The aim of this project is to develop a diagnostic tool for the rapid and accurate assessment of grapevine nutritional status in the field. A smartphone app will be developed to capture and analyse images of vine leaves, to rapidly assess nutritional status. Using this tool, vineyard managers and growers will be able to take a photo of a leaf with a smartphone, and through customised machine learning and computer vision techniques, make an assessment of nutrient deficiency/toxicity. The GPS function of the smartphone will be used to geo-tag the location of the selected vines, allowing tracking of seasonal changes in nutrient status, and the creation of an historical database.
The project will also generate a diagnostic key and an image library (from published sources) showing symptomatic leaves for various nutrient disorders, to assist growers in making in-field assessments. The feasibility of using a mini-spectrometer for nutrient assessment in real time will be evaluated against traditional petiole analyses, and the current tissue sampling protocol for vine macro and micronutrient status will be refined.
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