Automatic Freezing-Tolerant Rapeseed Material Recognition Using UAV Images and Deep Learning

Abstract

Abstract Background: Freezing injury is a serious and common damage that occurs to winter rapeseed during the overwintering period. The freezing injury directly reduces the rapeseed yield and causes serious economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipment. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of experts. Methods: To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning technology and unmanned aerial vehicle (UAV) images captured by a consumer drone. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning technology. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials collected by the Phantom 4 Pro. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and Conclusion: The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33%) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based approach significantly outperforms the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Plant phenotyping relevance

UAV画像と深層学習により、ナタネの凍結耐性という植物状態を自動推定する手法を開発・比較評価しており、表現型取得・抽出が研究の中心である。

Code and data availability

The supplied blocks describe a UAV-image dataset of 2847 rapeseed samples and CNN experiments, but contain no data availability statement, no public repository deposit, and no author code URL. The dataset and trained models are described but not publicly released in the provided text, and allowed_urls is empty, so no p

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