本文選題：甘蔗 + 長(zhǎng)勢(shì)監(jiān)測(cè)�。� 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：農(nóng)作物的長(zhǎng)勢(shì)監(jiān)測(cè)是農(nóng)業(yè)氣象觀測(cè)的一個(gè)重要組成部分,可以為農(nóng)作物種植田間管理、產(chǎn)量預(yù)報(bào)提供基礎(chǔ)數(shù)據(jù)。長(zhǎng)期以來(lái),作物長(zhǎng)勢(shì)地面監(jiān)測(cè)主要通過人工觀測(cè)方式采集獲取,靠觀測(cè)人員到田間進(jìn)行實(shí)地觀測(cè),這種方法主觀性強(qiáng)且費(fèi)時(shí)、費(fèi)力。隨著計(jì)算機(jī)技術(shù)和互聯(lián)網(wǎng)在農(nóng)業(yè)中的廣泛應(yīng)用,人工觀測(cè)方法已不能滿足現(xiàn)代化農(nóng)業(yè)發(fā)展的需求。因此,亟需一種在線、實(shí)時(shí)、自動(dòng)的觀測(cè)方式來(lái)實(shí)現(xiàn)作物長(zhǎng)勢(shì)的監(jiān)測(cè)。在大田環(huán)境下,利用成像設(shè)備結(jié)合計(jì)算機(jī)、圖像處理、互聯(lián)網(wǎng)技術(shù)對(duì)作物進(jìn)行實(shí)時(shí)監(jiān)測(cè)是一種可選的方法。本文通過在廣西省壯族自治區(qū)柳州市柳城縣社沖鄉(xiāng)的甘蔗田進(jìn)行實(shí)地實(shí)驗(yàn),采用全要素農(nóng)業(yè)自動(dòng)氣象觀測(cè)系統(tǒng)對(duì)獲取的甘蔗圖像進(jìn)行提取、分析,獲取甘蔗的主要生長(zhǎng)特征參數(shù),目的是為甘蔗長(zhǎng)勢(shì)的在線定量化無(wú)損監(jiān)測(cè)提供技術(shù)基礎(chǔ)。通過圖像自動(dòng)采集裝置獲取2015年和2016年全生育期甘蔗圖像,利用圖像處理技術(shù)對(duì)獲取的圖像進(jìn)行分析、處理,并結(jié)合RGB分量所構(gòu)建的顏色指數(shù)構(gòu)建了甘蔗葉面積指數(shù)估算模型、甘蔗覆蓋度估算模型,從而反映甘蔗的長(zhǎng)勢(shì)狀況。采用圖像分割、圖像二值化、連通域標(biāo)記、連通域特征統(tǒng)計(jì)等技術(shù)方法實(shí)現(xiàn)了對(duì)甘蔗出苗期進(jìn)行自動(dòng)檢測(cè),為甘蔗出苗期的農(nóng)事管理決策提供了基礎(chǔ)數(shù)據(jù)。通過理論和實(shí)驗(yàn)相結(jié)合的方法,研究獲得了以下主要結(jié)果:(1)通過對(duì)攝像機(jī)正下視圖像數(shù)據(jù)進(jìn)行預(yù)處理,構(gòu)建了基于顏色指數(shù)的甘蔗葉面積指數(shù)估算模型,并且通過分析不同顏色指數(shù)、時(shí)間組合對(duì)甘蔗葉面積指數(shù)估算模型精度的影響,確定甘蔗葉面積指數(shù)最佳估算指數(shù)為G-B,且G-B指數(shù)采用9點(diǎn)、11點(diǎn)、15點(diǎn)獲取的數(shù)字照片計(jì)算平均值時(shí),估算精度最高,預(yù)測(cè)R2最大為0.8164,RMSE最小為0.1211。(2)通過對(duì)攝像機(jī)正下視圖像數(shù)據(jù)進(jìn)行預(yù)處理,采用PFMRF法對(duì)甘蔗圖像進(jìn)行分割,由此計(jì)算甘蔗的覆蓋度作為覆蓋度參考值;同時(shí)計(jì)算圖像的九種顏色指數(shù),通過對(duì)覆蓋度參考值和顏色指數(shù)進(jìn)行相關(guān)分析,發(fā)現(xiàn)甘蔗的覆蓋度和由圖像計(jì)算的顏色指數(shù)之間存在較好的相關(guān)性。通過分析、建模、驗(yàn)證,最終得出,以顏色指數(shù)ExG-ExR、ExG或CIVE為變量的甘蔗覆蓋度估算模型精度均較好。其中,最佳的顏色指數(shù)是ExG,利用上午11:00的圖像數(shù)據(jù)所建立的模型估算精度最高,RMSE和MAE分別為0.0484、0.0409。(3)通過對(duì)相機(jī)圖像數(shù)據(jù)和CCD攝像機(jī)圖像數(shù)據(jù)進(jìn)行分析,以圖像上甘蔗幼苗的形狀作為特征,將圖像中的雜草去除,進(jìn)而識(shí)別出甘蔗幼苗。然后對(duì)識(shí)別出的甘蔗幼苗的分布特性進(jìn)行分析,進(jìn)而判斷該圖像是到達(dá)出苗期。通過與觀測(cè)人員記錄的甘蔗發(fā)育期時(shí)間比較,發(fā)現(xiàn)基于圖像的甘蔗出苗期自動(dòng)識(shí)別算法精度較高,誤差在±3天以內(nèi)。
[Abstract]:Crop growth monitoring is an important part of agrometeorological observation, which can provide basic data for crop planting field management and yield forecast. For a long time, crop growth monitoring is mainly acquired by artificial observation, and is observed in the field by observers. This method is subjective, time-consuming and laborious. With the wide application of computer technology and Internet in agriculture, artificial observation method can not meet the needs of modern agricultural development. Therefore, an online, real-time and automatic observation method is needed to monitor crop growth. In the field environment, it is an optional method to use imaging equipment combined with computer, image processing and Internet technology to monitor crops in real time. In this paper, the sugarcane field in Liucheng County, Liuzhou City, Guangxi Province was tested in the field, and the obtained sugarcane image was extracted and