Nowadays, avocado has strong demand around the world due to its nutritional properties and because it is all year supplied from different parts of the world, being Peru one of the main providers. However, nutrient deficiencies and plague attacks during cultivation stages represent a major difficulty for farmers since early identification of these states (i.e. deficiencies and plagues) is a time-consuming activity that requires trained evaluators to do so. In this paper, an automatic method for identification of avocado leaf state is proposed. This method uses k-means, in a s-v space at superpixel level, to segment leaf from uniform background from images captured in-field in semi-controlled conditions and a shallow neural network to classify composed histograms from segmented leaves into 4 states: Healthy, Fe deficiency, Mg deficiency and red spider plague. The proposed method separates leaf from background with an average F-score of 0.98 and classifies leaf condition with an overall accuracy of 96.8%.
|Título de la publicación alojada||2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings|
|Editorial||Institute of Electrical and Electronics Engineers Inc.|
|ISBN (versión digital)||9781728114910|
|Estado||Publicada - abr 2019|
|Publicado de forma externa||Sí|
|Evento||22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Bucaramanga, Colombia|
Duración: 24 abr 2019 → 26 abr 2019
Serie de la publicación
|Nombre||2019 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019 - Conference Proceedings|
|Conferencia||22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2019|
|Período||24/04/19 → 26/04/19|
Nota bibliográficaPublisher Copyright:
© 2019 IEEE.
Copyright 2020 Elsevier B.V., All rights reserved.