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 and Erick-Alejandro González-Barbosa
Additional contact information
José-Joel González-Barbosa: Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada Unidad Querétaro, Cerro Blanco 141, Querétaro 76090, Mexico
Alfonso Ramírez-Pedraza: Visión Robótica, Centro de Investigaciones en Óptica A.C., Loma del Bosque 115, Guanajuato 37150, Mexico
Francisco-Javier Ornelas-Rodríguez: Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada Unidad Querétaro, Cerro Blanco 141, Querétaro 76090, Mexico
Diana-Margarita Cordova-Esparza: Facultad de Informática, Universidad Autónoma de Querétaro, Av. de las Ciencias S/N, Juriquilla 76230, Mexico
Erick-Alejandro González-Barbosa: Tecnológico Nacional de México/ITS de Irapuato, Carretera Irapuato—Silao km 12.5 Colonia El Copal, Irapuato 36821, Mexico

Agriculture, 2022, vol. 12, issue 4, 1-24

Abstract: 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.

Keywords: seedling; morphology features; cloud points; 3D Segmentation; Kinect 2.0 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/12/4/449/pdf (application/pdf)
https://www.mdpi.com/2077-0472/12/4/449/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:12:y:2022:i:4:p:449-:d:777917

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().