基于圖像處理的棉花生長監(jiān)測(cè)系統(tǒng)

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摘要 隨著計(jì)算機(jī)技術(shù)的迅猛發(fā)展，數(shù)字圖像技術(shù)在農(nóng)作物科學(xué)上得到了廣泛的應(yīng)用。在農(nóng)作物群體特征的提取上，已有很多利用數(shù)字圖像技術(shù)對(duì)空間分布相對(duì)均勻的農(nóng)作物的群體圖像進(jìn)行研究，而對(duì)于在空間上分布不均勻的棉花群體圖像特征研究較少。本研究意圖將計(jì)算機(jī)圖像處理及識(shí)別技術(shù)運(yùn)用于田間拍攝棉花群體圖像的分析研究，并以機(jī)器自動(dòng)提取群體特征參數(shù)代替?zhèn)鹘y(tǒng)手工提取群體特征參數(shù)。
論文在分析農(nóng)作物長勢(shì)監(jiān)控研究現(xiàn)狀和存在問題基礎(chǔ)上，采用低成本BF3703攝像模組和低功耗EFM32GG380微控制器，構(gòu)建低功耗嵌入式圖像處理實(shí)驗(yàn)平臺(tái)，實(shí)現(xiàn)棉花植株長勢(shì)在線監(jiān)測(cè)。論文從軟硬件方面進(jìn)行低功耗設(shè)計(jì)，包括硬件整體框架設(shè)計(jì)，芯片選型，圖像采集和處理模塊設(shè)計(jì)，軟件低功耗控制流程，圖像處理算法優(yōu)化等。通過研究葉片側(cè)枝角度提取算法和葉片葉綠素含量均值提取算法，計(jì)算出葉片側(cè)枝傾斜角度和葉綠素含量，識(shí)別葉片生長狀態(tài)；通過分析棉花植株整體特征和局部特征、棉花植株生長狀態(tài)，實(shí)現(xiàn)棉花長勢(shì)在線監(jiān)測(cè)和棉花健康狀況診斷?，F(xiàn)場(chǎng)節(jié)點(diǎn)采集的數(shù)據(jù)通過無線傳感網(wǎng)絡(luò)傳送到遠(yuǎn)程控制中心，達(dá)到對(duì)棉花長勢(shì)遠(yuǎn)程監(jiān)控的目的。

關(guān)鍵詞：機(jī)器視覺 EFM32 圖像處理 棉花長勢(shì) 低功耗

Cotton Growth Monitoring System Based on Image Processing
Abstract With the rapid development of computer technology, digital image technology has been widely used in crop science.On the extraction of crop community characteristic, it has a lot of use of digital image technology in relatively uniform spatial distribution of crops, while for the uneven distribution of cotton on the space group image characteristics research rarely reported.Intention of this study is to apply computer image processing and recognition technology to analysis and research of the image taken cotton field group, and group characteristics is extracted by automatic machine parameters instead of traditional manual extraction group characteristics.
On the basis of present researches and existing problems of crop condition monitoring analysis, this paper propose a low-power embedded image process system using low-cost camera module BF3703 image sensor and low power consumption EFM32GG380 micro-controller to track the growth of local area of cotton plants. This embedded system was designed low power consumption from hardware and software aspects, including hardware overall frame design, chip selection, image acquisition and processing module design, the software control flow, low power consumption and optimization of image processing algorithms. The blade lateral branch angle extraction algorithm and the mean leaf chlorophyll content extraction algorithm were designed to calculate the blade lateral branch angle and content of green leaves, completing growth state identification of leaves. Through the analysis of cotton plant overall characteristics, local characteristics and period growth status, realized the cotton growth track and health diagnosis. Data collected by experimental platform can be transmitted to the remote control center by wireless sensor network, achieved the purpose of cotton growth remote monitoring.

Keywords: Machine vision EFM32 Image process Cotton growing Low power consumption

目 錄
摘要 I
第一章 緒論 1
1.1 課題來源及意義 1
1.2 農(nóng)作物在線監(jiān)測(cè)系統(tǒng)的研究現(xiàn)狀 1
1.2.1 在農(nóng)作物特性獲取和監(jiān)測(cè)方面的研究 2
1.2.2 在農(nóng)作物特征信息方面的研究 4
1.2.3 在農(nóng)作物病蟲草害方面的研究 5
1.3 課題主要工作及論文結(jié)構(gòu)安排 6
第二章 系統(tǒng)設(shè)計(jì)及關(guān)鍵技術(shù) 7
2.1 系統(tǒng)總體方案及思路 7
2.2 系統(tǒng)工作原理 8
2.3 關(guān)鍵技術(shù) 9
2.3.1 硬軟件低功耗 9
2.3.2 圖像采集與處理 10
第三章 圖像處理核心算法 11
3.1 圖像預(yù)處理 11
3.1.1 去除噪聲 11
3.1.2 灰度化 14
3.1.3 二值化 15
3.2 棉花圖像特征提取 16
3.2.1 高度與葉面積 17
3.2.2 葉綠素 18
3.2.3 葉片側(cè)枝角度計(jì)算 21
第四章 基于棉花長勢(shì)在線監(jiān)控的硬件 23
4.1 處理器模塊 24
4.2 電源模塊 26
4.3 圖像采集模塊 27
4.3.1 BF3703 27
4.3.2 圖像緩存電路設(shè)計(jì) 29
4.4 無線傳輸模塊 30
4.5 實(shí)時(shí)時(shí)鐘芯片 32
4.6 USB接口電路 33
4.7 SD存儲(chǔ)卡 34
第五章 基于棉花長勢(shì)在線監(jiān)控的軟件 36
5.1 IAR開發(fā)平臺(tái) 36
5.2 嵌入式軟件體流程 37
5.2.1 總流程圖 37
5.2.2 圖像采集子流程圖 38
5.2.3 圖像處理子程序圖 39
5.2.4 數(shù)據(jù)通信子程序圖 40
第六章 總結(jié)與展望 42
6.1 研究工作總結(jié) 42
6.2 課題展望 43
致 謝 44
參考文獻(xiàn) 45