Crowdsourcing Image Analysis for Plant Phenomics to Generate Ground Truth Data for Machine Learning
| Autor: | Nigel Lee, Jonathan W. Kelly, Zachary D. Siegel, Carson M. Andorf, Dan Nettleton, Scott Zarecor, Iddo Friedberg, Baskar Ganapathysubramanian, Darwin A. Campbell, Naihui Zhou, Carolyn J. Lawrence-Dill |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
0106 biological sciences Computer science Image Processing Pilot Projects Plant Science Plant Genetics computer.software_genre 01 natural sciences Field (computer science) Food Supply Task (project management) Machine Learning 0302 clinical medicine Image Processing Computer-Assisted lcsh:QH301-705.5 media_common 2. Zero hunger 0303 health sciences Ground truth Ecology Applied Mathematics Simulation and Modeling Eukaryota Agriculture Plants Data Accuracy Phenotypes Phenotype Computational Theory and Mathematics Experimental Organism Systems Research Design Modeling and Simulation Physical Sciences Engineering and Technology Crowdsourcing The Internet Algorithms Research Article Crops Agricultural Computer and Information Sciences Best practice media_common.quotation_subject Crops Context (language use) Research and Analysis Methods Machine learning Cellular and Molecular Neuroscience Machine Learning Algorithms 03 medical and health sciences Model Organisms Plant and Algal Models Artificial Intelligence Genetics Humans Quality (business) Grasses Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology Crop Genetics Internet business.industry Organisms Biology and Life Sciences Pilot Studies 15. Life on land Maize 030104 developmental biology lcsh:Biology (General) Data quality Signal Processing Artificial intelligence business computer 030217 neurology & neurosurgery Mathematics Crop Science 010606 plant biology & botany |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 14, Iss 7, p e1006337 (2018) |
| DOI: | 10.1101/265918 |
| Popis: | The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to generate a large number of training data of good quality. We explore an image analysis task involving the segmentation of corn tassels from images taken in a field setting. We investigate the accuracy, speed and other quality metrics when this task is performed by students for academic credit, Amazon MTurk workers, and Master Amazon MTurk workers. We conclude that the Amazon MTurk and Master Mturk workers perform significantly better than the for-credit students, but with no significant difference between the two MTurk worker types. Furthermore, the quality of the segmentation produced by Amazon MTurk workers rivals that of an expert worker. We provide best practices to assess the quality of ground truth data, and to compare data quality produced by different sources. We conclude that properly managed crowdsourcing can be used to establish large volumes of viable ground truth data at a low cost and high quality, especially in the context of high throughput plant phenotyping. We also provide several metrics for assessing the quality of the generated datasets. Author summary Food security is a growing global concern. Farmers, plant breeders, and geneticists are hastening to address the challenges presented to agriculture by climate change, dwindling arable land, and population growth. Scientists in the field of plant phenomics are using satellite and drone images to understand how crops respond to a changing environment and to combine genetics and environmental measures to maximize crop growth efficiency. However, the terabytes of image data require new computational methods to extract useful information. Machine learning algorithms are effective in recognizing select parts of images, but they require high quality data curated by people to train them, a process that can be laborious and costly. We examined how well crowdsourcing works in providing training data for plant phenomics, specifically, segmenting a corn tassel—the male flower of the corn plant—from the often-cluttered images of a cornfield. We provided images to students, and to Amazon MTurkers, the latter being an on-demand workforce brokered by Amazon.com and paid on a task-by-task basis. We report on best practices in crowdsourcing image labeling for phenomics, and compare the different groups on measures such as fatigue and accuracy over time. We find that crowdsourcing is a good way of generating quality labeled data, rivaling that of experts. |
| Databáze: | OpenAIRE |
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