Precision Farming

Agriculture is one of the most important and ancient human activities. In recent years it has been strengthened and shared according to needs, problems, features of the specific area involved: this is the innovative energy of Precision Farming.  

The digital world we live has changed the way we see and interpret things. This is the same upgrade which involves agriculture: it is working hard to answer promptly the requests our society is posing.

This is the reason there is a need to create tools and techniques able to put the natural world and the mathematical one together: science and technology dedicated to agricultural activity.

AGRICULTURE REINVENTS ITSELF

Foto con vecchio fienile e aratro

 Picture with plow – Credits by mike138

Agriculture has been characterized by several revolutions that have increasingly brought riches and knowledge, refining techniques, highlighting good practices and identifying the specificities of different crops.

Agricultural production is the result of synergies between physical elements (soil, atmosphere, water) and biological (plants and parasites): such interactions are characterized by sudden changes that we must observe and analyse. In order to choose the best solutions, we need:

  • tools that read the changes;
  • more precise and punctual means for optimising work and resources consumption.

Due to climate change and variables that intervene in the system, maintaining optimal conditions is increasingly difficult and mistakes in crop management can create serious damage to the company’s economic sustainability. Control and prediction of infestations, fertilization and irrigation requirements (key elements of precision farming) allow to increase quality of products with great economic benefits for the farm.

Precision Farming aims to bring a significant increase in the quality of products with a significant reduction in the invested economic resources, while paying close attention to the environment.

MACHINERIES AND ANALYSIS

rilevamento dati con drone

 Data collection from field by using drones

The most important step of the agricultural evolution has been the massive use of equipment, machineries and instruments for easing the physical work of the farmer, reducing time of working with tools such as tractors, plows, harrows, harvesting machines, sprayers machines, etc.

Precision farming introduces monitoring and control activities, which can also relieve the mental work of the farm specialist. Thanks to software able to study the field situation in real time and to suggest the best practices to optimize resource use, it is possible to get maximum yield and minimize losses by taking advantage of the possibility of predicting risk situations and avoiding unnecessary intervention.

Then the production cycle can be divided into:

  • material component (workmanship),
  • intangible component (decisions).

This synergy between mechanical evolution and software application has changed the production process, as mechanical work is characterised by three basic phases:

  • information,
  • control,
  • prediction.

STUDY CYCLE

Modello di rielaborazione dati

 Data collection graphic

The control system characterising precision agriculture is based on the identification of the limits and the reference points; they are fundamental to personalise each decision by identifying two types of features:

  • constant over time, which can be considered for more decision-making and productive cycles (e.g. identifying the limits of a vineyard, mechanical properties of a machine, etc.)
  • variables for each cycle or decision (phenological phase of cultivation, seasonality, crop precession, etc.).

Once decisions are made, the monitoring and control system is applied in three phases.

  1. MEASURING OF FIELD DATA: the measurement covers the set of physical and chemical parameters related to operational aspects (use of instrumentation and means) and environmental (biology, meteorology, soil chemistry etc.). Examples: machine moving speed, crop vigor, fuel consumption, intensity of an infestation, distributed fertilizer quantity, etc.
  2. ANALYSIS: interpretation of measured variables. The analysis allows to interpret the measured phenomenon (e.g. it is possible to trace back to the nutritional status of crops through soil analysis) or to predict a phenomenon (e.g. pathogen infestations can be predicted according to weather data). Interpretative and predictive algorithms built specifically are used.
  3. PRESCRIPTION: creating a list of actions based on the analysis to improve the effectiveness of farm management. The prescription guides decisions about when, where and how much to intervene according to the intersection of two factors: measures analysis and specific prescriptive algorithms. This step aims to optimize the production process and to maximize the result by reducing costs. The operation is specifically implemented for different crops and operations (fertilization, irrigation, etc.).

Thanks to machine learning algorithms, the process is recursive and increases its accuracy year after year, decreasing the amount of data needed. The three phases, cyclically repeated, are able to increase their reliability over time and adapt to crop variations, allowing a dynamic control of them.