Machine learning techniques in the detection of cocoa (Theobroma cacao l.) diseases

Ciro Rodriguez Rodriguez, Oswaldo Alfaro, Pervis Paredes, Doris Esenarro, Francisco Hilario

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva


The purpose of the research is to apply machine learning techniques to identify the cocoa tree's diseases (Theobroma cacao L.) and avoid the loss of crop harvests because farmers lack immediate tools to detect diseases on time. The methodology considers the use of machine learning with techniques for image processing and analysis such as HoG (Histograms of Oriented Gradient), LBP (Local Binary Pattern), and the SVM (Support Vector Machine) algorithm, for the classification to determine if the plant cocoa is being affected or not by disease. The results obtained show that SVM, Random Forest, and ANN's application with the characteristic vectors extracted with the HOG and LBP extraction algorithms predict the cocoa plant state; therefore, it is advisable to increase the dataset so that the results are more accurate.

Idioma originalInglés
Páginas (desde-hasta)7732-7741
Número de páginas10
PublicaciónAnnals of the Romanian Society for Cell Biology
EstadoPublicada - 2021
Publicado de forma externa

Nota bibliográfica

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