About

Today, more than ever, agriculture has to face difficult challenges. Being a traditional productive activity, it has gone through many technological transformations in order to increase productivity and quality. Agriculture needs to reconsider its role and adapt to a changing world. Agricultural plants are extremely sensitive to climate changes as higher temperatures and variation of precipitations increase the chance of disease occurrence, leading to crop damage and even irreversible destruction of plants. Current advances in Internet of things (IoT) and Cloud Computing have led to the development of new applications based on highly innovative and scalable service platforms, including viticulture. The IoT solutions have great potential in assuring the quality and safety of agricultural products.

As a general objective, the project proposes the development of a precision farming platform using M2M / IoT radio-telemetry systems and a Cloud platform for processing collected data. Based on this, modelling and management applications can be developed by telemonitoring the risks of plant disease and managing irrigation in agriculture.

The purpose of the project is to implement and test a platform that allows decision making and the initiation of real-time actions through radio-telemetry systems. The project will develop a telemetry system with self-diagnosis and self-configuration functions for intelligent agriculture, based on renewable energy sources for powering the system. The proposal seeks to use advanced technology for IoT / M2M communications, especially where no GSM coverage is available, as well as increasing energy efficiency by using low-power sensors powered by a small solar panel. The telemetry system is well suited to intelligent farming activities. Precision agriculture contributes substantially to increasing crop yield by reducing resource losses such as water, fertilizers or pesticides by directing them to the crops most in need.

Phase I:

Activity 1.1. Critical analysis of the impact of weather conditions and pollutants on the health of crops.

This activity presents an analysis of chemical fertilizers, pesticides used in agriculture, their effects on human health and the impact of pollutants and weather conditions on agriculture.

Activity 1.2. Definition of use cases

During this activity are identified use cases such as irrigation, which involves controlled water management in order to increase crop yield and harvest quality. The document proposes to develop a support decision system for an irrigation system that considers meteorological parameters, plant evapotranspiration and which aims to develop commands to start irrigation pumps for a certain time, based on the information taken from transducers.

Activity 1.3. Definition of functional technical requirements and selection of technologies that meet the requirements

The use of sophisticated data processing techniques offered by measuring sensors (average values on demand intervals, exceedances of imposed thresholds, developments of the amounts of pollutants, comparisons with the limits imposed by European norms etc.) can be made at any time / from any place / by any beneficiary, transparently via the Internet. To cover both emergency and disaster situations, the system could also include a reliable satellite connection.

Phase II: Designing the M2M Precision Agriculture System

Activity 2.1. The general architecture of the system

A detailed description of many IoT platforms / architectures for intelligent agriculture, as well as some of the standardized architectures currently in place, is presented. The description also contains comparative analyses between the different architectures presented.

Activity 2.2. Technical specifications for making hardware and software components and interfaces and connectors

During this activity are identified platforms for agriculture, both in terms of irrigation and fertilizer utilization, in this report the hardware and software components (Libelium Smart Agriculture Xtreme, ADCON and Raspberry Pi) which underpin the intelligent agriculture monitoring system. Besides the description of these components, the data transmission, the connectors used for better data recording and the protocols used, also have an essential role to play.