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Artificial intelligence for yield estimations at fruit orchards

Descrizione del progetto

Computer per contare le colture

La stima del rendimento del raccolto è una funzione centrale nella produzione di frutta poiché determina la parte della coltura adatta al consumo e alla commercializzazione. La stima del rendimento, fondamentale per la produzione di mele, deve essere affidabile, rapida ed economica. Oggi la frutta viene contata manualmente dai lavoratori agricoli. Non solo ciò richiede molto tempo, ma non garantisce la precisione, soprattutto per grandi rendimenti. Il progetto AGERPIX, finanziato dall’UE, sta sviluppando una soluzione basata su algoritmi di intelligenza artificiale. Esso si propone di offrire previsioni affidabili e veloci, riducendo notevolmente i costi operativi e di gestione. Fornisce inoltre ai coltivatori informazioni sulle dimensioni e sulla qualità dei frutti e sulla produzione del raccolto. Infine, calcola la forza lavoro necessaria e offre una pianificazione logistica.

Obiettivo

"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.

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Invito a presentare proposte

H2020-EIC-SMEInst-2018-2020

Vedi altri progetti per questo bando

Bando secondario

H2020-SMEInst-2018-2020-1

Meccanismo di finanziamento

SME-1 - SME instrument phase 1

Coordinatore

CODESIAN SOFTWARE TECH SL
Contribution nette de l'UE
€ 50 000,00
Indirizzo
CL DEL NARANJO 6 4 44
42190 GOLMAYO (SORIA)
Spagna

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PMI

L’organizzazione si è definita una PMI (piccola e media impresa) al momento della firma dell’accordo di sovvenzione.

Regione
Centro (ES) Castilla y León Soria
Tipo di attività
Private for-profit entities (excluding Higher or Secondary Education Establishments)
Collegamenti
Costo totale
€ 71 429,00