Lightweight lotus phenotype recognition based on MobileNetV2-SE with reliable pseudo-labels

Abstract

Due to the wide variety of lotus species and the need for phenotypic categorization, traditional recognition is limited by the current manual observation and measurement of lotus phenotypes. In this paper, a lotus species recognition technique based on MobileNetV2-SE with reliable pseudo-labelling is proposed to construct an image dataset containing 94 different lotus species, and various data enhancement techniques are employed. Within MobileNetV2-SE, the classical MobileNetV2 network is improved by embedding the SE (Squeeze-and-Excitation) module, and then pseudo-labelling technical of semi-supervised learning is adopted to improve the classification performance of the model by generating high-quality labelling data. The test results show that the model in this paper can achieve an accuracy of 98.11% for lotus phenotype classification, and the precision, recall and F1 value can reach 98.45%, 98.47% and 98.40%, respectively, and the number of parameters and the amount of computation are 2.41×106 and 3.41×108 FLOPs, which are significantly better than other networks. This paper provides an effective solution for the automatic identification of lotus varieties and provides a reference for other plant variety identification tasks.

Plant phenotyping relevance

画像ベースでハスの表現型・品種を自動分類する深層学習手法を開発し、データセット構築と性能評価を行っており、表現型取得・分類手法が研究の中心である。

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