Algorithms to estimation of size and shape tomato using Artificial Vision Techniques

Published in: Industry, Innovation, and Infrastructure for Sustainable Cities and Communities: Proceedings of the 17th LACCEI International Multi-Conference for Engineering, Education and Technology
Date of Conference: July 24-26, 2019
Location of Conference: Montego Bay, Jamaica
Authors: Wolffang David Niño Pacheco (Universidad Pedagógica y Tecnológica de Colombia, CO)
Fabián Rolando Jiménez López (Universidad Pedagógica y Tecnológica de Colombia, CO)
Andrés Fernando Jiménez López (Universidad de los Llanos, CO)
(Universidad Pedagógica y Tecnológica de Colombia)
Full Paper: #5


This paper describes the development of estimation algorithms for the size and shape of tomato fruits, implemented on a portable electronic system used in greenhouses, with the purpose to classify and collect of tomatoes milano and chonto type. The algorithms were implemented in a Raspberry-Pi embedded card through the use of free software libraries (Python, OpenCV, Pillow and Scikit-Image) and using a Pi-Camera for real-time processing, taking into account the parameters of classification defined in national (NTC1103-1) and international (USDA) standards. Were applied classification algorithms based on Canny to estimate the size of the tomato fruit, as well as techniques based on estimation of the eccentricity and statistical moments descriptors for the measurement of the shape, which were evaluated with a performance superior to 90 %. Classification techniques of tomato shape and size using artificial vision overcame subjective techniques of visual and tactile type classification carried out by farmers and specialized technicians.