Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/9759
Título
Temporal forecasting of plant height and canopy diameter from RGB images using a CNN-based regression model for ornamental pepper plants (Capsicum spp.) growing under high-temperature stress
Autor
Publicado en
Neural Computing and Applications. 2024,
Editorial
Springer
Fecha de publicación
2024
ISSN
0941-0643
DOI
10.1007/s00521-024-10502-w
Resumen
Being capable of accurately predicting morphological parameters of the plant weeks before achieving fruit maturation is of great importance in the production and selection of suitable ornamental pepper plants. The objective of this article is evaluating the feasibility and assessing the performance of CNN-based models using RGB images as input to forecast two morphological parameters: plant height and canopy diameter. To this end, four CNN-based models are proposed to predict these morphological parameters in four different scenarios: first, using as input a single image of the plant; second, using as input several images from different viewpoints of the plant acquired on the same date; third, using as input two images from two consecutive weeks; and fourth, using as input a set of images consisting of one image from each week up to the current date. The results show that it is possible to accurately predict both plant height and canopy diameter. The RMSE for a forecast performed 6 weeks in advance to the actual measurements was below 4.5 cm and 4.2 cm, respectively. When information from previous weeks is added to the model, better results can be achieved and as the prediction date gets closer to the assessment date the accuracy improves as well.
Palabras clave
Image-based temporal prediction
Morphological parameters
Ornamental potential
Precision agriculture
Solanaceae
Thermal stress
Materia
Redes neuronales artificiales
Neural networks (Computer science)
Agricultura
Agriculture
Versión del editor
Aparece en las colecciones