Authors: Takashi Okayasu, Jiro Ito, Koichi Nomura, Daisuke Yasutake, Yukio Ozaki, Tadashige Iwao, Eiji Inoue, Yasumaru Hirai
Published in: CSBE-SCGAB Technical Conferences » 5th CIGR and AGM Quebec City 2021 » Regular Sessions
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Description: Plant growth characteristics are influenced not only by the gene properties but also by the ambient environmental condition and the nutritional status in general. Thus, a lot of methods have been developed in order to measure the field environmental information and the plant growth behavior. Among them, image processing and analyzing methods using visible (RGB), hyper (multi) spectrum, and IR images are used to extract plant growth features. Due to rapid expansion of recent IoT and AI technologies, they have been utilized to measure plant phenotype, which is focused on the comprehensive assessment of complex plant features such as growth, physical property, and yield. Especially, high-throughput plant phenotyping technologies are being actively investigated in the world and contribute to solve problems related to food and biomass production under drastic climate change, global warming, and the increase in the global population. However, typical systems are very expensive. Thus, development of the affordable system is needed to improve smart agriculture.
In this study, we developed a low cost plant phenotyping system using affordable IoT devices, and open source hardware and software. In this system, several sensors and cameras were incorporated into the measurement robot unit. The validity and performance were verified by real field cultivation test.
Keywords: plant phenotyping, affordable IoT devices, open source hardware and software
Conference name: 5th CIGR International Conference and CSBE-SCGAB AGM 2021, Quebec City,QC, 11-14 May 2021.
Session name: Monitoring, Control and Data Analysis 2
Publication type: Presentation
Language 1: en
Rights: Canadian Society for Bioengineering