A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

Bai, Geng; Ge, Yufeng; Hussain, Waseem; Baenziger, P. Stephen; Graef, George L.

doi:10.1016/j.compag.2016.08.021

Cited by 210 publications

(156 citation statements)

References 33 publications

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“…The first one is the limited space in the phytotron, which make more complex the process of data acquisition at advanced growth stages. Furthermore, artificial lighting can alter pattern of plant growth and development [9].…”

Section: Methodsmentioning

confidence: 99%

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Cloud architecture for digital phenotyping and automation

Debauche

Mahmoudi

Manneback

et al. 2017

2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech)

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Abstract-Digital phenotyping presents a very important tool for scientists to measure with high accuracy the effects of external phenomena on plant development. Plant phenotyping is mainly based on imaging techniques. However, the number of images and parameters used to store and treat these parameters are continuously growing. Consequently, the high-throughput of data and the need of specific treatment in real or near real-time requires a large quantity of resources. Moreover, the increasing amount of particular phenotyping case studies needs the development of specific application. Cloud architectures offers means to store a wide range of numerous data and host a large quantity of specific software to process these data. In this paper, we propose a new approach that shows how logic synthesis works to match digital phenotyping need and cloud possibilities in a lambda cloud architecture in order to store and treat this important amount of data in real time. We also suggest a data platform allowing to host applications and access to the stored data within the lambda architecture. The present application platform allows to use several frameworks with a fine-grained resource use of the cluster. Finally, we develop a case study in a controlled environment system (growth chamber) where we grow basil plants.

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Section: Methodsmentioning

confidence: 99%

“…Apache Mesos is a fault-tolerant and highly available sharing layer that provides a framework common interface allowing a fine-grained sharing across diverse cluster computing frameworks. Fault tolerance is ensured by Apache Zookeper [9]. Mesos offers a scalable and resilient core for enabling various frameworks.…”

Section: Related Workmentioning

confidence: 99%

Cloud architecture for digital phenotyping and automation

Debauche

Mahmoudi

Manneback

et al. 2017

2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech)

View full text Add to dashboard Cite

show abstract

“…For instance, a number of studies assume a controlled greenhouse condition [9]. Use of different hardware setups and fundamentally different algorithms for monitoring different crop stresses is also another problem in the reported work [10], [11]. For the solution to be optimal, it should be easily adaptable in different situations with minimal changes.…”

Section: Issn: 2319 -1058mentioning

confidence: 99%

Suitable Image Features for Drought Stress Dection In Beans Using Raspberry Pi Imaging System

2017

IJIET

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-Crop stresses are often detected late and this leads to massive crop yield loss. There exists a need for development of a real time system for monitoring crop growth progress with a view of early detection of diseases and other forms of crop stress. In this work, a simple raspberry pi imaging system is developed and used to capture images of beans subjected to drought stress. The images are used to extract features for classification of the stress. The features are then used to train classifiers for drought stress detection. Detection accuracy of between 90 to 100% has been achieved.

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“…Bai et al [78] mounted NDVI sensors and portable spectrometers on a field platform to measure chlorophyll. The sensors and spectrometers are all consisted of an up-looking unit to measure solar radiation and a down-looking unit to detect reflected spectrum.…”

Section: Chlorophyll Measurementsmentioning

confidence: 99%

“…Typically, thermometer, thermography, spectral sensors and cameras are implemented to evaluate water stress, the related methods will be described as follow. 4.2.1 Water stress measurement based on thermometer Thermometers can be mounted on some phenotyping platforms to monitor plant canopy temperature [78,84] , whose measuring region is concentrated and small that contribute to reduce interference. Although ambient light does not affect thermometers due to its measured radiations are in the long-infrared, ambient temperature has a significantly effect on thermometers reading.…”

Section: Water Stress Measurementsmentioning

confidence: 99%

Sensors for measuring plant phenotyping: A review

Qiu¹,

Wei²,

Zhang³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Food crisis is a matter of prime importance because it becomes more severe as the global population grows. Among the solutions to this crisis, breeding is deemed one of the most effective ways. However, traditional phenotyping in breeding is time consuming and laborious, and the database is insufficient to meet the requirements of plant breeders, which hinders the development of breeding. Accordingly, innovations in phenotyping are urgent to solve this bottleneck. The morphometric and physiological parameters of plant are particularly interested to breeders. Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters. This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years. Some parameters that have been measured in phenotyping are introduced and discussed, including plant height, leaf parameters, in-plant space, chlorophyll, water stress, and biomass. And the measurement methods of each parameter with different sensors were classified and compared. Some comprehensive measurement platforms were also summarized, which are able to measure several parameters simultaneously. Besides, some deficiencies of phenotyping should be addressed, and novel methods should be proposed to reduce cost, improve efficiency, and promote phenotyping in the future.

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A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

Cited by 210 publications

References 33 publications

Cloud architecture for digital phenotyping and automation

Cloud architecture for digital phenotyping and automation

Suitable Image Features for Drought Stress Dection In Beans Using Raspberry Pi Imaging System

Sensors for measuring plant phenotyping: A review

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