【摘要】：為了響應國家轉變農業(yè)發(fā)展方式,實施農業(yè)現代化改革號召,推行精細農業(yè)生產理念與管理方式,從根本上改變農民化肥的均勻播撒,引導其根據農作物生長情況及土壤養(yǎng)分情況適量播撒,避免形成浪費和環(huán)境污染。本課題針對大田作物冠層無損檢測設備進行硬件開發(fā)及田間試驗,儀器開發(fā)難度適中,試驗結果精度較高,主要分為以三部分內容:(1)設計開發(fā)基于光譜分析技術及ZigBee無線網絡技術的禾信通作物長勢監(jiān)測儀,包括采集節(jié)點與控制器兩部分。采集節(jié)點包括ZigBee芯片的選取、硬件電路系統、光學結構和軟件系統。控制器設計包括手持式內嵌PDA控制器與個人PC機USB協調器,完成兩種控制器的硬件電路、軟件設計,并針對PDA控制器進行玉米試驗。試驗計算NDVI、RVI、TVI、SAVI四種常用作物監(jiān)測植被指數,分析高、中、低三個水平及大田整體長勢水平下各植被指數與葉綠素指標相關性,選取RVI、及(R766,R550)和(R850,R550)波段組合NDVI建立玉米冠層葉綠素含量指標檢測模型。通過比較三種不同參數組合方式的建模結果,確定采用NDVI(R850,R550)建立玉米冠層葉綠素含量指標檢測模型(建模精度R2為0.508,驗證建模精度R2為0.458),精度較高,應用其檢測結果生成的田間玉米作物葉綠素水平空間分布圖,可為玉米拔節(jié)期變量作業(yè)提供技術支持。(2)設計開發(fā)無損檢測車載平臺,包括車體框架結構設計、控制器設計及禾信通作物長勢監(jiān)測儀采集節(jié)點、Topcon CropSpec氮含量傳感器的搭載設計,框架采用鋁型材為主要材料,驅動電機采用一體輪無刷直流電機�？刂破髟O計包括手持控制器與車載控制器兩部分,車載控制器設計包括硬件與軟件設計,手持控制器作為實驗員操作使用,車載控制器作為車體控制使用,并針對平臺可行性進行簡單的驗證試驗。試驗將禾信通采集節(jié)點與Topcon輸出的植被生長信息光譜數據進行數據處理,選取NDVI與SAVI兩種植被指數對兩種設備的采集數據進行相關性分析,分析結果NDVI的相關性較高,對NDVI進行建模分析,建模精度R2為0.514,驗證建模精度R2為0.373。結果精度較高,車載平臺可行性較好,對綠色植被的生長信息獲取較準確,可進行后期大田采集實驗。(3)針對田間冬小麥試驗設計使用ASD地物光譜儀(Analytical Spectral Devices.USA)采集冬小麥的冠層光譜反射率數據,使用SPAD-502Plus便攜式葉綠素儀測量小麥倒一葉和倒二葉的葉綠素指標(SPAD值),以及G738CM型手持式GPS記錄采樣點的位置信息。分別進行冠層光譜反射率小麥倒一葉和倒二葉的預處理,結果表明冠層反射光譜倒二葉的SPAD值相關系數高于倒一葉�；谙嚓P性分析,選取敏感波段538nm、661nm、740nm和850nm分別與預處理前、后的光譜數據進行多元線性回歸分析,其預處理后的模型精度較高,建模精度R2為0.48,驗證建模精度R2為0.32。進而繪制大田作物長勢圖,可為冬小麥追肥作業(yè)提供支持。
[Abstract]:In order to respond to the national transformation of the mode of agricultural development, to carry out the call for the reform of agricultural modernization, to promote the concept and management of fine agricultural production, and to fundamentally change the uniform distribution of chemical fertilizers among farmers, It can be spread according to crop growth and soil nutrient to avoid waste and environmental pollution. In this paper, the hardware development and field test of field crop canopy nondestructive testing equipment are carried out. The development of the instrument is moderate, and the precision of the test result is high. It is mainly divided into three parts: (1) designing and developing a crop growth monitor based on spectrum analysis and ZigBee wireless network technology, which includes two parts: acquisition node and controller. The acquisition node includes the selection of ZigBee chip, hardware circuit system, optical structure and software system. The controller design includes the hand-held embedded PDA controller and the USB coordinator of personal PC machine. The hardware circuit and software design of the two controllers are completed, and the corn experiment is carried out for the PDA controller. The vegetation index monitored by four common crops of NDVI,RVI,TVI,SAVI was calculated and the correlation between vegetation index and chlorophyll index was analyzed at the three levels of high, middle and low, and the whole field growth level. RVI, and (R766N R550) and (R850 R550) band combination NDVI were selected to establish a model for the detection of chlorophyll content in maize canopy. By comparing the modeling results of three different parameter combinations, NDVI (R850 R550) was used to establish the maize canopy chlorophyll content detection model (modeling precision R2 was 0.508, and the modeling precision R 2 was 0.458), and the precision was high. The horizontal spatial distribution map of chlorophyll produced by the test results can provide technical support for maize jointing variable operation. (2) Design and development of non-destructive testing vehicle platform, including the design of car-body frame structure. The controller design and the carrying design of the, Topcon CropSpec nitrogen sensor in the collecting node of the plant growth monitor are designed. Aluminum profile is used as the main material in the frame and the brushless DC motor is used as the driving motor. The controller design includes two parts: handheld controller and vehicle controller. The design of vehicle controller includes hardware and software design. Handheld controller is used as experimenter, vehicle controller is used as carbody control. And the feasibility of the platform to carry out a simple verification test. In the experiment, the spectral data of vegetation growth information produced by Topcon and the node of Hexintong were processed, and two kinds of vegetation indexes, NDVI and SAVI, were selected to analyze the correlation between the data collected by the two equipments. The results showed that the correlation of NDVI was high. The modeling precision R2 is 0.514, and the modeling precision R2 is 0.373. Results the accuracy was high, the vehicle platform was feasible, and the growth information of green vegetation was more accurate. (3) the ASD ground object spectrometer (Analytical Spectral Devices.USA) was used to collect the canopy spectral reflectance data of winter wheat. SPAD-502Plus portable chlorophyll meter was used to measure the chlorophyll index (SPAD value) of the first and second leaves of wheat and the position information of the sampling points recorded by G738CM hand-held GPS. The canopy spectral reflectance of wheat was pretreated with the first leaf and the second leaf, respectively. The results showed that the correlation coefficient of SPAD between the two leaves was higher than that in the first leaf. Based on the correlation analysis, the sensitive band of 538nm ~ 661nm ~ (740nm) and 850nm are selected respectively and the spectral data before and after pretreatment are analyzed by multivariate linear regression analysis. The model precision is high, the modeling precision is 0.48, and the modeling precision is 0.32. Furthermore, the plot of field crop growth can provide support for winter wheat topdressing.
【學位授予單位】：東北農業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：S126

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