基于光环境校正的便携作物叶绿素检测仪设计
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国家自然科学基金项目(31971785)、山东省烟台市校地融合项目(2020XDRHXMPT35)、中央高校基本科研业务费项目(2022TC053)和中国农业大学研究生教改项目(JG2019004、JG202026、QYJC202101、JG202102)


Design of Portable Crop Chlorophyll Detector with Ambient Light Correction
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    为快速获取作物的生长状态信息及时指导农业生产,基于作物生理生化光谱学响应机理,设计了基于光环境校正的便携作物叶绿素检测装置。装置测量以610、680、730、760、810、860nm为中心,20nm带宽的反射光谱以及环境光照光谱数据,计算植被指数并预测植物叶绿素含量,在环境光照强度较差时使用主动补光灯进行补光,并对补光条件下环境光照强度进行校正。实验表明GPS定位在纬度最大漂移为6.2m、经度最大漂移为4.9m;光谱传感器6个波段的光强响应与照度计测量值之间的决定系数均超过0.99;标定的2块光谱传感器的匹配系数在610nm和860nm波段分别为0.743、1.035。建立了610nm和860nm波段补光强度与测量距离间的拟合模型用于光环境校正;使用无纺布进行了叶绿素梯度实验,建立了植被指数NDVI与植物叶绿素含量的数学模型,在较差光环境条件下不进行补光的模型决定系数为0.685,补光并进行校正情况下模型决定系数为0.965。

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    Rapid acquisition of crop growth status information is essential for timely guidance of agricultural production. Based on the response mechanism of crop physiological and biochemical spectroscopy, a portable crop chlorophyll detection device based on ambient light correction was designed. The device measured reflectance spectral and ambient light spectral data with 20nm bandwidth centered at 610nm, 680nm, 730nm, 760nm, 810nm, and 860nm to calculate vegetation index and predict plant chlorophyll content. The features of the device were the supplemental light when the ambient light intensity was poor and the correction of the ambient light intensity under the supplemental light condition. To evaluate the sensor performance, the sensor was tested and calibrated. Experiments showed that the maximum difference in GPS positioning was 6.2m in latitude and 4.9m in longitude;the correlation between the light intensity response of the six bands of the spectral sensors and the measured values of the illuminance meter exceeded 0.99;the matching coefficients of the two spectral sensors were calibrated to 0.743 and 1.035 in the 610nm and 860nm bands, respectively. A fitting model between the supplemental light intensity and the measurement distance in the 610nm and 860nm bands was established for light environment correction;chlorophyll gradient experiments were conducted using nonwoven fabrics, and a mathematical model of NDVI vegetation index and plant chlorophyll content was established, with a model R2 of 0.685 under poor light environment conditions without supplemental light and a model R2 of 0.965 under supplemental light and with correction.

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唐伟杰,王楠,刘国辉,赵若梅,李民赞,孙红.基于光环境校正的便携作物叶绿素检测仪设计[J].农业机械学报,2022,53(s1):249-256. TANG Weijie, WANG Nan, LIU Guohui, ZHAO Ruomei, LI Minzan, SUN Hong. Design of Portable Crop Chlorophyll Detector with Ambient Light Correction[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(s1):249-256.

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  • 收稿日期:2022-06-25
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  • 在线发布日期: 2022-11-10
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