Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido

Translated title of the contribution: Cocoa pods ripeness estimation, using convolutional neural networks in an embedded system

Juan F. Heredia-Gómez, Juan P. Rueda-Gómez, Leonardo H. Talero-Sarmiento, Juan S. Ramírez-Acuña, Roberto A. Coronado-Silva

Research output: Contribution to journalArticlepeer-review

Abstract

A correct cocoa harvest involves determining a pod maturity. However, this farm activity is usually handmade, using criteria such as Size and Color of the pod; those characteristics differ according to the cocoa variety, making it difficult to standardize. For this reason, this work proposes an automated method to simplify the number of variables to develop a portable, low-cost, and custom-made tool, which makes use of a convolutional neural network to indicate whether a cocoa pod is found it at the right time to harvest. The main results of this work are: 1) the construction of three labeled data sets (1992 images each), and 2) we developed an embedded system with a 34.83% mAP (mean Average Precision) accuracy. Finally, variance analysis demonstrates that image size (i.e., 4033x4033 p, 1009x1009 p, and 505x505 p) does not affect accuracy.

Translated title of the contributionCocoa pods ripeness estimation, using convolutional neural networks in an embedded system
Original languageSpanish
Pages (from-to)42-55
Number of pages14
JournalRevista Colombiana de Computacion
Volume21
Issue number2
DOIs
StatePublished - Jul 2020
Externally publishedYes

Fingerprint

Dive into the research topics of 'Cocoa pods ripeness estimation, using convolutional neural networks in an embedded system'. Together they form a unique fingerprint.

Cite this