Research On Fluorescence-induced Early Warning For Plant Diseases And Growth Information Monitoring System

In recent years,with the continuous development of agriculture in our country,problems such as the decline of soil fertility,environmental pollution,and pesticide residues caused by unscientific agricultural production have become increasingly prominent.How to rationally use resources and reduce production costs has become the focus of agricultural development in our country.This thesis starts with fluorescence detection,studies the relationship between plant growth information and photoelectric information,and the system for rapid and accurate detection of plant growth information is designed to provide more accurate information data for refined agricultural operations.On the basis of analyzing and comparing the development status of plant information detection technology at home and abroad,it was proposed to use the near-infrared method to detect plant water content,relative chlorophyll content(SPAD)to detect the chlorophyll content of plants,chlorophyll luorescence intensity method to determine the nitrogen fertilizer content of plants,and chlorophyll fluorescence imaging technology to detect plant diseases.According to the detection principle of their own information and the characteristics of the Agricultural Internet of things,a plant growth information monitoring system,including sensor detection,microcontrol unit(MCU),wireless communication(Wi-Fi),analysis model,database and browsing web(Web),has been designed to realize the rapid and accurate detection of multipath plant information.The correlation between the fluorescence characteristic parameter Rfd and the disease infection was analyzed by using the fluorescence image information of the plant leaves of the inoculating pathogen.The early warning model with Rfd as the main input and the output of the disease was established,the accuracy of the model was verified.Through the calibration and prediction experiments of chlorophyll content,water and nitrogen content,a complete plant growth information monitoring model was established,and the accuracy of the system was analyzed by repeatability and stability experiments.The experimental results show that the system designed in this thesis is reliable,the monitoring efficiency of the system is higher than the traditional method,the correlation coefficient R of the monitoring value and the real value are above 0.85,and has good correlation.The experimental study in this thesis shows that diseases can affect plant fluorescence and can be used for early warning of plant diseases by using fluorescence characteristic parameter m.The plant growth information monitoring system based on the photoelectric detection technology and the Internet of things technology can realize the rapid and accurate detection of multiple informationes of living plants without damaging the leaves,and has good performance,the plant growth information model can reflect the true growth status of plants.