ArticleGigaScience2019
CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management.
Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of IoT Solutions for Smart Farming.Sensors (Basel, Switzerland) · 2020Pooled it
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Article
- Advancing radiation-induced mutant screening through high-throughput technology: a preliminary evaluation of mutant screening in Arabidopsis thaliana.Plant methods · 2025Article
- Research on the impact and mechanism of digital economy on China's food production capacity.Scientific reports · 2024Article
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework.Plant phenomics (Washington, D.C.) · 2024Article
- PlantInfoCMS: Scalable Plant Disease Information Collection and Management System for Training AI Models.Sensors (Basel, Switzerland) · 2023Article
- Review
- High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants.Plant phenomics (Washington, D.C.) · 2023Article
- CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones.Frontiers in plant science · 2023Article
- A low-cost and open-source solution to automate imaging and analysis of cyst nematode infection assays for Arabidopsis thaliana.Plant methods · 2022Article
- A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt.Scientific reports · 2022Article
- A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping.Phenomics (Cham, Switzerland) · 2022Review
- Development of a plant conveyance system using an AGV and a self-designed plant-handling device: A case study of DIY plant phenotyping.Breeding science · 2022Article
- The field phenotyping platform's next darling: Dicotyledons.Frontiers in plant science · 2022Review
- Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat.Plant physiology · 2021Article
- Applications of Multi-Omics Technologies for Crop Improvement.Frontiers in plant science · 2021Review
- KAT4IA:Plant phenomics (Washington, D.C.) · 2021Article
- Advances in genome-wide association studies of complex traits in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundHigh-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales.
findingsCropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralized data management. Data transfer and annotation are accomplished automatically through an hypertext transfer protocol-accessible RESTful API installed on both device side and server side of the CropSight system, which synchronize daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance while different experiments are being conducted.
conclusionsAs a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.