Vine nutrition: a diagnostic smartphone app for vine nutritional disorders

Research output: Other contribution to conferencePoster

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.
Original languageEnglish
Publication statusPublished - 2019
Event17th Australian Wine Industry Technical Conference (AWITC 2019) - Adelaide Convention Centre, Adelaide, Australia
Duration: 21 Jul 201924 Jul 2019
https://awitc.com.au

Conference

Conference17th Australian Wine Industry Technical Conference (AWITC 2019)
CountryAustralia
CityAdelaide
Period21/07/1924/07/19
Internet address

Fingerprint

diet-related diseases
vines
signs and symptoms (plants)
nutrition
vineyards
wine quality
artificial intelligence
nutrient deficiencies
toxicity
image analysis
leaves
viticulture
petioles
nutrient requirements
rootstocks
small fruits
crop yield
grapes
soil types
growers

Cite this

Baby, T., Holzapfel, B., Oczkowski, A., Rahaman, M., Paul, M., Zheng, L., ... Rogiers, S. (2019). Vine nutrition: a diagnostic smartphone app for vine nutritional disorders. Poster session presented at 17th Australian Wine Industry Technical Conference (AWITC 2019), Adelaide, Australia.
@conference{723d851751004faea89953826e05ae1c,
title = "Vine nutrition: a diagnostic smartphone app for vine nutritional disorders",
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.",
author = "Tintu Baby and Bruno Holzapfel and Alex Oczkowski and Motiur Rahaman and Manoranjan Paul and Lihong Zheng and Leigh Schmidtke and Rob Walker and Suzy Rogiers",
year = "2019",
language = "English",
note = "17th Australian Wine Industry Technical Conference (AWITC 2019) ; Conference date: 21-07-2019 Through 24-07-2019",
url = "https://awitc.com.au",

}

Baby, T, Holzapfel, B, Oczkowski, A, Rahaman, M, Paul, M, Zheng, L, Schmidtke, L, Walker, R & Rogiers, S 2019, 'Vine nutrition: a diagnostic smartphone app for vine nutritional disorders' 17th Australian Wine Industry Technical Conference (AWITC 2019), Adelaide, Australia, 21/07/19 - 24/07/19, .

Vine nutrition: a diagnostic smartphone app for vine nutritional disorders. / Baby, Tintu; Holzapfel, Bruno; Oczkowski, Alex; Rahaman, Motiur; Paul, Manoranjan; Zheng, Lihong; Schmidtke, Leigh; Walker, Rob; Rogiers, Suzy.

2019. Poster session presented at 17th Australian Wine Industry Technical Conference (AWITC 2019), Adelaide, Australia.

Research output: Other contribution to conferencePoster

TY - CONF

T1 - Vine nutrition: a diagnostic smartphone app for vine nutritional disorders

AU - Baby, Tintu

AU - Holzapfel, Bruno

AU - Oczkowski, Alex

AU - Rahaman, Motiur

AU - Paul, Manoranjan

AU - Zheng, Lihong

AU - Schmidtke, Leigh

AU - Walker, Rob

AU - Rogiers, Suzy

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

M3 - Poster

ER -

Baby T, Holzapfel B, Oczkowski A, Rahaman M, Paul M, Zheng L et al. Vine nutrition: a diagnostic smartphone app for vine nutritional disorders. 2019. Poster session presented at 17th Australian Wine Industry Technical Conference (AWITC 2019), Adelaide, Australia.