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 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.

MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction

“…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.…”

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

“…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).…”

Smart Energy Control Internet of Things based Agriculture Clustered Scheme for Smart Farming

“…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.…”

“…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.…”