Recognition of Weeds Associated with Coffee Crops by Use of Co-occurrence Matrices, Texture Analysis and Machine Learning
DOI:
https://doi.org/10.22517/23447214.24589Keywords:
Coffee, Weed Recognition, Texture Analysis, Machine Learning.Abstract
The present work presents the results of twenty-four experiments realized for recognition of two classes of weed leaves associated with coffee crops. 210 images were acquired, 70 for each weed class, and 70 for coffee leaves samples. All images were processing and transformed into HSV color format. From each image, 33 texture patterns were extracted, which were reduced to four through principal component analysis. The fractal dimension was added as a fifth pattern. The recognition was done through the implementation of three machine learning techniques, support vector machine (SVM), k-near neighbors (KNN), and artificial neuronal network. Machine learning techniques permitted classification with precision and recall upper or equal to 95%, on average, when the fractal dimension was not used, and upper or equal to 97% on average when the fractal dimension was used. SVM and ANN were methods with better outcomes. Experiments constitute a first approximation to the implementation of an automatic system for selective weed eradication in a coffee crop.
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