{"id":77734,"date":"2020-09-29T14:18:11","date_gmt":"2020-09-29T11:18:11","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/automated-image-processing-could-aid-crop-evals\/"},"modified":"2020-09-29T14:18:11","modified_gmt":"2020-09-29T11:18:11","slug":"automated-image-processing-could-aid-crop-evals","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/automated-image-processing-could-aid-crop-evals\/","title":{"rendered":"#Automated image processing could aid crop\u00a0evals"},"content":{"rendered":"

“#Automated image processing could aid crop\u00a0evals<\/strong>”<\/p>\n

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Sunlight allows crops to engage photosynthesis and produce the yields that become food, feed, fiber and\u00a0fuel.<\/p>\n

That light gets captured by leaves. More upright leaves allow plants to use light more efficiently while casting less shade on neighbors, allowing growers to fit more plants into a field. Leaf angles also change when crops are deprived of water, making them a useful telltale for comparing how genetic lines respond to\u00a0drought.<\/p>\n

Unfortunately, measuring leaf angles is labor-intensive and time-consuming. Though automated systems exist, most work best in chambers that fail to mimic field\u00a0conditions.<\/p>\n

Nebraska’s James Schnable and colleagues developed an image-processing framework, Leaf Angle eXtractor, that quantifies leaf angles from time-lapse photography of plants. Experiments with corn and sorghum plants showed that Leaf Angle eXtractor could discern minute-to-minute shifts in individual leaves\u2014even from medium-resolution photos\u2014that corresponded with rolling, wilting and other common signs of water\u00a0deprivation.<\/p>\n

The framework could accelerate and reduce the cost of comparing how genetic lines respond to water stress in greenhouses, along with which varieties of corn and sorghum boast desirable leaf angles. Refining its ability to distinguish among individual plants under field conditions will rank as a future goal, the team\u00a0said.<\/p>\n

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\n A row of 6-megapixel cameras capturing time-lapse imagery of corn and sorghum at the Greenhouse Innovation Center. Credit: James Schnable
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Why plants in wetlands are highly productive\n <\/p><\/div>\n

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\n More information:<\/strong>
\n Sunil K. Kenchanmane Raju et al. Leaf Angle eXtractor: A high\u2010throughput image processing framework for leaf angle measurements in maize and sorghum, App<\/a>lications in Plant Science<\/a>s<\/i> (2020). DOI: 10.1002\/aps3.11385<\/a><\/p><\/div>\n
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\n University of Nebraska-Lincoln
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Citation<\/strong>:
\n Automated image processing could aid crop\u00a0evals (2020, September 29)
\n retrieved 29 September 2020
\n from https:\/\/phys.org\/news<\/a>\/2020-09-automated-image-aid-cropevals.html<\/p>\n

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\n part may be reproduced without the written permission. The content is provided for information purposes only.<\/p><\/div>\n<\/p><\/div>\n