Application status and challenges of machine vision in plant factory-A review

Plant factories have a great potential for mitigating the contradiction between the world's growing population and food scarcity. During the process of its automatic production, machine vision plays a significant role. This technique almost covers every production link from raising seedlings, transplanting, management, and harvesting to fruit grading. To pro-vide references and a starting point for those who are committed to studying this issue. In this paper, the application prospects of machine vision in plant factories were analyzed, and the present researches were summarized from the fields of plant growth monitoring, robot operation assistance, and fruit grading. The results found that although the existing methods have solved some practical problems at low cost, high efficiency and precision, some challenges still are faced by machine vision. Firstly, the changing lighting, complex backgrounds, and color similarity within plant different parts cause the commonly used image segmentation algorithms to fail. The shortage of standard agricultural datasets also keeps deep learning and unsupervised classification algorithms from making progress. Secondly, there are some theoretical knowledge gaps for machine vision application in a particular environment of plant factories, which seriously contains its application effect. Thirdly, the lack of special image acquisition devices and supporting facilities resulted in poor image quality. All these factors hinder machine vision application in plant factories. Nevertheless, it is still a powerful tool and irreplaceable at present. We believed that this technique would promote plant factory development greatly with more robust, efficient, and reliable algorithms are developed in the future.(c) 2021 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).