In order to implement the agricultural IoT systems of chlorophyll dynamic monitoring the function, a visiblenear infrared (660nm, 880nm) band spectral module was designed with the characteristics of small volume and low power consumption for the chlorophyll content detection in plants. Through AD conversion circuit, digital filter circuit was designed to get the blade reflected light digital signal. The reflectivity of reflected light signal was calibrated by gray scale plate, the R2 of the reflectivity correction model at 660nm and 880nm were 0.9996 and 09995, respectively. A total of 80 samples of 10 different grades were taken, and the chlorophyll content was detected by national standard method. The solution was poured into nonwoven cloth and measured by chlorophyll detection module. The normalized vegetation index (NDVI) value and soil and plant analyzer development (SPAD) value were obtained by the calculation of dual bands spectral reflectance, and the corresponding mathematical model was established to monitor the chlorophyll content. As a result, the determination coefficient R2 was 0.9557 and 0.9587, respectively. An experiment was conducted to establish the chlorophyll content monitoring model. After the spectrum signal measurement by chlorophyll detection module in the living plants nondestructively, the leaves were sampled and measured to get the true value of chlorophyll with the national standard method. According to NDVI and SPAD parameter, the correlation coefficient between the detection value and the true value was 0.8887 and 0.8745. Furthermore, an online dynamic monitoring experiment was conducted to monitor maize seedlings in the waterfertilizer stress group and the normal waterfertilizer management control group in real time. The chlorophyll changes in the plants were detected within 90h. Under the same management conditions, the chlorophyll change rules of plants were roughly the same. Under the influence of water and fertilizer stress, the chlorophyll concentration in the water and fertilizer stress group showed a downward trend. It was showed that the sensor system was feasible to monitor the chlorophyll dynamics of crops online and can provide support for crop information acquisition.