EVALUATION OF MACHINE LEARNING MODELS TO IDENTIFY PEACH VARIETIES BASED ON LEAF COLOR

Abstract

Machine learning and deep learning approaches are applied in different areas of the agricultural sector, particularly in the digital image-based identification of characteristics of interest in crops. In this research, the performance of three machine learning classifiers was evaluated: support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP). The aim was to identify four varieties of peach (Prunus persica L. Batsch) (CP-03-06, Oro Azteca, Oro San Juan, and Cardenal), based on the color of digital images of the upper and lower side of leaves, represented by two color spaces: RGB (red, green, blue) and HSV (hue, saturation, value). The classifiers were trained and evaluated based on six data input scenarios, defined by the combinations of the upper, lower, and both sides of the leaf with the RGB and HSV color spaces. The three machine learning classifiers (SVM, RF, and MLP) achieved their best prediction performance when they examined the color characteristics of the upper side of leaves transformed to the HSV color space. The SVM classifier outperformed RF and MLP. SVM achieved a global average correct classification accuracy of 84.1 %, F1 macro of 83.7 %, and area under the ROC curve (AUC macro) of 0.93. The Oro Azteca variety reached the highest classification rate with a F1 score of 87.9 % and the Oro San Juan variety obtained the lowest rate with a F1 of 71.3 %