Field Crop Canopy Application Of Nondestructive Testing Equipment And Development Of Vehicle Platform

In order to respond the transformation of agricultural development mode in China. The implementation of the reform calls for the modernization of agriculture and promotes agricultural production concept and management. The purpose changes to fundamentally the farmers fertilizer uniform seeding, guide it according to the growth situation of crops and soil nutrient situation in an appropriate seeding. It avoids to the formation of waste and environmental pollution. The task was developed in the field of crop canopy nondestructive equipment and hardware development. Instrument development of moderate difficulty and the test resulted of high precision. In the three part of the content was mainly divided into:(1) Design and development of Crop Network based on Zig Bee wireless network technology and spectral analysis. Crop Network included two parts of data acquisition node and controller. Data acquisition node included Zig Bee chip selection, hardware circuit, optical structure and software. Design of controller comprised a handheld embedded controller PDA and individual PC machine USB Coordinator. The hardware circuit and software were completed for two kinds of controller. And field corn tested for the PDA controller. Based on the optical sensor measurement data to calculate the four common crop monitoring vegetation index that ratio vegetation index(RVI), normalized difference vegetation index(NDVI), transformational vegetation index(TVI) and soil adjusted vegetation index(SAVI). Analyzed the high, medium and low three levels, field looked like the overall level of each vegetation index correlation with chlorophyll index. Selected the RVI、NDVI(R766,R550)and NDVI(R850,R550)maize jointing stage chlorophyll index monitoring model was established. The research results showed that the vegetation index of NDVI(R850,R550) and the SPAD value under different growth levels all have high correlation. The linear regression model was established by selection of NDVI(R850,R550), its modeling R2 for 0.508 and validation R2 for 0.458. On account of the modeling results mapped the distribution of field crop condition and used to guide the field fertilizer management. The research could be used to jointing stage of corn crop condition monitoring and fertilizer management decision-making support.(2) Nondestructive testing system design and development of vehicle platform. It included vehicle body frame structure design、Controller design and Crop Network、Topcon Crop Spec. The frame adopted the aluminum as main material. One wheel driving motor adopted brushless DC motor. The controller design included two parts of handheld controller and car controller. Designof vehicle controller included hardware and software. Hand held controller used as a laboratory assistant operation. Vehicle controller used to control the vehicle. And verify test simple platform for feasibility. The test collected vegetation growth information of spectral data for Crop Network and Topcon Crop Spec. Selection of NDVI and SAVI two vegetation data collected on two kinds of equipment index correlation analysis. The higher correlation analysis NDVI. Then the modeling analysis of NDVI, the modeling accuracy of R2 was 0.514, verify the modeling accuracy of R2 was 0.373. The result was high accuracy.And feasibility of vehicle platform. So the vegetation growth obtained to more accurate carried on the late stage acquisition experiment.(3) According to the use of design in the field experiment of Winter Wheat. The spectral technology was applied. The Analytical Spectral Devices Field Spec Hand Held(USA) spectral radiometer was used to collect spectral reflectance data of winter wheat. The visible and NIR band(325-1050nm) reflectance of winter wheat canopy was measured. The portable chlorophyll meter(SPAD-502plus) was applied to measure SPAD index of the pour wheat leaf and the second countdown leaf of each plant. And handheld GPS(G738CM) was used to record the location of sampling points. The preprocessing between the reflectance and the wheat leaf SPAD index was analyzed. It was showed that the relationship between spectral data the second countdown leaves SPAD values higher than the pour leaves. In addition, four sensitive wavelengths were selected at the 538 nm、661 nm、740 nm and 850 nm based on correlation analysis.And four sensitive wavelengths respectively proceed to multiple linear regression analysis for the before and after preprocessing. The result showed that the after preprocessing modeling accuracy was 0.48 and validation accuracy was 0.32.The distribution map was drawn by GPS coordinates and modeling prediction result. With the application of spectral technology, it provides a feasible method to detect the winter wheat growth status at heading stage.