Oil palm cultivation is one of the major agricultural activities in Colombia. Production performance is related to the good practices in the plantation, mainly regarding the management of phytosanitary conditions. Bud rot disease is the one with the greatest impact in Colombia. The most commonly used technique for its detection is from routine visual inspection on each palm, being costly and inefficient. For this reason, the aim of this study is the development of a classification algorithm based on binary support vector machines for the detection of Bud Rot. The model was obtained from 798 aerial images acquired by unmanned aerial vehicles. Each image was tagged by an expert palm grower based on the presence or absence of the disease. These images were described by 531 morphological features extracted using the concatenation of uniform binary local pattern vectors. Bootstrapping was used to balance the classes, obtaining 507 observations per class. To evaluate the performance metrics of the classifier, an 8-fold Monte Carlo cross-validation was implemented by randomly splitting the data set into training (80%), validation (10%), and test (10%) sets with balanced classes. Finally, the model achieved a performance greater than 96.0%. This indicates that the model developed could be a great technique to automate bud rot detection with high reliability, increasing the efficiency in the recognition. All these thanks to the fusion of Machine Learning techniques with the phenomena of optical physics.
|Número de artículo||012010|
|Publicación||Journal of Physics: Conference Series|
|Estado||Publicada - 1 dic. 2020|
|Evento||7th International Conference Days of Applied Mathematics, ICDAM 2020 - San Jose de Cucuta, Colombia|
Duración: 4 nov. 2020 → 6 nov. 2020
Nota bibliográficaPublisher Copyright:
© 2020 Published under licence by IOP Publishing Ltd.