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/lp/springer-journals/proceedings-of-the-forum-math-for-industry-2019-development-of-plant-2ZEWx48ZeS
- Publisher
- Springer Nature Singapore
- Copyright
- © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
- ISBN
- 978-981-19-1153-8
- Pages
- 109 –116
- DOI
- 10.1007/978-981-19-1154-5_6
- Publisher site
- See Chapter on Publisher Site
Abstract
[Recently, smart agriculture has been extensively investigated in Japan to solve the problems of ageing of farmers, abnormal weather and food diversity of consumers. Plant behaviour monitoring and analysis are required for advancing the technology and developing new varieties of plants to address current issues. In this study, a plant phenotyping platform was developed using affordable devices to measure and collect plant growth features automatically. An RGB-D sensor was utilised to measure the three-dimensional shape details of plants. Augmented reality (AR) markers were adopted as the position unrecognition and control, and their accuracy was sufficiently high (less than 3 mm). The validity of the operation rate and the measurement accuracy of plant growth features were investigated based on actual cultivation tests. The operating rate was approximately 90% throughout the cultivation period. In addition, the plant height was calculated using depth information, and its spatial distribution was manually estimated based on the plants’ height.]
Published: Sep 11, 2022
Keywords: Plant phenotyping; Computer vision; RGB-D sensor; Unmanned driving system; Smart agriculture