Project description DEENESFRITPL Computers to count crops Crop yield estimation is a central function in fruit production since it determines the part of the crop that is suitable for consumption and commercialisation. Yield estimation, which is crucial for apple production, needs to be reliable, quick and cost-effective. Today, fruit is counted manually by farm workers. Not only does this take a lot of time, but accuracy cannot be guaranteed especially for large yields. The EU-funded AGERPIX project is developing a solution based on artificial intelligence algorithms. It aims to offer reliable and fast predictions while considerably reducing operation and management costs. It provides growers with information on the size and quality of fruits and yield production. It calculates the labour force needed and offers logistical planning. Show the project objective Hide the project objective Objective "Crop yield estimation is an important task in apple orchard management. The current practice of yield estimation is based on manual counting of fruits by workers. It is extremely time-consuming, labour-intensive, highly inaccurate, and it is not practical for large fields. Agerpix provides accurate predictions to help growers improve fruit quality and reduce operating costs by making better decisions on intensity of fruit thinning and plant nutrients and treatments (mid-season), size of the harvest labour force, machinery and materials and logistical planning of storage, packing and cold warehouses, not to mention the development of a commercialization strategy tailored to the expected production, achieving a 50% cost reduction in orchard management operations. Artificial Intelligence algorithms are used to identify, measure diameter ranges, and envision the fruit leafiness and vigour, providing yield estimations over the plant heights and plant health variables.Several piloting projects for the yield estimation system at top apple producers (Nurfri - #1 Spanish and Blue Whale #1 French among others) have been deployed with +140ha analysed with a precision of 90-95%. AGERPIX system has been adapted to four different apple varieties. After successful validation activities, CODESIAN is developing a customer portfolio worth 38M€ in five years. However, because AGERPIX is offering as a B2B service and because the technology can be easily replicated to other fruits (ongoing validations with table grapes and peach with minor AI/sensor adaptations), a careful scale-up design to strengthen the business plan towards covering global needs fruit markets (apple: 517 M€; table grape: 124 M€; peach: 160M€; tangerine: 299 M€; avocado: 54 M€) is needed. After further data gathering through extensive validations across new fruits, CODESIAN projects +7,9M€ revenues with +4,5M€ EBIT and +60 new jobs created by 2024. " Fields of science natural sciencescomputer and information sciencesartificial intelligenceagricultural sciencesagriculture, forestry, and fisheriesagricultureagronomyplant protectionnatural sciencesbiological scienceszoologymammalogycetologyagricultural sciencesagriculture, forestry, and fisheriesagriculturehorticulturefruit growingengineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors Programme(s) H2020-EU.2.3. - INDUSTRIAL LEADERSHIP - Innovation In SMEs Main Programme H2020-EU.3. - PRIORITY 'Societal challenges H2020-EU.2.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies Topic(s) EIC-SMEInst-2018-2020 - SME instrument Call for proposal H2020-EIC-SMEInst-2018-2020 See other projects for this call Sub call H2020-SMEInst-2018-2020-1 Funding Scheme SME-1 - SME instrument phase 1 Coordinator CODESIAN SOFTWARE TECH SL Net EU contribution € 50 000,00 Address Cl del naranjo 6 4 44 42190 Golmayo (soria) Spain See on map Region Centro (ES) Castilla y León Soria Activity type Private for-profit entities (excluding Higher or Secondary Education Establishments) Links Contact the organisation Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 21 429,00
