中文摘要：Artificial intelligence has allowed the development of tools for automatic recognition of fruits and vegetables with greater precision and speed. The development of new genotypes of fruit trees requires the use of technological tools to identify varieties with greater robustness than conventional methods. In this research, machine learning algorithms were applied to identify six peach varieties (Prunus persica L.) CP-03-06, Oro Azteca, Oro San Juan, Cardenal, Colegio and Robin from digital leaf images. The support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) models were trained and evaluated based on three chromatic and 14 morphological descriptors extracted from digital images. The evaluation of the prediction performance of the models was based on global metrics and specific for each target class (peach variety). The five most important descriptors to identify peach varieties were three HSV color channels (hue, saturation, value), roundness and eccentricity of the leaves. SVM achieved the highest overall classification accuracy with Acc of 98.7 % and F1macro of 98 %. SVM obtained the highest F1 score (99.2 %) to identify the peach variety CP-03-06 and the lowest F1 score (96.1 %) to identify the Cardenal variety. The joint use of chromatic and morphological descriptors improved the performance of learning algorithms to identify the six peach varieties. The SVM, RF and MLP models obtained an Acc of 98.7, 98.6 and 97 %, respectively. This study shows the potential of machine learning methods for their application in recognizing descriptors of interest in agricultural crops and their application to automated processes in agriculture.
外文关键词：computer vision;machine learning;artificial intelligence;Pattern recognition;Prunus persica L.
作者：Ayala-Nino, Daniel;Gonzalez-Camacho, Juanmanuel
作者单位：Posgrad SEI Computo Aplicado
期刊名称：REVISTA FITOTECNIA MEXICANA
期刊影响因子：0.0
出版年份：2024
出版刊次：47（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台