Pig Advisor - ITCL

Pig Advisor – Virtual consultant for decision making in the management of intensive pig farms

Safe and healthy food chain

Project description

Modern animal production encompasses the generation of a massive amount of data or Big Data that requires more complex and complete management systems to optimize their use. This high volume of data requires new large-scale storage techniques and different approaches to retrieve the information; the variety of data sources makes simple relational networks difficult to apply; and finally, the relentless increase in data generation makes speed a key parameter in data management. In a competitive environment such as livestock production, the primary objective must be to improve the efficiency of production systems, for which the correct management of the data (collection, processing, analysis and distribution of information) generated every day in livestock farms is essential. This project aims to optimise the productivity of intensive pig farms. To this end, an early detection system will be generated for anomalous animal behaviour and suboptimal environmental conditions based on the prediction algorithms developed and the specific production history of each farm. Likewise, an early diagnosis tool for diseases will be offered through the online submission of the symptomatology appeared on the farm and the telediagnosis. That is to say, the scientific results obtained after the analysis of the databases available in this project will be applied in computer tools that will allow the application of the so-called precision livestock or Smart Farming. The main objective of the project therefore lies in the development of decision tools and automation technologies for the intelligent management of farms (Smart Farming) integrating different areas of knowledge to improve management, productivity and profits by valuing the data obtained daily and which, to date, are not exploited. The system will generate suggestion algorithms based on the data obtained by means of image analysis and sensors that will help to reach behavioural and environmental alerts, as well as a fast syndromic telediagnosis and recommendations on the necessary analytics.  

Objetives

SCIENTIFIC OBJETIVES:
  • To obtain algorithms for predicting reproductive results based on the feeding patterns of pregnant sows by correlating the data collected through feeding machines with the reproductive information contained in the farm management software.
  • Obtain disease incidence prediction algorithms based on the environmental conditions of the accommodations by correlating the data collected through sensors with the animal health information.
  • Generate a system of suggestion algorithms that allows a fast syndromic telediagnosis based on the incorporation of the information contained in the databases of the clinical diagnostic laboratories participating in the project.
  TECHNOLOGICAL OBJETIVES: Development of a computer application capable of:
  •  Collect data collected through feeding machines and environmental sensors, apply the algorithms developed in this project and generate health alerts for imminent appearance of disease or low reproductive yields.
  • Send images of syndromic manifestations online and, based on the information contained in the software database, offer a telediagnosis and the corresponding laboratory analysis recommendations.

Duration: 2018-2020

Partnes:

Financed by: MINECO, a través del programa de Retos Colaboración 2017