College of Agriculture, Nanjing Agriculture University, Nanjing 210095, China.
National Engineering and Technology Center for Information Agriculture, Nanjing 210095, China.
Sensors (Basel). 2018 Sep 17;18(9):3129. doi: 10.3390/s18093129.
To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (²) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, ² were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, ² of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively.
为了以低成本、高速率无损获取叶片氮含量（LNC）、叶片氮积累量（LNA）、叶面积指数（LAI）和叶片干重（LDW）数据，根据作物生长的光谱监测机制，开发了一种用于作物生长监测和诊断（CGMD）的便携式设备。根据作物冠层特征和田间作业环境的实际要求，确定了传感器的分光光束和适当的结构参数。同时，设计了一种积分式弱光电信号处理电路，该电路通过自动调整积分周期来改变系统增益，保证了仪器的高分辨率，该积分周期基于接收到的环境光辐照度进行调整。此外，还开发了一种基于单片机的传感器信息和生长模型的耦合处理系统。田间试验表明，CGMD 仪器和 ASD 光谱仪分别测量的归一化植被指数（NDVI）具有良好的线性相关性。在测量水稻和小麦冠层反射光谱时，其线性决定系数（²）分别为 0.95 和 0.92，均方根误差（RMSE）分别为 0.02 和 0.03。CGMD 仪器测量的 NDVI 值与水稻和小麦的生长指数呈线性关系。基于 NDVI 线性拟合的水稻 LNC、LNA、LAI 和 LDW 监测模型，²分别为 0.64、0.67、0.63 和 0.70，RMSE 分别为 0.31、2.29、1.15 和 0.05。此外，基于 NDVI 线性拟合的小麦 LNC、LNA、LAI 和 LDW 监测模型的²分别为 0.82、0.71、0.72 和 0.70，RMSE 分别为 0.26、2.30、1.43 和 0.05。
