Developing a Low-cost Digital Imaging System for Plant Phenotyping using Raspberry Pi Computers

Phenotyping allows the measurement of morphometric and physiological parameters of plants in a rapid, non-destructive, accurate, and high-throughput manner. Traditional phenotyping in breeding is time-consuming, labor-intensive, and the database is insufficient to satisfy the needs of plant breeders which hampers the breeding progress. Recent advancements in electronics, and sensor technologies in agriculture have aided in the development of innovative methods for measuring phenotypic characteristics. These sensor systems can provide a high spatial and temporal resolution data to characterize crop growth parameters within the diverse environmental condition. In this study the Raspberry Pi (RPi) -based sensor imaging system was integrated with a camera (RPi Sony 8MP) in growth chamber to analyze the crop growth conditions in wheat breeding trial for automated phenotypic application. The collected digital images were suitable for extracting measureable plant traits. The plant traits studied includes morphometric parameters such as plant density, canopy cover, leaf area index, and physiological parameters such as photosynthetic rate and biomass, which represents the plant growth and health. The developed low cost digital imaging system will be integrated with internet to facilitate internet-of-things (IoT) based sensor system which helps plant breeder to make timely decisions, screen elite cultivar and monitor crop in real-time.