小麥葉部病害識(shí)別方法研究及智能手機(jī)診斷系統(tǒng)研發(fā)
發(fā)布時(shí)間：2018-10-19 06:29
【摘要】：病蟲害是影響農(nóng)作物產(chǎn)量和品質(zhì)的重要因素,如何對(duì)其進(jìn)行實(shí)時(shí)監(jiān)測(cè)和快速準(zhǔn)確區(qū)分對(duì)指導(dǎo)農(nóng)作物生產(chǎn)管理具有重要意義。傳統(tǒng)的監(jiān)測(cè)和區(qū)分方法是通過植保專家抽樣調(diào)查、人工區(qū)分進(jìn)行判斷,費(fèi)時(shí)費(fèi)力,難以滿足大面積調(diào)查的需求。隨著科學(xué)技術(shù)的快速發(fā)展,植保專家利用圖像處理、模式識(shí)別、遙感等技術(shù)對(duì)植物病蟲害進(jìn)行監(jiān)測(cè)和識(shí)別,取得了顯著效果。但是,已有技術(shù)和開發(fā)的傳感器距離實(shí)用、低值、便攜應(yīng)用仍有差距。本論文以小麥葉部條銹病、白粉病為觀測(cè)對(duì)象,結(jié)合圖像處理和模式識(shí)別技術(shù)探索適用儀器開發(fā)的小麥病害快速識(shí)別方法,設(shè)計(jì)了一款基于Android智能手機(jī)的病害診斷系統(tǒng)。主要內(nèi)容、創(chuàng)新點(diǎn)和結(jié)果如下：(1)研究了高通濾波、中值濾波、鄰域平均法3種圖像增強(qiáng)算法用于減少圖像采集環(huán)境帶來的影響；研究了3種分割算法(優(yōu)化分水嶺分割、自動(dòng)閾值分割和水平集分割),以將病斑從小麥健康葉片中分離出來,用于提取病斑特征。從顏色、形狀和紋理三個(gè)方面(共計(jì)提取23個(gè)病斑特征參數(shù))對(duì)小麥葉部病害特征進(jìn)行描述。試驗(yàn)結(jié)果表明：?jiǎn)我坏膱D像增強(qiáng)算法不能達(dá)到理想增強(qiáng)效果,且單一的圖像分割算法也不能很好地將目標(biāo)區(qū)域分割出來。因此,應(yīng)將圖像增強(qiáng)和分割算法進(jìn)行優(yōu)化以提高其增強(qiáng)和分割效果。(2)研究了相關(guān)向量機(jī)(Relevance Vector Machine, RVM)、支持向量機(jī)(Support Vector Machine, SVM)口反向傳播神經(jīng)網(wǎng)絡(luò)(Back Propagation Neural Network, BPNN)三種圖像識(shí)別方法。本論文中選取150個(gè)不同嚴(yán)重程度(含輕度、中度和重度)的小麥葉部病害(條銹病、白粉病)為試驗(yàn)材料,以輕中度病害為重點(diǎn),選取其中68個(gè)病害葉片為訓(xùn)練樣本,提取每個(gè)病害葉片的顏色、紋理和形狀共計(jì)23個(gè)特征,利用Relief算法計(jì)算病害顏色、紋理和形狀中每個(gè)特征的權(quán)重(即對(duì)病害識(shí)別的貢獻(xiàn)大小),并選取其中20個(gè)權(quán)重較大的特征作為SVM、 BPNN和RVM的輸入?yún)?shù),分別建立三種識(shí)別模型。通過2組試驗(yàn)的68個(gè)測(cè)試樣本驗(yàn)證,結(jié)果顯示：SVM、BPNN和RVM的平均識(shí)別準(zhǔn)確率分別為86.76%、91.17%和89.71%,而對(duì)輕中度病害的識(shí)別準(zhǔn)確率分別為86.67%、90.00%和88.33%,其中,RVM的執(zhí)行效率分別是SVM和BP神經(jīng)網(wǎng)絡(luò)的7.96和31.68倍。(3)針對(duì)目前裝置攜帶不便、價(jià)格昂貴、專業(yè)性要求高等問題,結(jié)合RVM識(shí)別算法,開發(fā)了一款基于Android智能手機(jī)的小麥葉部病害診斷系統(tǒng)。本文利用Sony DSC-H9相機(jī)和SAMSUNG GT-N7100手機(jī)采集白粉病和條銹病不同嚴(yán)重程度(輕度、中度和重度)樣本各66個(gè)(白粉病和條銹病各33個(gè)),選取其中48個(gè)(白粉病與條銹病各24個(gè))作為訓(xùn)練樣本,其余用作測(cè)試樣本；同時(shí),改變手機(jī)采集樣本像素作為另一對(duì)照組來研究像素與識(shí)別率的關(guān)系,同上安排樣本分布。研究結(jié)果表明：RVM得到的平均識(shí)別率為88.89%,病害的正確識(shí)別率與采集工具有關(guān),并與其像素成正比。因此,進(jìn)行病害識(shí)別需選擇像素合適的手機(jī)以得到較高的準(zhǔn)確率。同時(shí),經(jīng)應(yīng)用測(cè)試發(fā)現(xiàn),識(shí)別一副病害圖片可在20s內(nèi)完成,能夠?qū)崿F(xiàn)對(duì)小麥不同嚴(yán)重程度葉部病害快速準(zhǔn)確識(shí)別,這為植保人員田間調(diào)查提供了重要的技術(shù)支持。
[Abstract]:Pest is an important factor affecting the yield and quality of crops, and how to monitor and distinguish it in real time is of great significance to guide crop production management. The traditional method of monitoring and distinguishing is through sample sampling of plant protection experts, artificial differentiation and judgment, and it is difficult to meet the needs of large-area investigation. With the rapid development of science and technology, plant protection experts use the techniques of image processing, pattern recognition and remote sensing to monitor and identify plant diseases and insect pests. However, prior art and developed sensor distances are practical, low, and portable applications still have gaps. This paper uses wheat leaf rust and powdery mildew as the observation object, and probes into the rapid recognition method of wheat disease developed by suitable instrument in combination with image processing and pattern recognition technology, and designs a disease diagnosis system based on Android smart phone. The main content, innovation point and result are as follows: (1) The effects of high-pass filtering, middle value filtering and neighborhood averaging method are studied to reduce the influence of image acquisition environment; 3 segmentation algorithms (optimized watershed segmentation) are studied. Automatic threshold segmentation and horizontal set segmentation) to separate disease spots from wheat healthy leaves for extraction of disease spot characteristics. The disease characteristics of wheat leaf