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生菜田高速作物与杂草识别以实现精准除草。

High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding.

机构信息

School of Electrical and Electronic Engineering, The University of Manchester, Oxford Rd, Manchester M13 9PL, UK.

出版信息

Sensors (Basel). 2020 Jan 14;20(2):455. doi: 10.3390/s20020455.

Abstract

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.

摘要

精准除草可以显著减少甚至完全消除农业生产中除草剂的使用。为了实现对杂草的高精度、个别化、高速、低成本的识别,植物识别技术至关重要。我们的系统利用红、绿、近红外反射率,并结合尺寸差异化方法,用于识别生菜田中作物和杂草。系统使用 525nm、650nm 和 850nm 的 LED 阵列进行照明,并使用改装后的 RGB 相机进行单次拍摄获取图像。运动立体法用于补偿图像中的视差误差,并提供植物的准确位置数据。该系统在三个不同生长阶段(从 0.5 到 10km/h)的三个生菜田进行了实地试验验证。田间试验结果表明,杂草和作物的识别率分别为 56%和 69%。试验后处理的平均杂草和作物识别率分别为 81%和 88%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb81/7013443/7c35f545464b/sensors-20-00455-g001.jpg

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