Automatic Detection of Nutritional Deficiencies In Coffee Tree Leaves Through Shape And Texture Descriptors
Marcelo Vassallo-Barco, Luis Vives-Garnique, Victor Tuesta-Monteza, Heber I. MejÃ­a-Cabrera, Raciel Yera Toledo
Journal of Digital Information Management, Vol. 15, No. 1, 2017.
DOI: 
Link: http://dline.info/fpaper/jdim/v15i1/jdimv15i1_2.pdf
Abstract:
Nutritional deficiencies in coffee plants affect production and therefore it is important its early
identification. The current research is focused on the
automatic identification of nutritional deficiencies of Boron
(B), Calcium (Ca), Iron (Fe) and Potassium (K), by using
shape and texture descriptors in images of coffee tree
leaves. After the acquisition of images containing coffee
tree leaves, they are subjected to a segmentation process
using Otsu's method. Afterwards, for the resulting images
they are applied the descriptors Blurred Shape Model
(BSM) and Gray-Level Co-occurrence Matrix (GLCM) for
extracting characteristics of shape and texture. Finally,
the obtained image representation is used for training KNN,
NaÃ¯ve Bayes and Neural Network classifiers by using the
extracted features, in order to infer the type of deficiency
presented in each analyzed image. The experimental
results show that the developed procedure has a high
accuracy, being the better results associated to the
identification of Boron (B) and Iron (Fe) deficiencies.