engleski

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

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.

annotation, mineral, orchard, plant nutrition, rover

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