Detection of tomato flowers from greenhouse images using colorspace transformations

Manya Afonso*, Angelo Mencarelli, Gerrit Polder, Ron Wehrens, Dick Lensink, Nanne Faber

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

Abstract

In this paper we propose an image analysis method for detecting and counting tomato flowers from images taken in a greenhouse. Detecting and locating flowers is useful information for tomato growers and breeders, for phenotyping, yield prediction, and for automating procedures such as pollination and spraying. Since the tomato flowers are yellow, we first apply a set of grayscale transformations in which yellow regions stand out, and then threshold and combine them by a logical binary AND operation. Using more than one transform reduces the possibility of spurious detections due to non-flower regions of the image appearing yellow due to illumination conditions. Connected regions larger than a certain threshold are selected as instances belonging to the class flower. Experimental results over images acquired in a greenhouse using a Realsense camera show that this approach could detect flowers with a recall of 0.79 and precision of 0.77, which are comparable to the values reported in literature with higher resolution cameras closer to the flowers being imaged.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence
Subtitle of host publication19th EPIA Conference on Artificial Intelligence, EPIA 2019, Proceedings, Part I
EditorsPaulo Moura Oliveira, Paulo Novais, Luís Paulo Reis
PublisherSpringer Verlag
Pages146-155
ISBN (Print)9783030302405
DOIs
Publication statusPublished - 1 Jan 2019
Event19th EPIA Conference on Artificial Intelligence, EPIA 2019 - Vila Real, Portugal
Duration: 3 Sep 20196 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberLNAI 11804
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th EPIA Conference on Artificial Intelligence, EPIA 2019
CountryPortugal
CityVila Real
Period3/09/196/09/19

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Keywords

  • Computer vision in agriculture
  • Flower detection
  • Phenotyping

Cite this

Afonso, M., Mencarelli, A., Polder, G., Wehrens, R., Lensink, D., & Faber, N. (2019). Detection of tomato flowers from greenhouse images using colorspace transformations. In P. Moura Oliveira, P. Novais, & L. P. Reis (Eds.), Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Proceedings, Part I (pp. 146-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); No. LNAI 11804). Springer Verlag. https://doi.org/10.1007/978-3-030-30241-2_13