What we perceive of the world is revealed to us through interference. Let me explain. Light hits a leaf and we perceive it in its shape, color and also its morphology. If the light is not there, we do not see it. So here it is the interference, the relationship between leaf and light ray, that reveals the leaf to us. From these interferences we are also able to deduce other situations. For example, when we notice a lower intensity of green on the leaves of plants of the same crop we think of a deficiency, for example of nitrogen. This is a good assumption. Other information allows us to confirm or modify this first impression; that is, we give meaning to the original information. The “meaning” of what we have observed will allow us to decide the agronomic action to be applied. Information such as: How extensive is the deficiency? Do I detect signs of possible phytosanitary problems? When did I last fertilize? With what dose? Does the crop species require a lot of nitrogen? What is the production potential of the field? Are the other productive factors available in the right quantities? …are the ones that help us interpret the mere color information.
Processing all this information requires “experience.” Understanding the logical steps and knowledge we put into the analysis helps us understand how precision agriculture works and, in particular, how decision support systems (DSS) work, which are, in fact, software that try to emulate the farmer’s experience by increasing the amount of information used and developing numerical inferences with greater precision. So, in summary, a precision system captures a series of signals that are processed into indices and then translated into agronomic meanings by a model that is fed by various sources of information and knowledge.
The difference between us and the precision system is that we apply overall evaluations on the basis of a series of experiences, knowledge, analogies, … through a basically holistic path and we have a limited and selective information storage capacity (we remember a few elements, only those we consider important and sometimes we forget even those!); the precision system does all this always on a numerical basis and has a storage capacity of information as wide and selective as we want. All this allows us to receive detailed information on large areas, that is, at scales beyond the capacity of our mind. However, the fact that we decide the selectivity with which the PC stores data, leads to a sort of mnemonic gigantism: we store billions of data that are useless, because they are characterized by too many decimals, well above the sensitivity of the measure, because they refer to portions of the territory too small, because they are already condensed into useful information. It is the “you never know” approach that preserves a piece of information regardless. Naked information, once processed, should be eliminated to avoid creating libraries of unbound sheets of drafts that risk concealing the location of the author’s final work.
To this different capacity of analysis and memorization, the precision agriculture systems associate the ability to find information in any part of the cultivated area of our interest, to be able to do it almost simultaneously, to be able to keep the observation connected to the place where it has been detected. Since we are not able to fly over our cornfields or our apple orchards, this is very interesting and obviously very useful.
But precision agriculture does more: it uses sensors that broaden the spectrum investigated by our senses and creates completely new ones by increasing the type of interference taken into account. Among these we mention the sensor that analyzes the interference between neutrons produced by cosmic rays and water stored in the soil, which we discuss in this issue.
Therefore, precision farming systems reproduce our senses (sight, hearing, touch, taste and smell), they broaden the spectrum of investigation (they see infrared, hear ultrasound, feel an infinitesimal film of water on a surface, smell and taste specific molecules, …), they add new perception tools capable of detecting interferences that we do not even imagine. Then, they process these “bits of information” using models that interpolate them with the knowledge so far acquired from science and agronomic experience and finally they indicate how to act or act accordingly.
In the words of Ernst Mach, it is the relationships that give an entity to objects: analyzing relationships and interferences with a greater number of senses, as precision agriculture does, allows us to better discover the object of our activity. Precision agriculture, therefore, offers us an augmented reality.
Lorenzo Benvenuti
