Vine nutrition: A diagnostic app for nutrient disorder assessment

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Abstract

A smartphone app was developed for the diagnosis of nutritional disorders in grapevines using visual leaf symptoms. The app uses artificial intelligence (AI) to assess images captured by the user using a standard camera on a smartphone. To develop the symptom database, Chardonnay and Shiraz vines were grown hydroponically in various nutrient solutions and RGB images were captured of leaves as symptoms developed and progressed. The image database includes symptoms for nitrogen, potassium, magnesium, calcium and iron deficiency. Machine learning was used to process the images and a model with high accuracy and rapid processing times was incorporated into the app. In a follow-up study, hyperspectral imaging accompanied by machine learning also proved effective in identifying leaf age-based differences and individual nutrient disorders. Lastly, based on gradients in nutrients along the petiole, tissue sampling protocols for nutrient assessment were refined.
Original languageEnglish
Place of PublicationNSW, Australia
PublisherNSW Department of Primary Industries
Commissioning bodyAustralian Grape and Wine Authority trading as Wine Australia
Number of pages86
Publication statusPublished - 15 Dec 2021

Grant Number

  • CSU1702-3

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