Fine-Grained Plant Identification using wide and deep learning model ¹

Abstract

In recent years, with the evolution of deep learning technology, the performance of plant image recognition has improved remarkably. In this paper, we propose a model to address the fine-grained plant image classification task by using the wide and deep learning framework which combines a linear model and a deep learning model. Proposed method sums the result of the wide and deep learning model using a logistic function so that discrete features can be considered simultaneously with continuous image content. Our works used metadata such as the date of flowering and locational information for the wide model. Our experiment shows that the proposed method gives better performance than a baseline method.