Abstract
Optimising vine nutrition is one of the key vineyard management aspects determining vine growth, crop yield, berry composition, and wine quality. Nutritional requirements can vary between vineyards due to the influence of soil type, climate, vine age, crop removal, variety, rootstock and desired wine quality. A rapid diagnostic tool for assessing vine nutritional disorders is vital for grape growers and vineyard managers. There are smartphone Apps available that provide diagnostic information on plant nutrient deficiency and toxicity symptoms, however, they are not specific to viticulture. Even though there are several grapevine fact sheets, handbooks, field manuals, and an online tool the information does not detail the progression of the symptoms, does not take into account leaf age and usually does not provide information specific to red or white varieties.
The current project aims to develop a smartphone App to capture and analyse images of vine leaves so as to rapidly and conveniently assess nutritional disorders of grapevines with minimal cost. Nutrient deficiency/toxicity symptoms were created in hydroponically grown grapevine plants, for both red and white varieties. RGB (red, green, and blue) images of old and young leaves were taken weekly to track progression of symptoms. Nutrient analysis of petioles were matched with symptoms severity. Using image analysis features (e.g. texture, smoothness, contrast, salience and shape) and customised machine learning techniques, algorithms were created to identify specific deficiency and toxicity symptoms. The experimental results show that image analysis and machine learning approach provide better results.
The current project aims to develop a smartphone App to capture and analyse images of vine leaves so as to rapidly and conveniently assess nutritional disorders of grapevines with minimal cost. Nutrient deficiency/toxicity symptoms were created in hydroponically grown grapevine plants, for both red and white varieties. RGB (red, green, and blue) images of old and young leaves were taken weekly to track progression of symptoms. Nutrient analysis of petioles were matched with symptoms severity. Using image analysis features (e.g. texture, smoothness, contrast, salience and shape) and customised machine learning techniques, algorithms were created to identify specific deficiency and toxicity symptoms. The experimental results show that image analysis and machine learning approach provide better results.
| Original language | English |
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| Publication status | Published - 2019 |
| Event | 17th Australian Wine Industry Technical Conference (AWITC 2019) - Adelaide Convention Centre, Adelaide, Australia Duration: 21 Jul 2019 → 24 Jul 2019 https://awitc.com.au https://awitc.com.au/conference-program/ (Program) https://awitc.com.au/program/proceedings/seventeenth/ (Conference report) |
Conference
| Conference | 17th Australian Wine Industry Technical Conference (AWITC 2019) |
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| Country/Territory | Australia |
| City | Adelaide |
| Period | 21/07/19 → 24/07/19 |
| Other | The Australian Wine Industry Technical Conference is the premier technical event for the Australian wine industry. Held every three years since 1970, it combines an extensive program of plenary sessions, workshops, posters, student forum and social events with the industry’s most respected and extensive trade exhibition. The Australian Wine Industry Technical Conference Inc. has two members: The Australian Wine Research Institute and the Australian Society of Viticulture and Oenology. |
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