Abstract
Various diseases of greenhouse tomatoes reduce fruit yield and quality, which leads to serious economic losses globally. In large and well-equipped greenhouses, there are different solutions to avoid or minimize the spread of diseases, including environmental sensors and control options, plant vigor sensors, different visioning systems, decision support systems (DSS), etc. besides experienced agronomists are also available for quick, timely, and most effective procedures. On the other hand, smaller and less-equipped greenhouses often have problems with both of the mentioned obstacles. It is not worth offering complicated and expensive solutions to small and medium-size tomato growers, because they do not have access to sufficient financial and human resources. The goal of this project is to develop a relatively inexpensive and effective solution to help growers reduce the potential risk of spread of diseases in small and medium-sized tomato greenhouses.
Greenhouse tomato disease risk warning and detection system includes a disease development risk evaluation model as well as a specific disease detection model. The development of the system will be based on the following regular and occasional data acquisition tools/sources: i) data acquisition from existing greenhouse sensors (mostly inside/outside temperature and air humidity), ii) data captured by MultiSpectral (MS) camera; iii) data captured by RGB camera (mobile phone) and iv) additional information which added manually (cultivar, sizes of greenhouse, etc.). The greenhouse tomato disease risk warning and detection system will use two separate models: i) one model to integrate and evaluate different data to conclude the level of specific disease spread/development risk, ii) another model uses images of diseased plant leaves, to detect the specific disease if it occurs. Data for both model development will be collected in scientific and commercial greenhouses over a three-year period. In a scientific greenhouse, inoculation of tomato plants with relevant diseases will be conducted, as well as modelling of suitable conditions for disease development to collect maximum data (e.g. images) of diseased plants.
While in a commercial greenhouse, previously developed models will be evaluated and non-diseased measurements conducted - for leaf area index assessment, plant density on different levels assessment, etc.
Keywords
VIS-NIR sensors, RGB camera, MS camera, tomato growing in the greenhouse, decision support sensors
Project coordinator
Dr. Viktorija Zagorska, Latvia University of Life Sciences and Technologies, Faculty of Agriculture and Food Technology, Institute of Plant Protection, Latvia. (P1)
Partners
- LATVIA: Dr. Aleksejs Zacepins, Latvia University of Life Sciences and Technologies, Faculty of Engineering and Information Technologies (P1)
- LATVIA: Dr. Vitalijs Komasilovs, Latvia University of Life Sciences and Technologies, Faculty of Engineering and Information Technologies (P1)
- LATVIA: Dr. Gunita Bimsteine, Latvia University of Life Sciences and Technologies, Faculty of Agriculture and Food Technology (P1)
- LATVIA: Mr. Maksims Filipovics, Latvia University of Life Sciences and Technologies, Faculty of Engineering and Information Technologies (P1)
- ESTONIA: Mr. Aare Raev, Tamiatics OU (P2)
- ESTONIA: Dr. Ulvi Moor, Tamiatics OU (P2)
- ESTONIA: Mr Olev Abel, Tamiatics OU (P2)
- ESTONIA: Mr Karl Martin, Tamiatics OU (P2)
- TURKEY: Dr. Mahmut Durgun, Department of Electronic Commerce and Management. Turhal Faculty of Applied Sciences (P3)
- TURKEY: Dr. Yeliz Durgun, Department of Electronic Commerce and Management. Turhal Faculty of Applied Sciences (P3)
Funding institutions
- Latvian Council of Science (LZP), Latvia.
- The Scientific and Technological Research Council of TÜRKİYE (TUBITAK), Turkey.
- Ministry of Regional Affairs and Agriculture (MEM), Estonia
Project duration
36 Months
Abstract
Various diseases of greenhouse tomatoes reduce fruit yield and quality, which leads to serious economic losses globally. In large and well-equipped greenhouses, there are different solutions to avoid or minimize the spread of diseases, including environmental sensors and control options, plant vigor sensors, different visioning systems, decision support systems (DSS), etc. besides experienced agronomists are also available for quick, timely, and most effective procedures. On the other hand, smaller and less-equipped greenhouses often have problems with both of the mentioned obstacles. It is not worth offering complicated and expensive solutions to small and medium-size tomato growers, because they do not have access to sufficient financial and human resources. The goal of this project is to develop a relatively inexpensive and effective solution to help growers reduce the potential risk of spread of diseases in small and medium-sized tomato greenhouses.
Greenhouse tomato disease risk warning and detection system includes a disease development risk evaluation model as well as a specific disease detection model. The development of the system will be based on the following regular and occasional data acquisition tools/sources: i) data acquisition from existing greenhouse sensors (mostly inside/outside temperature and air humidity), ii) data captured by MultiSpectral (MS) camera; iii) data captured by RGB camera (mobile phone) and iv) additional information which added manually (cultivar, sizes of greenhouse, etc.). The greenhouse tomato disease risk warning and detection system will use two separate models: i) one model to integrate and evaluate different data to conclude the level of specific disease spread/development risk, ii) another model uses images of diseased plant leaves, to detect the specific disease if it occurs. Data for both model development will be collected in scientific and commercial greenhouses over a three-year period. In a scientific greenhouse, inoculation of tomato plants with relevant diseases will be conducted, as well as modelling of suitable conditions for disease development to collect maximum data (e.g. images) of diseased plants.
While in a commercial greenhouse, previously developed models will be evaluated and non-diseased measurements conducted - for leaf area index assessment, plant density on different levels assessment, etc.
Keywords
VIS-NIR sensors, RGB camera, MS camera, tomato growing in the greenhouse, decision support sensors
Project coordinator
Dr. Viktorija Zagorska, Latvia University of Life Sciences and Technologies, Faculty of Agriculture and Food Technology, Institute of Plant Protection, Latvia. (P1)
Partners
Funding institutions
Project duration
36 Months