Measurement Method of Plant Phenotypic Parameters Based on Image Deep Learning

Author:

Dong Mo1,Yu Haiye1,Zhang Lei1,Wu Mingzhi2,Sun Zhipeng2,Zeng Dequan2,Zhao Ruohan3ORCID

Affiliation:

1. College of Biological and Agricultural Engineering, Jilin University, Changchun, 130022 Jilin, China

2. Nanchang Automotive Institution of Intelligence & New Energy, Nanchang, 330052 Jiangxi, China

3. Mudanjiang Medical University, Mudanjiang, 157000 Heilongjiang, China

Abstract

This article applies deep learning and electromechanical technology to plant phenotype measurement. First, an electromechanical device is designed to collect plant phenotype images, which solves the difficulty of collecting deep learning training data. The data set required for deep learning model training for plant phenotype detection is made by an automated method. This paper takes the Lactuca sativa plant image as an example and uses the ASM-based data enhancement method to solve the problem of insufficient image data of Lactuca sativa leaf pests and effectively avoid the phenomenon of overfitting. The plant image recognition method based on deep learning proposed breaks through the limitations of plant local feature recognition, gets rid of the limitation of highly specialized data collection, lowers the threshold of plant image recognition, and has advantages in recognition speed and accuracy. This method requires a large amount of training data. In the future, we can explore the collection of massive plant pictures from the Internet as a training set to achieve rapid iteration and optimization of the model.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant;Journal of Computer Networks and Communications;2024-02-27

2. Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge;International Journal of Agricultural and Biological Engineering;2024

3. Research advance in phenotype detection robots for agriculture and forestry;International Journal of Agricultural and Biological Engineering;2023

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