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J Exp Bot. 2016 May;67(11):3587-99. doi: 10.1093/jxb/erw176. Epub 2016 May 3.

Image Harvest: an open-source platform for high-throughput plant image processing and analysis.

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University of Nebraska-Lincoln, Holland Computing Center, Lincoln, NE 68583, USA.
University of Nebraska-Lincoln, Department of Agronomy and Horticulture, Lincoln, NE 68583, USA.
University of Nebraska-Lincoln, Department of Agronomy and Horticulture, Lincoln, NE 68583, USA.


High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets.


High throughput computing; Open Science Grid; OpenCV; image analysis; image processing; large-scale biology; open-source software; phenomics.

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