Agriculture is one of the oldest anthropogenic activities in the world, it always goes in parallel with the evolution of humanity. It can be called that, through the development of agricultural production since our ancestors, efficiency has been prioritized in the different crop management practices so that they are sustainable with the environment and in the face of climate change, especially in recent decades. (EFE-Green, 2014).
That's right, our ancestors designed metrics and planting systems to guarantee sustainable production in each crop cycle. Despite this, the takeoff of agricultural progress since the industrial revolution has mechanized some of these processes, partially replacing the labor of people in large areas of crops around the world.
However, during the 20th century, despite such advances, the concept of sustainability was not taken into account until the 1970s with the agroecology revolution. The problems that began their discussion by Carson (1962) in his book –silent spring– As the consequences of pollutants putting people's health and infrastructure at risk and the high fuel costs in agricultural mechanization, have been a challenge ever since, therefore, the concern remains:
How can we integrate this concept of sustainability and optimize our harvests today, even more so in larger-scale crops? Could it be that we lost that awareness of making metrics as perhaps formerly great indigenous cultures in Latin America applied them apparently as an indispensable strategy?
If we had a crystal ball since we planted our crops, we could establish how much the profitability could increase with respect to the investment. But unfortunately this is not so. Undoubtedly, the greatest concentration of attention from us as producers is at the time of establishing our crops, but without a doubt, the information provided on our variety or strain is not enough to be able to predict with moderate accuracy or close to the yield that we would like each year. get. Observing carefully, not only at the beginning of the establishment of the crop, should also be a conscious exercise during all production cycles.
The observation extracts the basic information of the critical points such as the phenological stages of the crop where it could obtain low harvest yields. Observation makes it possible to bring awareness that a plant is a living being, and that, like any organism, it is influenced by climatic factors and changes, nutrition, irrigation, and susceptibility to certain diseases.
Therefore, in these substantial technological advances, it brings the need to integrate them as our allies in this -"observation consciousness"-, for a clean and efficient production manifesting itself in obtaining high yields and fruit quality within the commercial guidelines.
As mentioned at the beginning, the agriculture is always synchronizing with the evolution of humanity. Technology, as a reflection of this continuous evolution, is increasing its adoption in activities related to agriculture. Among the current technologies in agriculture 5.0, Artificial Intelligence stands out, which unlike 4.0, is focused on higher analytical maturity levels such as prediction (What will happen?) and prescriptive analysis (What do I have to do?).
McCarthy defined Artificial Intelligence as “...the science and ingenuity of making intelligent machines, especially intelligent computer programs...» and within it there is a field called Machine Learning which gives computers the ability to learn without being explicitly programmed (Samuel, 1959).
Currently, AI/ML is mainly used in agriculture to:
Its use and implementation allows the farmer to take proactive and preventive measures thanks to the findings of critical points in the integrated management of their agricultural practices according to the data collected. The success is such that a market for the use of these technologies is estimated at USD$1.5 billion by 2025 (Gupta 2019).
Just as water is essential for crops to grow and tolerate abiotic stress, for Agriculture 5.0 data is needed to feed Artificial Intelligence models and algorithms so that they are capable of making highly accurate predictions and reliably alerting us of any deviations that may occur. affect crops. For this, an internal cultural change must be led to lead to a data-based agricultural management strategy or specifically in this case: data-based smart agriculture. This strategy basically consists of the systematic capture of data related to each of the practices or management in our crops, their processing and the corresponding decision-making based on the conclusions obtained. Data can be generated from sensors, satellites, phones, cameras, drones, and scientific instruments. The most important thing is that there is a process of systematic data capture, to have centralized information ordered and systematized for each agricultural activity, with them we will greatly improve the quality of our forecasts and make investment in technology profitable in the long term.
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