Dynamic Measurement of Portos Tomato Seedling Growth Using the Kinect 2.0 Sensor

José Joel González-Barbosa, Alfonso Ramírez-Pedraza, Francisco Javier Ornelas-Rodríguez, Diana Margarita Cordova-Esparza, Erick Alejandro González-Barbosa

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Traditionally farmers monitor their crops employing their senses and experience. However, the human sensory system is inconsistent due to stress, health, and age. In this paper, we propose an agronomic application for monitoring the growth of Portos tomato seedlings using Kinect 2.0 to build a more accurate, cost-effective, and portable system. The proposed methodology classifies the tomato seedlings into four categories: The first corresponds to the seedling with normal growth at the time of germination; the second corresponds to germination that occurred days after; the third category entails exceedingly late germination where its growth will be outside of the estimated harvest time; the fourth category corresponds to seedlings that did not germinate. Typically, an expert performs this classification by analyzing ten percent of the randomly selected seedlings. In this work, we studied different methods of segmentation and classification where the Gaussian Mixture Model (GMM) and Decision Tree Classifier (DTC) showed the best performance in segmenting and classifying Portos tomato seedlings.

Original languageEnglish
Article number449
Pages (from-to)449
JournalAgriculture (Switzerland)
Issue number4
StatePublished - 23 Mar 2022
Externally publishedYes


  • 3D Segmentation
  • cloud points
  • Kinect 2.0
  • morphology features
  • seedling


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