Three-dimensional image recognition of soybean canopy based on improved multi-view network

Abstract

Rapid and effective identification and diagnosis of soybean drought conditions is crucial for soybean yield and quality. Due to the complexity and diversity of agricultural environments, deep learning models based on three-dimensional data suffer from low accuracy and slow efficiency in practical applications, this paper proposes a three-dimensional image recognition method for soybean canopy based on an improved multi-view network. A lightweight network Res2net was used to reconstruct the feature extraction skeleton network in the MVCNN model, and the group convolution module of the network was optimized by embedding the ECA attention mechanism to propose a new three-dimensional image recognition model based on multi-view network (ECA-MVRes2net). In the study, drought soybeans were used as an example to obtain projected images of soybean canopy in six viewpoints using three-dimensional rotation and image feature theory, and the proposed ECA-MVRes2net was applied to carry out three-dimensional image recognition experiments of drought soybeans, and its recognition accuracy, F1 value and Kappa coefficient reached 96.665 %, 96.7 % and 0.924, respectively, compared with MVCNN, MVResnet, Pointnet++ and PointConv models with 3 evaluation metrics average improved by 17.289 %, 17.43 % and 0.356, respectively. The result realized a lightweight fast and accurate network model suitable for three-dimensional image recognition, which provides a theoretical foundation and technical support for the rapid recognition and accurate management of crops based on three-dimensional image processing.

Plant phenotyping relevance

乾燥状態という植物状態を対象に、3次元画像と改良マルチビュー深層学習モデルによる認識手法を開発・評価しており、表現型取得・推定が研究の中心である。

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