Marja Masiina - Machine vision berry observation in follow-up (Q4297655): Difference between revisions
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(Added [en] label: Marja Masiina - Machine vision berry observation in follow-up) |
(Added [fi] label: Marjamasiina ‐ Konenäkö marjahavaintoseurannassa) |
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Marjamasiina ‐ Konenäkö marjahavaintoseurannassa |
Revision as of 13:55, 20 June 2022
Project Q4297655 in Finland
Language | Label | Description | Also known as |
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English | Marja Masiina - Machine vision berry observation in follow-up |
Project Q4297655 in Finland |
Statements
93,880.0 Euro
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144,431.0 Euro
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65.0 percent
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18 January 2021
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30 September 2022
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Lapin ammattikorkeakoulu Oy
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Berry harvest observations and forecast have been going on since 90' in Finland. The observations have been based on network of monitoring forests, in every forest there are five 1 square meter observation squares. Flowers, raw berries and ripe berries are calculated during the growing season and forecasts are based on these calculations. Nibio is currently establishing similar approach in Norway. On 2017 Natural Resources Finland (Luke) started to apply citizen science concept in berry observations. In the concept, everyone can establish a monitoring forest with observation squares and do the calculations. The results are stored to marjahavainnot.fi -website, in which the results of observations are freely available. Although the observation procedure is simple and straightforward, calculations are time-consuming and require meticulousness and accuracy. New approachs and techniques are needed for the observations. One possibility is to determine the number of flowers, raw berries and ripe berries by machine vision. In this approach, observer takes a digital pictire of the observation square and computer algorithm estimates the number of flowers, raw berries or ripe berries. The objective of the project is to develop a solution that combines machine learning and image analysis in order to determine the amounts of pre-determined objects in imagine. In this case the objects are the flowers, raw berries and ripe berries. Cloud computing services will be applied, since they often have the features supporting machine learning capabilities already to a certain extent. This cloud-based machine learning system will be referred as back end. The ultimate goal is to be able to determine the amount of berries in a location by a photo created by ordinary mobile device. For this new mobile application a prototype will be produced, which allows us to create testing material with a varying range of mobile phones from different price ranges. A test plan will also be created in the project, which allows us to perform tests on mobile phones of different price ranges and their cpabilities. The purpose is to research whether it is possible to achieve valid measurements by using mobile phone cameras. The validity of hthe source material will also be tested - i.e. whether a single photo or a video source is a better alternative for analysis. (English)
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