Pregled bibliografske jedinice broj: 1220445
The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples
The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples // 31st International Horticultural Congress (IHC2022)
Angers, Francuska, 2022. str. 363-372 doi:10.17660/ActaHortic.2023.1360.44 (predavanje, međunarodna recenzija, neobjavljeni rad, znanstveni)
CROSBI ID: 1220445 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
The potential of RGB camera for machine learning
in nondestructive detection of nutrient
deficiencies in apples
Autori
Viduka, Antonio ; Fruk, Goran ; Skendrović Babojelić, Martina ; Antolković, Ana Marija ; Vrtodušić, Rea ; Karažija, Tomislav ; Šatvar Vrbančić, Mihaela ; Grgić, Zoran ; Petek, Marko
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni
Skup
31st International Horticultural Congress (IHC2022)
Mjesto i datum
Angers, Francuska, 14.08.2022. - 20.08.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
annotation, mineral, orchard, plant nutrition, rover
Sažetak
From a plant nutrition perspective, the appearance of color changes and malformations on leaves and fruits usually indicates a nutrient imbalance in a complex and dynamic soil-plant-air system. Each nutrient deficiency symptom occurs differently on the plant. Observing such color changes in the appearance of transformation could help fruit growers respond and prevent further nutritional problems. The aim of this research was to create a model that could be used as a tool for nondestructive detection of nutrient deficiencies on leaves. RGB camera was used to manually record the occurrence of nutrient deficiencies in commercial apple orchards. Two hundred images were taken at each of five intervals during the day for several months of vegetation. The images were then processed in an annotation program (LabelImg) in which each leaf was classified into one of the following categories: healthy leaf or nitrogen, phosphorus, potassium, calcium, magnesium, iron, zinc, or manganese deficient. The data obtained from the latter program is used as training data which is used to build a model in the machine learning process. Machine learning is applied to a rover designed as a machine that records nutrient deficiencies with RGB cameras and drives autonomously through apple orchards. The training data was used as comparison points that enabled the machine to detect and classify nutrient deficiencies.
Izvorni jezik
Engleski
Znanstvena područja
Poljoprivreda (agronomija)
POVEZANOST RADA
Projekti:
--KK.01.2.1.02.0290 - AgriART sveobuhvatni upravljački sustav u području precizne poljoprivrede (AgriART) (Fruk, Goran) ( CroRIS)
Ustanove:
Agronomski fakultet, Zagreb
Profili:
Goran Fruk (autor)
Ana Marija Antolković (autor)
Antonio Viduka (autor)
Zoran Grgić (autor)
Tomislav Karažija (autor)
Marko Petek (autor)
Mihaela Šatvar (autor)
Rea Vrtodušić (autor)
Martina Skendrović Babojelić (autor)