were described from three aspects: color, shape and texture (total extraction of 23 plaque characteristic parameters). The results show that the single image enhancement algorithm can not achieve the ideal enhancement effect, and the single image segmentation algorithm can not partition the target area well. Therefore, image enhancement and segmentation algorithms should be optimized to improve their enhancement and segmentation effects. (2) Three kinds of image recognition methods such as correlation vector machine (RVM), support vector machine (SVM) port inverse propagation neural network (BPNN) are studied. In this paper, 150 wheat leaf diseases (stripe rust and powdery mildew) of 150 different severity (including mild, moderate and severe) were selected as test materials. Among them, 68 disease leaves were selected as training samples, and the color of each disease blade was extracted. The texture and shape are 23 characters, and the weight of each feature in the disease color, texture and shape is calculated by using the Relief algorithm (i.e. the contribution size to the disease recognition), and 20 weight-weight features are selected as input parameters of the SVM, the BPNN and the RVM, and three identification models are respectively established. The results showed that the average recognition accuracy of SVM, BPNN and RVM was 86. 76%, 91. 17% and 89. 71%, respectively, while the accuracy of recognition of mild moderate disease was 86. 67%, 90. 00% and 88. 33%, respectively. The execution efficiency of RVM is 7.96 and 31.68 times that of SVM and BP neural network, respectively. (3) Aiming at the problems of inconvenience, high price, high professional requirement and so on, a diagnosis system of wheat leaf disease based on Android smart phone was developed in combination with RVM recognition algorithm. Sixty-six (33 powdery mildew and stripe rust) samples were collected by Sony DSC-H9 camera and SAMSUNG GT-N7100 cell phone, 48 of them (24 samples of powdery mildew and stripe rust) were selected as training samples and the rest were used as test samples. At the same time, the relationship between the pixel and the recognition rate is studied by changing the sampling pixel of the mobile phone as another control group, and the sample distribution is arranged on the same. The results show that the average recognition rate of RVM is 88. 89%, the correct recognition rate of disease is related to the acquisition tool and is directly proportional to its pixels. Therefore, it is necessary to select a suitable mobile phone for disease recognition to obtain higher accuracy. At the same time, through the application test, identification of a pair of disease pictures can be completed within 20s, and can realize rapid and accurate identification of the diseases of different severity leaf parts of wheat, which provides important technical support for the field investigation of plant protection personnel.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：S435.12;TP391.41
本文編號(hào)：2280414
[Abstract]:Pest is an important factor affecting the yield and quality of crops, and how to monitor and distinguish it in real time is of great significance to guide crop production management. The traditional method of monitoring and distinguishing is through sample sampling of plant protection experts, artificial differentiation and judgment, and it is difficult to meet the needs of large-area investigation. With the rapid development of science and technology, plant protection experts use the techniques of image processing, pattern recognition and remote sensing to monitor and identify plant diseases and insect pests. However, prior art and developed sensor distances are practical, low, and portable applications still have gaps. This paper uses wheat leaf rust and powdery mildew as the