Zhou Chengquan, Hu Jun, Xu Zhifu, Yue Jibo, Ye Hongbao, Yang Guijun
Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Food science institute, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Front Plant Sci. 2020 Jun 3;11:559. doi: 10.3389/fpls.2020.00559. eCollection 2020.
The automated harvesting of strawberry brings benefits such as reduced labor costs, sustainability, increased productivity, less waste, and improved use of natural resources. The accurate detection of strawberries in a greenhouse can be used to assist in the effective recognition and location of strawberries for the process of strawberry collection. Furthermore, being able to detect and characterize strawberries based on field images is an essential component in the breeding pipeline for the selection of high-yield varieties. The existing manual examination method is error-prone and time-consuming, which makes mechanized harvesting difficult. In this work, we propose a robust architecture, named "improved Faster-RCNN," to detect strawberries in ground-level RGB images captured by a self-developed "Large Scene Camera System." The purpose of this research is to develop a fully automatic detection and plumpness grading system for living plants in field conditions which does not require any prior information about targets. The experimental results show that the proposed method obtained an average fruit extraction accuracy of more than 86%, which is higher than that obtained using three other methods. This demonstrates that image processing combined with the introduced novel deep learning architecture is highly feasible for counting the number of, and identifying the quality of, strawberries from ground-level images. Additionally, this work shows that deep learning techniques can serve as invaluable tools in larger field investigation frameworks, specifically for applications involving plant phenotyping.
草莓的自动化采收带来了诸多益处，如降低劳动力成本、可持续性、提高生产率、减少浪费以及更好地利用自然资源。温室内草莓的精确检测可用于辅助草莓采收过程中对草莓的有效识别和定位。此外，基于田间图像检测和表征草莓是高产品种选育育种流程中的一个重要组成部分。现有的人工检查方法容易出错且耗时，这使得机械化采收变得困难。在这项工作中，我们提出了一种名为“改进的Faster-RCNN”的强大架构，用于检测由自主研发的“大场景相机系统”拍摄的地面RGB图像中的草莓。本研究的目的是开发一种针对田间条件下活体植物的全自动检测和饱满度分级系统，该系统不需要任何关于目标的先验信息。实验结果表明，所提出的方法获得了超过86%的平均果实提取准确率，高于使用其他三种方法所获得的准确率。这表明图像处理与引入的新型深度学习架构相结合，对于从地面图像中统计草莓数量和识别草莓品质非常可行。此外，这项工作表明深度学习技术可以作为更大规模田间调查框架中的宝贵工具，特别是对于涉及植物表型分析的应用。
