Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning

Manya Afonso*, Hubert Fonteijn, Felipe Schadeck Fiorentin, Dick Lensink, Marcel Mooij, Nanne Faber, Gerrit Polder, Ron Wehrens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

21 Citations (Scopus)


Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical step toward automating processes such as harvesting. Deep learning based methods have emerged as the state-of-the-art techniques in many problems in image segmentation and classification, and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. This paper reports results on the detection of tomatoes in images taken in a greenhouse, using the MaskRCNN algorithm, which detects objects and also the pixels corresponding to each object. Our experimental results on the detection of tomatoes from images taken in greenhouses using a RealSense camera are comparable to or better than the metrics reported by earlier work, even though those were obtained in laboratory conditions or using higher resolution images. Our results also show that MaskRCNN can implicitly learn object depth, which is necessary for background elimination.
Original languageEnglish
Article number571299
JournalFrontiers in Plant Science
Publication statusPublished - 19 Nov 2020


  • agriculture
  • deep learning
  • greenhouse
  • phenotyping
  • tomato


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