A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques

Teodoro Ibarra-Pérez; Ramón Jaramillo-Martínez; Hans C. Correa-Aguado; Christophe Ndjatchi; Ma. del Rosario Martínez-Blanco; Héctor A. Guerrero-Osuna; Flabio D. Mirelez-Delgado; José I. Casas-Flores; Rafael Reveles-Martínez; Umanel A. Hernández-González

A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques

2024

The early and precise identification of the different phenological stages of the bean (<i>Phaseolus vulgaris</i> L.) allows for the determination of critical and timely moments for the implementation of certain agricultural activities that contribute in a significant manner to the output and quality of the harvest, as well as the necessary actions to prevent and control possible damage caused by plagues and diseases. Overall, the standard procedure for phenological identification is conducted by the farmer. This can lead to the possibility of overlooking important findings during the phenological development of the plant, which could result in the appearance of plagues and diseases. In recent years, deep learning (DL) methods have been used to analyze crop behavior and minimize risk in agricultural decision making. One of the most used DL methods in image processing is the convolutional neural network (CNN) due to its high capacity for learning relevant features and recognizing objects in images. In this article, a transfer learning approach and a data augmentation method were applied. A station equipped with RGB cameras was used to gather data from images during the complete phenological cycle of the bean. The information gathered was used to create a set of data to evaluate the performance of each of the four proposed network models: AlexNet, VGG19, SqueezeNet, and GoogleNet. The metrics used were accuracy, precision, sensitivity, specificity, and F1-Score. The results of the best architecture obtained in the validation were those of GoogleNet, which obtained 96.71% accuracy, 96.81% precision, 95.77% sensitivity, 98.73% specificity, and 96.25% F1-Score.

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