Coffee Crop Detection from UAS Orthomaps with Convolutional Neural Networks

Sergio Arriola-Valverde, Santiago López-Rojas, Daniel Ramírez-Valerio, Eduardo Somarriba-Chavez, Renato Rimolo-Donadio

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

This paper proposes a methodology for the automatic detection of coffee plants from close-range aerial images and deep learning techniques based on the YOLO framework. The data collection methodology, image preprocessing, and neural network training are discussed. Image datasets were collected at different locations in the central region of Costa Rica between 2018 and 2021. Models developed using this methodological approach had average mean accuracies up to 93% ([email protected]) on independent test sets; the detection performance achieved was 92% by using yolov4ext-ghost as a network architecture.

Idioma originalInglés
Título de la publicación alojada2023 IEEE Conference on AgriFood Electronics, CAFE 2023 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas5-9
Número de páginas5
ISBN (versión digital)9798350327113
DOI
EstadoPublicada - 2023
Evento1st IEEE Conference on AgriFood Electronics, CAFE 2023 - Torino, Italia
Duración: 25 sept 202327 sept 2023

Serie de la publicación

Nombre2023 IEEE Conference on AgriFood Electronics, CAFE 2023 - Proceedings

Conferencia

Conferencia1st IEEE Conference on AgriFood Electronics, CAFE 2023
País/TerritorioItalia
CiudadTorino
Período25/09/2327/09/23

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