2019
DOI: 10.1093/gigascience/giz009
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CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management

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 appli… Show more

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Cited by 60 publications
(25 citation statements)
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“…Data Management System. Phenotyping platforms generally acquire a large number of biological datasets, most of which cannot be processed into agronomic phenotypic traits immediately; thus, phenotyping databases [47] or information management systems [48,49] are required to support data management. Huge amounts of data can be generated using the MVS-Pheno platform ( Figure 5), including agronomic information about the designed experiments, MVS 8 Plant Phenomics image sequences acquired during the device use, reconstructed 3D point clouds of maize shoots, extracted shoot skeletons, and phenotypic traits derived using the software.…”
Section: Phenotype Extraction Of Individual Plants Bymentioning
confidence: 99%
“…Data Management System. Phenotyping platforms generally acquire a large number of biological datasets, most of which cannot be processed into agronomic phenotypic traits immediately; thus, phenotyping databases [47] or information management systems [48,49] are required to support data management. Huge amounts of data can be generated using the MVS-Pheno platform ( Figure 5), including agronomic information about the designed experiments, MVS 8 Plant Phenomics image sequences acquired during the device use, reconstructed 3D point clouds of maize shoots, extracted shoot skeletons, and phenotypic traits derived using the software.…”
Section: Phenotype Extraction Of Individual Plants Bymentioning
confidence: 99%
“…To manage and integrate the extensive amounts of data from multioptical and other sensors, Wilkinson et al (2016) proposed the FAIR (findable, available, identifiable, and reusable) principle to allow the finding and reuse of data across different individuals or groups, which means all the necessary metadata, such as resource and data acquisition information, measurement protocols, data description, and environmental conditions, should be clearly addressed and capable of being accessed. According to the FAIR principle, several efforts have also been made for data management and analysis, for example, PHENOPSIS DB (Fabre et al, 2011) and CropSight (Reynolds et al, 2019a).…”
Section: Image Data Analysis and Big Data Organizationmentioning
confidence: 99%
“…Wherever E residual is node level remaining energy and where E initial the initial is level energy assigned. Therefore, the optimal number of clusters m opt could be written as = √ 4 (2 −1) 0 − (5) In this equation the diameter of network is represented by X whereas E 0 is the initial source of energy for each node. For the current round when cluster heads are chosen, they communicate their CH selection message to other nodes in the same clusters then non cluster head nodes examine the message signal strength and decide the cluster heads to enter.…”
Section: E Routing Phasementioning
confidence: 99%
“…Therefore IoT system can minimize the wastage of crops, efficient use of resources such as water and fertilizers and improve the crop yield and reduce operational expenses [4]. IoT networks for monitoring of farm environment should be of low-cost, making it affordable for farmers and should use low energy for prolong life of the network [5]. There are many sensor nodes in a typical monitoring network, a few sink nodes and a gateway www.ijacsa.thesai.org depending on the topology of the network and farm clustering.…”
Section: Introductionmentioning
confidence: 99%