analyzed by the automatic meteorological observation system of all-factor agriculture. The main growth characteristic parameters of sugarcane were obtained in order to provide technical basis for on-line quantitative nondestructive monitoring of sugarcane growth. The sugarcane images of the whole growth period of 2015 and 2016 were acquired by the automatic image acquisition device. The obtained images were analyzed and processed by image processing technology, and the estimation model of sugarcane leaf area index was constructed by combining the color index constructed by the RGB component. The estimation model of sugarcane coverage can reflect the growing condition of sugarcane. The techniques of image segmentation, image binarization, connected domain marking and connected domain feature statistics are used to realize automatic detection of sugarcane seedling stage, which provides basic data for sugarcane seedling management decision. By combining theory with experiment, the following main results are obtained: 1) based on color index, a model for estimating sugarcane leaf area index is established by preprocessing camera forward down view image data. The effects of different color indices and time combinations on the precision of the model were analyzed. The best estimation index of sugarcane leaf area index is G-B, and the G-B index is the best when the average value of digital photographs obtained from 9 points and 11 points and 15 points is used to calculate the average value, the estimation accuracy is the highest. The prediction R2 maximum is 0.8164 RMSE minimum 0.1211.02) by preprocessing the camera forward downlooking image data, the sugarcane image is segmented by PFMRF method, the coverage of sugarcane is calculated as the reference value of coverage, and the nine color indices of the image are calculated at the same time. By analyzing the correlation between the reference value of coverage and the color index, it is found that there is a good correlation between the coverage of sugarcane and the color index calculated by image. Through analysis, modeling and verification, it is concluded that the precision of sugarcane mulching estimation model with color index ExG-ExRGExG or CIVE as variable is better. Among them, the best color index is Exg, the model established by using 11:00 image data has the highest estimation accuracy (RMSE and MAE are 0.0484n0.0409.93) by analyzing the camera image data and the CCD camera image data. Taking the shape of sugarcane seedling in the image as the feature, the weed was removed from the image, and then the sugarcane seedling was identified. Then, the distribution characteristics of sugarcane seedlings were analyzed, and the image was determined to reach the emergence stage. By comparing with the time of sugarcane development recorded by the observer, it is found that the automatic recognition algorithm of sugarcane seedling period based on image has a high accuracy and the error is within 鹵3 days.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S566.1;S126

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