New publication on Botrytis cinerea forecast model
A new paper on Botrytis cinerea forecast modelling has been published in the Journal of Networking and Network Applications (2022). The paper is titled, “Qualitative parameter analysis for Botrytis cinerea forecast modelling using IoT sensor networks’. Botrytis cinerea is a fungus that affects many plant species but attacks wine grapes frequently. The research leading to these results received funding from the European Union’s Horizon 2020 — The EU Framework Programme for Research and Innovation 2014–2020, under DEMETER.
The authors of the publication are Spasenija Gajinov, Tomo Popović, Dejan Drajić, Nenad Gligorić and Srđan Krčo.
Abstract: This paper provides results of an evaluation of a fungal disease Botrytis cinerea forecast model (Model for Botrytis cinerea appearing) in vineyards for qualitative analysis of parameters that affect the development of the disease by using data from a network of connected sensors (air temperature and relative humidity, rain precipitation, and leaf wetness). The fungal disease model used by agronomists was digitalized and integrated into agroNET, a decision support tool, helping farmers to decide when to apply chemical treatments and which chemicals to use, to ensure the best growing conditions and suppress the growth of Botrytis cinerea. The temperature and humidity contexts are used to detect the risk of the disease occurrence. In this study, the impact of the humidity conditions (relative humidity, rain precipitation, and leaf wetness) is evaluated by assessing how different humidity parameters correlate with the accuracy of the Botrytis cinerea fungi forecast. Each observed parameter has its own threshold that triggers the second step of the disease modelling-risk index based on the temperature. The research showed that for relative humidity, rain precipitation, and leaf wetness measurements, a low-cost relative humidity sensor can detect, on average, 14.61% of cases, a leaf wetness sensor an additional 3.99% of risk cases, and finally, a precipitation sensor can detect an additional 0.59% of risk cases (in the observed period the risk was detected in 19.19% (14.61%+3.99%0.59%) of the time), which gives a guide to farmers how to consider cost effective implementation of sensors to achieve good performance. The use of the proposed model reduced the use of pesticides up to 20%
The full paper is available DOI:10.33969/J-NaNA.2022.020305
Read MorepoultryNET case study video presentation
poultryNET is a cloud-based solution for the digital transformation of poultry farms. It provides real-time observations of the parameters of interest and digitized domain expertise enabling farmers to manage all steps in the broiler production while reducing negative environmental impact and respecting animal wellbeing.
The main features provide complete 24/7 insight into poultry barns from monitoring environmental parameters, daily weight and consumed feed and water, to a biosafety guide, costs/revenue calculator and digital assistant in problem solving. Using the poultryNET the environmental conditions, as well as food and water consumption and prepared feed mixture, could be tailored to specific animal needs and production goals. Increased of energy, optimized food and water consumption and better meat quality are the main benefits.
The poultryNET solution is applicable for broiler farms, parental flocks and laying hens.
Case study
Four years ago, we started cooperation with Agroprodukt Šinković company by implementing our digital farming platform for poultry production on their farm. The collaboration continued within the DEMETER project by expanding solutions and setting a basis for a transparent supply chain.
The Agroprodukt Šinković is a Serbian company focused on raising laying hens, parental flocks and day-old chicks for over 20 years. Their annual production is more than 10 million day-old chickens.
The video briefly presents the poultryNET case study – with case study customer Agroproduct Šinković Farm.
Read MoreagroNET in poultry barns
Three years ago, we started cooperation with Agroprodukt Šinković company by implementing our agroNET platform for poultry production on their farm. The cooperation continued within the DEMETER project by expanding solution and setting a basis for a transparent supply chain.
agroNET platform modules for optimizing poultry breeding, both parental flocks and laying hens have been deployed in Agroprodukt’s poultry barns in order to provide real-time insight into environmental conditions and enable animal welfare.
The Agroprodukt Šinković is a Serbian company that has been focused on raising laying hens, parental flocks and day-old chicks for more than 20 years. Their annual production is more than 10 million day-old chickens.
Laslo Šinković, the owner of the company, said: “We are using the agroNET platform for poultry production as a tool for optimization of raising laying hens and parental flocks. The platform brings us 24/7 insight into poultry barns without the need for on-site inspection a few times per day. This allows us to react on time and provide optimal environmental conditions for raising poultry. Additionally, the possibility of comparing our results with technology parameters is very helpful in feed management.”
Future steps will lead to wider cooperation: “We are planning to expand digitization on other parts of our production to be able to optimize inputs and provide transparency of all relevant parameters for our partners, different stakeholders” – explains Laslo.
Watch the interview with Laslo and learn more about their involvement in the DEMETER project.
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