Abstract
Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.
Highlights
Plant growth and development are dynamic in nature, and phenotypes result from the cumulative effect of genetic and environmental factors’ interactions through the entire plant life cycle [1]
Traditional field phenotyping in crop breeding programs and genetics/genomics research can be laborious, time-consuming, and subjective
The low-cost sensor systems with dual cameras assembled from broadly available hardware operating on open-source software enabling tasks for continuous crop monitoring, especially for in-field crop evaluation, which is essential for field phenotyping; Camera operation script and automated trait analysis script integrated into the sensor system are open-source and expandable software based on community-driven numeric and scientific libraries, which are freely available and accessible
Summary
Plant growth and development are dynamic in nature, and phenotypes result from the cumulative effect of genetic and environmental factors’ interactions through the entire plant life cycle [1]. Quantifying plant phenotypes associated with different plant genetics under diverse environmental circumstances can improve the understanding of genotype-environment interactions (G × E). This concept is applied for plant variety selection in standard crop breeding schemes [2,3]. Traditional field phenotyping in crop breeding programs and genetics/genomics research can be laborious, time-consuming, and subjective. It is quite often with limited access to environmental data, which is needed to integrate with phenotypic data for in-depth analysis [9,10]
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