To achieve a non-destructive detection of chlorophyll content in field crops based on the reflectance characteristics of chlorophyll in the visible and near-infrared spectrum (400 nm–1000 nm), a crop chlorophyll detector based on an interference filter optical sensor was designed. The hardware part of this detector mainly comprises a microcontroller unit, a sensor module, an input/output module, and a power module. The software is written in Python language and includes main functions, acquisition sub-functions, data processing sub-functions, and data storage sub-functions. Calibration and test experiments were carried out to evaluate the performance of the sensor. Results show that the sensor has a good responsivity of light intensity changes, so as to measure the reflected radiation from crops with the absorption of chlorophyll content. Field verification experiments of corn crops were also carried out, and chlorophyll content detecting models were built by using four combinations of characteristic wavelengths, including 3 peak bands, 9 bands selected via the stepwise regression analysis method, 8 bands selected via the Monte Carlo uninformed variable elimination method, and all 18 bands. Among them, the stepwise regression method obtained the best modeling results. The model showed better performance after calibration than before the calibration with R_C² of 0.72, R_V² of 0.61, RMSEc of 2.35 mg/L, and RMSEv of 2.43 mg/L. The crop chlorophyll detector based on the interference filter optical sensor was used for filed estimation of chlorophyll content which showed a potential for the analysis of crop growth differences.

为实现基于叶绿素在可见光和近红外光谱（400 nm-1000 nm）反射特性的大田作物叶绿素含量无损检测，设计了一种基于干涉滤光光学传感器的作物叶绿素检测仪。该探测器的硬件部分主要包括微控制器单元、传感器模块、输入/输出模块和电源模块。软件采用Python语言编写，包括主要功能、采集子功能、数据处理子功能、数据存储子功能。进行了校准和测试实验以评估传感器的性能。结果表明，该传感器对光强变化具有良好的响应性，可以利用叶绿素含量的吸收来测量作物的反射辐射。还进行了玉米作物的田间验证实验，利用特征波长的4个组合建立叶绿素含量检测模型，其中3个峰波段，9个波段通过逐步回归分析法选取，8个波段通过蒙特卡罗无信息变量选取消除法，以及所有 18 个波段。其中，逐步回归方法获得了最好的建模结果。该模型在校准后表现出比使用 R 校准前更好的性能 以及所有 18 个乐队。其中，逐步回归方法获得了最好的建模结果。该模型在校准后表现出比使用 R 校准前更好的性能 以及所有 18 个乐队。其中，逐步回归方法获得了最好的建模结果。该模型在校准后表现出比使用 R 校准前更好的性能_C ²为0.72，R _V ²为0.61，RMSEc 为2.35 mg/L，RMSEv 为2.43 mg/L。基于干涉滤光光学传感器的作物叶绿素检测器用于叶绿素含量的现场估计，显示出用于分析作物生长差异的潜力。