techniques, tools and machines focused on agricultural crops also stands out.
For the first, when it became necessary to plan mechanized operations, especially those that require high farm traffic, alternatives were sought to minimize these impacts caused by trafficability through techniques for using agricultural machinery and equipment. It was then that permanent controlled traffic agriculture (ATC) emerged, which delimits small “roads” of passages (tramlines) that support the load of the machines, from the implantation of the crop to its harvest. In this way, the soil suffers a reduction in volume, that is, compaction, but only in the width ranges of the wheelsets. For complete application, in some cases, in addition to using position information and managing it, it will be necessary to standardize the different machines that will travel, as it will be necessary to adopt part of the system by adjusting the gauges and/or using axis extenders. , with greater aptitude for controlled traffic tasks, operating only on strips of the soil surface, leaving the rest of the area for crop development.
Therefore, adequate control of the number and location of agricultural machinery passes will allow the consolidation of adequate soil conditions for the development of crops throughout successive harvests, allowing, in well-established systems, the redefinition of sowing density, seeking to increase productivity, also made possible by better use of agricultural pesticides and reduced fuel costs. The latter can be achieved due to the better traction performance of the machines, due to the better coefficient of adhesion of the wheelsets with the more compacted soil and lower traction demand for the tools in the soil/machine ratio, which work in the uncompacted area.
Furthermore, tasks can be carried out in less time, by increasing operational efficiency due to planning, in addition to reducing machine maintenance costs, however, it requires precise management, such as spatial monitoring, mapping and management, within a structure precise space. Operations involving sowing, harvesting, application of chemicals and fertilizers must be carried out using more precise methods, as traffic cannot be altered if errors are made in any of the operations. If these objectives are achieved, they will guarantee successful monitoring, which will considerably reduce the overlap in the application of inputs, reducing costs and increasing productivity.
To this end, connectivity will be essential in the application of these techniques, being the central technology that will allow major paradigm shifts in the mechanization sector. By onboarding systems and fleets of connected machines we can enable teams of smaller units with standardized formats to exchange data and experiences, thus ensuring a reliable network.
Furthermore, the use of simplified solutions has gained significant attention from manufacturers, aiming to reduce project complexity, facilitate adjustments at the time of use and the possibility of agility in maintenance, preventive or corrective processes.
An example of this are the electrical drives of dosers in seeders, previously controlled by equally efficient (or even superior) mechanical relations, but with high complexity in adjustments and maintenance demands. Thus, even more precise controllability in operations, durability, integration with other processes in which the machine is involved, enabling connection and control digitally and quickly, in addition to ergonomic gains for operators, such as noise reduction, have made the manufacturers opt for less complex principles. However, to fully include all this potential, it will be necessary to rethink the opportunities that mechanized agricultural processes provide and create new equipment architectures, in addition to replacing some hydraulic and/or mechanical units.
Complete electrification is a viable alternative for reducing the complexity of projects, but it will only be a reality in a large-scale solution, as a single source of energy for a system, when more efficient batteries are developed. For this scenario to generate paradigm shifts, at this point the electrification of projects must seek to simplify and facilitate technological use, such as the driving line, maintenance, control and, even, the architecture of machines based on the inveterate objective manufacturers, that is, mass reduction.
Thus, it is clear that technological development and the overcoming of some paradigms, although it is possible to list some aspects, will be achieved through the intrinsic relationship between them. In this way, some can be recognized as techniques that permeate evolutionary aspects, with the processes of using intelligence and automated tools representing these perspectives.
Without a doubt, the natural tendency towards the possibility of “learning” from machines will allow more effective control of systems and processes, which will be improved as experiences and operations are carried out, through the feedback of information and updates as a form of learning. automatic, forming artificial neural networks.
The capacity of future self-learning machines will be possible through control algorithms that allow learning in a virtual way, where any type of behavior and information is used. Resulting in better immediate decisions, thus ensuring adequate soil management, execution of operations at appropriate times based on metric strategies, reduction of operator workload and efficient use of resources.
A promising future awaits us, but there are still some gaps that only with research, standardization and certification of equipment, in addition to the development of people, will we be able to achieve. Unquestionably, for there to be autonomy when including complete strategic intelligence in the solution, without the need for direct intervention from a professional, qualified labor is increasingly necessary for the correct and effective adaptation of assertive solutions for any agricultural process. mechanized in each distinct situation found on rural properties. In this way, the human role will remain the main one, which permeates the adoption of all technological solutions that seek to overcome the paradigms of current mechanization.
Vitor da Silva Pinheiro Santos, Tiago Rodrigo Francetto, Rafael Sobroza Becker, Airton dos Santos Alonço, UFSM/Laserg
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