CropQuant: An automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture

Abstract

Automated phenotyping technologies are capable of providing continuous and precise measurements of traits that are key to today’s crop research, breeding and agronomic practices. In additional to monitoring developmental changes, high-frequency and high-precision phenotypic analysis can enable both accurate delineation of the genotype-to-phenotype pathway and the identification of genetic variation influencing environmental adaptation and yield potential. Here, we present an automated and scalable field phenotyping platform called CropQuant, designed for easy and cost-effective deployment in different environments. To manage infield experiments and crop-climate data collection, we have also developed a web-based control system called CropMonitor to provide a unified graphical user interface (GUI) to enable realtime interactions between users and their experiments. Furthermore, we established a high-throughput trait analysis pipeline for phenotypic analyses so that lightweight machine-learning modelling can be executed on CropQuant workstations to study the dynamic interactions between genotypes (G), phenotypes (P), and environmental factors (E). We have used these technologies since 2015 and reported results generated in 2015 and 2016 field experiments, including developmental profiles of five wheat genotypes, performance-related traits analyses, and new biological insights emerged from the application of the CropQuant platform.

CropSurveyor: a scalable open-source experiment management system for distributed plant phenotyping and IoT-based crop management

“…Hence, our solution is to establish an ad-hoc and self-managed network through USB WiFi dongles mounted on IoT devices, e.g. a distributed CropQuant phenotyping workstation [21], so CropSurveyor can transfer data between distributed IoT devices and a central server. The self-managed network can be either a Star or a Mesh network topology, enabling peer-to-peer HTTP accessing points to network IoT devices for data calibration and synchronisation in the field (Fig.…”

“…The system provides a unified web interface for users to oversee data collection, calibration and storage on a regular basis. Through our three-year wheat prebreeding experiments (2016-2018) [21], a powerful visualisation component and a flexible data/experiment management solution has been established. Equipped with CropSurveyor, users can now closely monitor different experiments, ongoing and historic, running in different locations.…”

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

“…Shibayama et al (2015a) used two digital cameras at a height of 12 m above a rice field to evaluate the nitrogen content and leaf area index of paddy rice (Shibayama et al, 2015b). Zhou et al (2017) developed a scalable and cost-effective field phenotyping platform (CropQuant), a web-based control system (CropMonitor), and a high-throughput trait analysis pipeline to measure dynamic crop growth, vegetative greenness, and plot orientation. The advantage of the use of the fixed phenotyping tower was that it was easy to install and maintain, whereas the disadvantage was that limited crop information in fixed areas was obtained, and costs for large-scale experiments increase in a near-linear fashion.…”

SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks

“…CropQuant Dataset [27] We used 15 high-dimensional RGB image series of 6 × 1.5 metre wheat plots collected at Norwich Research Park (NRP) between May and July 2016. The image series covers one growing stage: flowering.…”

“…The application of the internet of things (IoT) in agriculture has enabled the monitoring of crop growth through networked remote sensors and non-invasive imaging devices [7,27]. Analysis of the output of such systems with machine learning and image processing techniques can help to extract meaningful information to assist crop management.…”