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Automated Image Processing Could Aid Crop Evals

Automated Image Processing Could Aid Crop Evals

By Scott Schrage

Sunlight allows crops to engage photosynthesis and produce the yields that become food, feed, fiber and fuel.

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 drought.

Unfortunately, measuring leaf angles is labor-intensive and time-consuming. Though automated systems exist, most work best in chambers that fail to mimic field conditions.

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—even from medium-resolution photos—that corresponded with rolling, wilting and other common signs of water deprivation.

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Early last season in Western Australia’s Great Southern region, Wellstead Farming faced a dilemma in their oat crop after growing herbicide-tolerant canola the year before. Compounded by no opportunity for knockdown herbicide applications prior to a late April planting, volunteer canola in the furrows started to smother the oat plants. Potential crop impact from early herbicide application in oats can be a concern for many growers, and volunteer herbicide-tolerant canola can be hard to control, so we visited Cropping Manager Duncan Burt to find out the story and the end result.