本文選題：水稻冠層 + 多光譜成像　； 參考：《東北農(nóng)業(yè)大學(xué)》2017年碩士論文

【摘要】：氮(N)是世界上作物生產(chǎn)最重要的營養(yǎng)之一,也是大多數(shù)農(nóng)作物中含量最大的營養(yǎng)元素。對于水稻的生長發(fā)育來說,氮素是其不可缺少的重要營養(yǎng)元素之一。合理的施用氮肥不僅能提高水稻的優(yōu)質(zhì)與高產(chǎn),還能緩解由農(nóng)業(yè)生產(chǎn)帶來的資源環(huán)境壓力。由于農(nóng)業(yè)生產(chǎn)具有很強(qiáng)的時效性,所以農(nóng)情基本信息的獲取要快速準(zhǔn)確。本文利用無人機(jī)技術(shù)實時、快速監(jiān)測水稻冠層氮素、葉綠素的營養(yǎng)狀況,進(jìn)一步指導(dǎo)其田間氮素的精細(xì)化管理,從而達(dá)到降低生產(chǎn)成本,提高氮肥利用率,減小環(huán)境污染的目的。本文以兩個品種的水稻田間氮素梯度試驗(N0-N5)為基礎(chǔ),以固定翼無人機(jī)獲取水稻冠層圖像為基本研究對象,從可見光遙感圖像與多光譜圖像中提取與地面取樣點(diǎn)對應(yīng)的圖像特征值入手,分析圖像特征值與地面測定值的關(guān)系,探尋水稻冠層的光譜響應(yīng)特性,明確水稻冠層光譜響應(yīng)的光譜參數(shù),構(gòu)建水稻冠層生理參數(shù)反演模型,由此來探究利用無人機(jī)影像對大尺度大田水稻氮素營養(yǎng)狀況進(jìn)行監(jiān)測的可行性。本研究的主要結(jié)論如下:由于水稻品種對航拍水稻冠層數(shù)碼影像的特征值影響較大,此次無人機(jī)拍攝的數(shù)碼影像不適用于本次大田多品種水稻冠層氮素的分類與反演。相比之下,機(jī)載多光譜影像較適合大田水稻冠層氮素的分類與反演。從對不同施氮水平的單個品種水稻冠層氮素的識別結(jié)果來看,氮梯度為嚴(yán)重缺氮與嚴(yán)重施氮即N0和N5的總體分類識別結(jié)果最高,其識別率達(dá)到了95%以上,并且水稻品種與分類方法對N0與N5的總體識別精度基本沒有什么較大影響。對于大田的多品種水稻氮素水平識別來說,多光譜影像經(jīng)波段變換后得到的綠色歸一化植被指數(shù)(GNDVI)影像識別嚴(yán)重缺氮與嚴(yán)重施氮即N0和N5的總體識別精度最高,達(dá)到93.83%,對于N0-N5所劃分出的嚴(yán)重缺氮、微量缺氮、微量施氮、嚴(yán)重施氮的4個氮素等級的總體識別精度較差,最高只達(dá)到了57.47%,這也說明水稻植株氮素的微量變化在水稻冠層圖像分類上表現(xiàn)不明顯。試驗結(jié)果表明,水稻冠層的光譜指數(shù)與水稻生理參數(shù)的擬合精度較高。對于兩個水稻品種來說,GNDVI與水稻SPAD的相關(guān)性最大(R2分別達(dá)到0.9478與0.8587)。由此說明,用GNDVI影像進(jìn)行水稻生理參數(shù)的反演監(jiān)測是可行的�？紤]水稻品種的影響,為了增加反演模型的普適性,將實測的兩個品種水稻的數(shù)據(jù)融合后,得到整體水稻葉片SPAD與光譜指數(shù)的最優(yōu)回歸模型與氮素含量的最優(yōu)反演模型。從而為利用水稻冠層圖像特征預(yù)測水稻生理參數(shù)的實時、快速、無損監(jiān)測提供參考依據(jù)。
[Abstract]:N) is one of the most important nutrients in crop production in the world, and is also the most important nutrient element in most crops. For the growth and development of rice, nitrogen is one of the important nutrient elements. The rational application of nitrogen fertilizer can not only improve the quality and high yield of rice, but also relieve the pressure of resources and environment brought by agricultural production. Agricultural production has a strong timeliness, so the basic information of agricultural conditions should be obtained quickly and accurately. In this paper, the real-time monitoring of nitrogen and chlorophyll in rice canopy was carried out by using UAV technology to further guide the fine management of nitrogen in the field, so as to reduce the production cost and improve the nitrogen utilization efficiency. The aim of reducing environmental pollution. In this paper, based on the nitrogen gradient test (N0-N5) between two varieties of paddy fields, a fixed wing UAV (Fixed-wing UAV) was used to obtain rice canopy image as the basic research object. Based on the extraction of the image eigenvalues corresponding to the ground sampling points from the visible and multispectral images, the relationship between the image eigenvalues and the ground measurements was analyzed, and the spectral response characteristics of the rice canopy were explored. The spectral parameters of the spectral response of rice canopy were determined, and the inversion model of rice canopy physiological parameters was constructed to explore the feasibility of monitoring the nitrogen nutrition status of rice in large scale field by using unmanned aerial vehicle (UAV) images. The main conclusions of this study are as follows: because rice varieties have a great influence on the characteristic values of aerial shoot rice canopy digital image, the digital image taken by UAV is not suitable for classification and inversion of canopy nitrogen in this field. In contrast, airborne multispectral images are more suitable for classification and inversion of nitrogen in rice canopy. According to the results of identification of nitrogen in the canopy of single rice varieties with different nitrogen application levels, the total classification and recognition results of N gradient was the highest for severe nitrogen deficiency and severe nitrogen application, i.e., N 0 and N 5, and the recognition rate was over 95%. And the total recognition accuracy of N0 and N5 was not greatly affected by rice varieties and classification methods. For the recognition of nitrogen level of rice varieties in the field, the green normalized vegetation index (GNDVI) image obtained from the multispectral image after band transformation was the best in the recognition of serious nitrogen deficiency and severe nitrogen application, namely, N0 and N5, the overall recognition accuracy of which was the highest, and that of N _ 0 and N _ 5 was the highest. Reached 93.83. The overall recognition accuracy of the four nitrogen grades classified by N0-N5 as severe nitrogen deficiency, trace nitrogen application and severe nitrogen application was poor. The highest value was only 57.47, which indicated that the microvariation of nitrogen content in rice canopy was not obvious in rice canopy image classification. The experimental results showed that the fitting accuracy between the spectral index of rice canopy and the physiological parameters of rice was higher. For two rice varieties, the correlation between GNDVI and Spad was 0.9478 and 0.8587 respectively. Therefore, it is feasible to use GNDVI image to invert and monitor the physiological parameters of rice. Considering the influence of rice varieties, in order to increase the universality of the inversion model, the optimal regression model of Spad and spectral index of whole rice leaves and the optimal inversion model of nitrogen content were obtained by fusing the measured data of two rice varieties. Therefore, it provides a reference for real-time, fast and nondestructive monitoring of rice physiological parameters by using the rice canopy image features.
【學(xué)位授予單位】：東北農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S511;S127

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