Grapevine nutritional disorder detection using image processing

Research output: Book chapter/Published conference paperConference paper

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Abstract

Vine nutrition is a key element of vineyard management. Nutrient disorders affect vine growth, crop yield, berry composition, and wine quality. Nutritional disorders can be detected visually on leaves, fruits, stems or roots. This paper presents our proposed method of using a smartphone app to capture and analyse images of vine leaves for identifying nutritional disorders of grapevines rapidly and conveniently. Nutrient deficiency/toxicity symptoms were created in hydroponically grown grapevines of both red and white varieties. RGB (red, green, and blue images of old and young leaves were taken weekly to track the progression of symptoms. A bench marked dataset was developed through a laboratory based nutrient analysis of the petioles. A wide range of features (e.g., texture, smoothness, contrast and shape) were selected for the following customised machine learning techniques. Our proposed algorithm was developed to identify specific deficiency and toxicity symptoms through training and testing process.
Original languageEnglish
Title of host publicationImage and Video Technology
Subtitle of host publication9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings
EditorsChilwoo Lee, Zhixun Su, Akihiro Sugimoto
PublisherSpringer
Pages184-196
Number of pages12
ISBN (Electronic)9783030348793
ISBN (Print)9783030348786
Publication statusPublished - 11 Nov 2019
Event9th Pacific-Rim Symposium on Image and Video Technology: PSIVT 2019 - Charles Sturt University Study Centre, Sydney, Australia
Duration: 18 Nov 201922 Nov 2019
http://www.psivt.org/psivt2019/index.html
http://www.psivt.org/psivt2019/program.html (program)
https://link.springer.com/book/10.1007/978-3-030-34879-3 (proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11854
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th Pacific-Rim Symposium on Image and Video Technology
CountryAustralia
CitySydney
Period18/11/1922/11/19
Internet address

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  • Cite this

    Rahaman, M., Baby, T., Oczkowski, A., Paul, M., Zheng, L., Schmidtke, L., Holzapfel, B., Walker, R., & Rogiers, S. (2019). Grapevine nutritional disorder detection using image processing. In C. Lee, Z. Su, & A. Sugimoto (Eds.), Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings (pp. 184-196). (Lecture Notes in Computer Science; Vol. 11854). Springer.