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Pregled bibliografske jedinice broj: 1219552

The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples


Viduka, Antonio; Fruk, Goran; Skendrović Babojelić, Martina; Antolković, Ana Marija; Vrtodušić, Rea; Karažija, Tomislav; Šatvar Vrbančić, Mihaela; Grgić, Zoran; Petek, Marko
The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples // Acta horticulturae (2022) (znanstveni, prihvaćen)


CROSBI ID: 1219552 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
Radovi u časopisima, znanstveni

Izvornik
Acta horticulturae (2022)

Status rada
Prihvaćen

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:
MINGO-KK.01.2.1.02.0290 - AgriART sveobuhvatni upravljački sustav u području precizne poljoprivrede (AgriART) (Fruk, Goran, MINGO ) ( CroRIS)

Ustanove:
Agronomski fakultet, Zagreb


Citiraj ovu publikaciju:

Viduka, Antonio; Fruk, Goran; Skendrović Babojelić, Martina; Antolković, Ana Marija; Vrtodušić, Rea; Karažija, Tomislav; Šatvar Vrbančić, Mihaela; Grgić, Zoran; Petek, Marko
The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples // Acta horticulturae (2022) (znanstveni, prihvaćen)
Viduka, A., Fruk, G., Skendrović Babojelić, M., Antolković, A., Vrtodušić, R., Karažija, T., Šatvar Vrbančić, M., Grgić, Z. & Petek, M. (2022) The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples. Prihvaćen za objavljivanje u Acta horticulturae. [Preprint].
@unknown{unknown, author = {Viduka, Antonio and Fruk, Goran and Skendrovi\'{c} Babojeli\'{c}, Martina and Antolkovi\'{c}, Ana Marija and Vrtodu\v{s}i\'{c}, Rea and Kara\v{z}ija, Tomislav and \v{S}atvar Vrban\v{c}i\'{c}, Mihaela and Grgi\'{c}, Zoran and Petek, Marko}, year = {2022}, keywords = {annotation, mineral, orchard, plant nutrition, rover}, journal = {Acta horticulturae}, title = {The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples}, keyword = {annotation, mineral, orchard, plant nutrition, rover} }
@unknown{unknown, author = {Viduka, Antonio and Fruk, Goran and Skendrovi\'{c} Babojeli\'{c}, Martina and Antolkovi\'{c}, Ana Marija and Vrtodu\v{s}i\'{c}, Rea and Kara\v{z}ija, Tomislav and \v{S}atvar Vrban\v{c}i\'{c}, Mihaela and Grgi\'{c}, Zoran and Petek, Marko}, year = {2022}, keywords = {annotation, mineral, orchard, plant nutrition, rover}, journal = {Acta horticulturae}, title = {The potential of RGB camera for machine learning in nondestructive detection of nutrient deficiencies in apples}, keyword = {annotation, mineral, orchard, plant nutrition, rover} }




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