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Machine vision: a method for the future to automatically recognize weeds from plants?

机译:机器视觉:未来自动识别植物杂草的方法吗?

摘要

Weeds compete with crop plants for sunlight, moisture and nutrients and can have a detrimental impact on crop yields and quality if uncontrolled. They are destroyed by chemical, non chemical and integrated methods. To perform a site-specific weeds destruction, combination of these techniques with ground-based machine vision technology has high potential. Several methods exist to differentiate weeds from soil, between the rows. The more complicated problem is encountered when weeds are mixed to crops within the rows. Algorithms based on colorimetric or shape features are widely dependant on the variability of weeds and crops and are difficult to transpose from one situation to another. Measurement of plant height is a promising method, since at low spatial scale, the growthing speed is more uniform for the plants than for the weeds. This growing speed is function of the height and of a characteristic time, such as the number of days after sowing. To implement this method, active stereoscopy combined to an accurate measurement of the soil microrelief is required.
机译:杂草与农作物竞争阳光,水分和养分,如果不受控制,杂草会对农作物的产量和质量产生不利影响。它们通过化学,非化学和综合方法销毁。为了执行特定地点的杂草破坏,将这些技术与基于地面的机器视觉技术相结合具有很高的潜力。行之间存在几种区分杂草与土壤的方法。将杂草混入行中的作物时会遇到更复杂的问题。基于比色或形状特征的算法在很大程度上取决于杂草和农作物的变异性,并且很难从一种情况转换为另一种情况。测量植物高度是一种有前途的方法,因为在低空间尺度上,植物的生长速度比杂草的生长速度更均匀。这种生长速度是高度和特征时间(例如播种后的天数)的函数。要实施此方法,需要将主动立体镜结合到土壤微浮雕的准确测量上。

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