{"id":1048,"date":"2020-03-09T09:09:01","date_gmt":"2020-03-09T09:09:01","guid":{"rendered":"http:\/\/www.plant-phenomics.ac.uk\/?p=1048"},"modified":"2020-03-09T09:09:09","modified_gmt":"2020-03-09T09:09:09","slug":"deeppod-a-convolutional-neural-network-based-quantification-of-fruit-number-in-arabidopsis","status":"publish","type":"post","link":"https:\/\/www.plant-phenomics.ac.uk\/index.php\/2020\/03\/09\/deeppod-a-convolutional-neural-network-based-quantification-of-fruit-number-in-arabidopsis\/","title":{"rendered":"DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis"},"content":{"rendered":"\n

High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addressed. Advances in sensor technology have led to the increasing use of imaging to monitor and measure a range of plants including the model Arabidopsis thaliana<\/em>. These extensive datasets contain diverse trait information, but feature extraction is often still implemented using approaches requiring substantial manual input.<\/p>\n

https:\/\/academic.oup.com\/gigascience\/article\/9\/3\/giaa012\/5780255<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. 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