Articles
The potential of RGB camera for machine learning in non-destructive detection of nutrient deficiencies in apples
Article number
1360_44
Pages
363 – 372
Language
English
Abstract
From a plant nutrition perspective, the appearance of colour 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 colour 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 non-destructive detection of nutrient deficiencies on leaves.
An 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 are 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 were used as comparison points that enabled the machine to detect and classify nutrient deficiencies.
Each nutrient deficiency symptom occurs differently on the plant.
Observing such colour 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 non-destructive detection of nutrient deficiencies on leaves.
An 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 are 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 were used as comparison points that enabled the machine to detect and classify nutrient deficiencies.
Authors
A. Viduka, G. Fruk, M. Skendrovic Babojelic, A.M. Antolkovic, R. Vrtodusic, T. Karazija, M. Satvar Vrbancic, Z. Grgic, M. Petek
Keywords
annotation, mineral, orchard, plant nutrition, rover
Groups involved
- Division Precision Horticulture and Engineering
- Division Plant-Environment Interactions in Field Systems
- Division Vine and Berry Fruits
- Division Tropical and Subtropical Fruit and Nuts
- Division Plant Genetic Resources, Breeding and Biotechnology
- Division Temperate Tree Nuts
- Division Temperate Tree Fruits
- Division Vegetables, Roots and Tubers
- Division Sustaining Horticulture in a Changing World
- Working Group Mechanization, Digitization, Sensing and Robotics
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