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A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks

文献类型： 外文期刊

作者： Zhou, Chengquan 1 ; Hu, Jun 1 ; Xu, Zhifu 1 ; Yue, Jibo 2 ; Ye, Hongbao 1 ; Yang, Guijun 3 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou, Peoples R China

2.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing, Peoples R China

3.Minist Agr PR China, Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Beijing, Peoples R China

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

关键词： growth monitoring; deep learning; improved ResNet; freshness grading; ground-based imaging system

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.753； 五年影响因子：6.612 ）

ISSN： 1664-462X

年卷期： 2020 年 11 卷

页码：

收录情况： SCI

摘要： Achieving the non-contact and non-destructive observation of broccoli head is the key step to realize the acquisition of high-throughput phenotyping information of broccoli. However, the rapid segmentation and grading of broccoli head remains difficult in many parts of the world due to low equipment development level. In this paper, we combined an advanced computer vision technique with a deep learning architecture to allow the acquisition of real-time and accurate information about broccoli head. By constructing a private image dataset with 100s of broccoli-head images (acquired using a self-developed imaging system) under controlled conditions, a deep convolutional neural network named "Improved ResNet" was trained to extract the broccoli pixels from the background. Then, a yield estimation model was built based on the number of extracted pixels and the corresponding pixel weight value. Additionally, the Particle Swarm Optimization Algorithm (PSOA) and the Otsu method were applied to grade the quality of each broccoli head according to our new standard. The trained model achieved an Accuracy of 0.896 on the test set for broccoli head segmentation, demonstrating the feasibility of this approach. When testing the model on a set of images with different light intensities or with some noise, the model still achieved satisfactory results. Overall, our approach of training a deep learning model using low-cost imaging devices represents a means to improve broccoli breeding and vegetable trade.

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