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Maize plant height automatic reading of measurement scale based on improved YOLOv5 lightweight model

文献类型： 外文期刊

作者： Li, Jiachao 1 ; Zhou, Ya'nan 1 ; Zhang, He 1 ; Pan, Dayu 1 ; Gu, Ying 1 ; Luo, Bin 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Xinjiang Agr Univ, Coll Mech & Elect Engn, Urumqi, Xinjiang, Peoples R China

4.Northeast Agr Univ, Coll Agr, Harbin, Heilongjiang, Peoples R China

关键词： Plant height measurement; Deep learning; Neural network; Attention mechanism

期刊名称：PEERJ COMPUTER SCIENCE （ 2023影响因子：3.5； 五年影响因子：3.4 ）

ISSN：

年卷期： 2024 年 10 卷

页码：

收录情况： SCI

摘要： Background: Plant height is a significant fi cant indicator of maize phenotypic morphology, and is closely related to crop growth, biomass, and lodging resistance. Obtaining the maize plant height accurately is of great significance fi cance for cultivating high-yielding maize varieties. Traditional measurement methods are labor-intensive and not conducive to data recording and storage. Therefore, it is very essential to implement the automated reading of maize plant height from measurement scales using object detection algorithms. Method: This study proposed a lightweight detection model based on the improved YOLOv5. The MobileNetv3 network replaced the YOLOv5 backbone network, and the Normalization-based Attention Module attention mechanism module was introduced into the neck network. The CioU loss function was replaced with the EioU loss function. Finally, a combined algorithm was used to achieve the automatic reading of maize plant height from measurement scales. Results: The improved model achieved an average precision of 98.6%, a computational complexity of 1.2 GFLOPs, and occupied 1.8 MB of memory. The detection frame rate on the computer was 54.1 fps. Through comparisons with models such as YOLOv5s, YOLOv7 and YOLOv8s, it was evident that the comprehensive performance of the improved model in this study was superior. Finally, a comparison between the algorithm's ' s 160 plant height data obtained from the test set and manual readings demonstrated that the relative error between the algorithm's ' s results and manual readings was within 0.2 cm, meeting the requirements of automatic reading of maize height measuring scale.

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