observation object, and probes into the rapid recognition method of wheat disease developed by suitable instrument in combination with image processing and pattern recognition technology, and designs a disease diagnosis system based on Android smart phone. The main content, innovation point and result are as follows: (1) The effects of high-pass filtering, middle value filtering and neighborhood averaging method are studied to reduce the influence of image acquisition environment; 3 segmentation algorithms (optimized watershed segmentation) are studied. Automatic threshold segmentation and horizontal set segmentation) to separate disease spots from wheat healthy leaves for extraction of disease spot characteristics. The disease characteristics of wheat leaf were described from three aspects: color, shape and texture (total extraction of 23 plaque characteristic parameters). The results show that the single image enhancement algorithm can not achieve the ideal enhancement effect, and the single image segmentation algorithm can not partition the target area well. Therefore, image enhancement and segmentation algorithms should be optimized to improve their enhancement and segmentation effects. (2) Three kinds of image recognition methods such as correlation vector machine (RVM), support vector machine (SVM) port inverse propagation neural network (BPNN) are studied. In this paper, 150 wheat leaf diseases (stripe rust and powdery mildew) of 150 different severity (including mild, moderate and severe) were selected as test materials. Among them, 68 disease leaves were selected as training samples, and the color of each disease blade was extracted. The texture and shape are 23 characters, and the weight of each feature in the disease color, texture and shape is calculated by using the Relief algorithm (i.e. the contribution size to the disease recognition), and 20 weight-weight features are selected as input parameters of the SVM, the BPNN and the RVM, and three identification models are respectively established. The results showed that the average recognition accuracy of SVM, BPNN and RVM was 86. 76%, 91. 17% and 89. 71%, respectively, while the accuracy of recognition of mild moderate disease was 86. 67%, 90. 00% and 88. 33%, respectively. The execution efficiency of RVM is 7.96 and 31.68 times that of SVM and BP neural network, respectively. (3) Aiming at the problems of inconvenience, high price, high professional requirement and so on, a diagnosis system of wheat leaf disease based on Android smart phone was developed in combination with RVM recognition algorithm. Sixty-six (33 powdery mildew and stripe rust) samples were collected by Sony DSC-H9 camera and SAMSUNG GT-N7100 cell phone, 48 of them (24 samples of powdery mildew and stripe rust) were selected as training samples and the rest were used as test samples. At the same time, the relationship between the pixel and the recognition rate is studied by changing the sampling pixel of the mobile phone as another control group, and the sample distribution is arranged on the same. The results show that the average recognition rate of RVM is 88. 89%, the correct recognition rate of disease is related to the acquisition tool and is directly proportional to its pixels. Therefore, it is necessary to select a suitable mobile phone for disease recognition to obtain higher accuracy. At the same time, through the application test, identification of a pair of disease pictures can be completed within 20s, and can realize rapid and accurate identification of the diseases of different severity leaf parts of wheat, which provides important technical support for the field investigation of plant protection personnel.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：S435.12;TP391.41
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