本文選題：無人機(jī)　切入點：顏色空間　出處：《沈陽農(nóng)業(yè)大學(xué)學(xué)報》2017年05期 　論文類型：期刊論文

【摘要】：目前常用的水稻產(chǎn)量估算方法以衛(wèi)星遙感估產(chǎn)為主,衛(wèi)星遙感估產(chǎn)的分辨率較低、缺乏機(jī)理性、誤差較大。為了能夠快速靈活地獲取水稻冠層信息、提高分辨率、準(zhǔn)確地估測水稻產(chǎn)量,利用無人機(jī)平臺搭載高清數(shù)碼相機(jī),拍攝從抽穗期到成熟期的水稻冠層影像,首先應(yīng)用中值濾波算法對RGB顏色空間下水稻冠層圖像進(jìn)行去噪,然后針對彩色水稻圖像的顏色特征,將圖像由RGB顏色空間轉(zhuǎn)換到L*a*b*顏色空間,運用K均值聚類算法對水稻冠層圖像進(jìn)行聚類分析、圖像分割,提取出水稻穗、獲得水稻穗數(shù)量、代入水稻產(chǎn)量估算公式進(jìn)行估產(chǎn)。試驗區(qū)域共有18塊水稻小區(qū)(長8m,寬5m),在水稻抽穗期到成熟期之間拍攝4次。試驗記錄的數(shù)據(jù)包括拍攝的時間、高度以及分辨率,同時還要在田間實測水稻穗的數(shù)量和水稻的產(chǎn)量,為后期評價和判斷K均值聚類算法提取水稻穗的精度以及水稻產(chǎn)量估測的精度提供依據(jù)。對水稻產(chǎn)量的實測值與估測值、田間實測的水稻穗數(shù)量與圖像中提取水稻穗數(shù)量進(jìn)行對比分析。結(jié)果表明:對8月18日無人機(jī)拍攝的水稻冠層影像進(jìn)行圖像分割,提取出水稻穗的效果較好,估產(chǎn)的精度較高,產(chǎn)量估計均方根誤差和平均絕對百分誤差分別為9.08和22.8%,水稻穗數(shù)估計均方根誤差和平均絕對百分誤差分別為19.86和5.8%。說明利用無人機(jī)搭載數(shù)碼相機(jī)能夠快速、無損地獲取水稻冠層信息,運用K均值聚類算法能夠較為準(zhǔn)確地將水稻穗從水稻冠層圖像中分割出來,利用數(shù)字圖像對水稻產(chǎn)量進(jìn)行估算是可行的。
[Abstract]:In order to obtain rice canopy information quickly and flexibly and improve the resolution, the commonly used methods for estimating rice yield are satellite remote sensing, which has the advantages of low resolution, lack of mechanical rationality and large error. The rice yield was estimated accurately, and the high-definition digital camera was used on the UAV platform to capture the rice canopy image from heading stage to mature stage. Firstly, the median filtering algorithm was used to de-noise the rice canopy image in RGB color space. Then according to the color characteristics of the color rice image, the image is transformed from RGB color space to Lena color space. The K-means clustering algorithm is used to cluster the rice canopy image, image segmentation, rice panicle extraction, and the number of rice panicles is obtained. A total of 18 rice plots (8 m in length and 5 m wide) were used to estimate rice yield. The data recorded in the experiment included the time, height and resolution of the shoot, which were taken 4 times between the heading stage and the maturity stage of rice. At the same time, the number of rice panicles and the yield of rice were measured in the field, which provided the basis for the later evaluation and judgement of the precision of rice panicle extraction by K-means clustering algorithm and the precision of rice yield estimation. The number of rice panicles measured in the field was compared with the number of rice panicles extracted from the images. The results showed that the rice crown images taken by UAV on August 18th had a good effect of extracting rice panicles and the precision of estimating yield was higher. The root mean square error and average absolute percent error of yield estimation are 9.08 and 22.80.The root mean square error and average absolute percentage error of rice panicle estimation are 19.86 and 5.80.It shows that using UAV to carry digital camera can be rapid. Using K-means clustering algorithm, rice panicles can be segmented accurately from rice canopy images, and it is feasible to estimate rice yield by using digital images.
【作者單位】： 沈陽農(nóng)業(yè)大學(xué)信息與電氣工程學(xué)院/遼寧省農(nóng)業(yè)信息化工程技術(shù)中心;
【基金】：國家重點研發(fā)項目(2016YFD0200700,2017YFD0300706) 遼寧省教育廳課題重點項目(LSNZD201605)
【分類號】：S127;S511

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