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基于細(xì)粒度校正的育種小區(qū)小麥株高無(wú)人機(jī)測(cè)量方法
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陜西省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2021NY-045)、校級(jí)科技創(chuàng)新與成果轉(zhuǎn)化項(xiàng)目(TGZX2021-30)和校級(jí)學(xué)科建設(shè)專項(xiàng)經(jīng)費(fèi)項(xiàng)目


UAV Measurement of Plant Height of Breeding Wheat Based on Fine-scale Correction
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    摘要:

    在小麥育種田間試驗(yàn)中,小區(qū)群體株高是最受關(guān)注的重要農(nóng)藝性狀之一。針對(duì)當(dāng)前無(wú)人機(jī)遙感在小麥育種小區(qū)粒度下獲取株高表型精確度低的問(wèn)題,提出了兩種方法:基于人工測(cè)量真值的近鄰校正法(Nearest neighbor correction method,NNCM)和基于多光譜+RGB數(shù)據(jù)融合的光譜指數(shù)校正法(Spectral indices correction method,SICM),近鄰校正法通過(guò)獲取小區(qū)群體高程信息、結(jié)合地埂進(jìn)行高程校正、再依據(jù)近鄰真值滑動(dòng)校正得到小區(qū)精確株高;光譜指數(shù)校正法通過(guò)計(jì)算植被指數(shù)并進(jìn)行指數(shù)優(yōu)選,從而構(gòu)建株高-植被指數(shù)精確反演模型。試驗(yàn)結(jié)果表明,在具有地面真值的6個(gè)時(shí)期,傳統(tǒng)無(wú)人機(jī)作物株高測(cè)量方法的相對(duì)均方根誤差(Relative root mean square error, RMSE100)分別為11.15%、59.44%、11.76%、12.31%、8.05%、59.76%;NNCM的RMSE100分別為7.17%、8.18%、5.70%、5.62%、5.65%、7.74%;SICM的RMSE100分別為7.33%、8.17%、6.05%、6.15%、6.45%、10.50%;NNCM與SICM核密度分布曲線與地面真值更加接近,中位數(shù)、四分位數(shù)、最大值、最小值偏差不超過(guò)0.5%,表明提出的2種方法均可以校正無(wú)人機(jī)測(cè)量育種小區(qū)粒度上的株高性狀。本文所提2種方法具有較高的精確度和較強(qiáng)的魯棒性,NNCM適用于具有地面隨機(jī)采樣真值的場(chǎng)景,SICM則適用于大范圍的農(nóng)田株高檢測(cè),可依據(jù)不同使用條件選擇相應(yīng)的校正方法。

    Abstract:

    In the field experiment of wheat breeding, an important measurement index is the plant height of the plot population. To solve the problem of low accuracy of wheat plant height measurement based on UAV remote sensing, two methods were proposed, including a nearest neighbor correction method (NNCM) and a spectral index correction method (SICM). NNCM based on the true value of manual measurement, the height information of the community group was obtained, the elevation correction was carried out in combination with the ridge, and then the accurate plant height of the community was obtained by sliding correction according to the true value of the neighbor. SICM of multi-spectral + RGB data fusion, by calculating vegetation index and performing index optimization, an accurate inversion model of plant height-vegetation index was constructed. The test results showed that the relative root mean square error (RMSE100) of the traditional UAV crop height measurement method in the six periods with ground truth were 11.15%, 59.44%, 11.76%, 12.31%, 8.05% and 59.76%; the RMSE100 of NNCM were 7.17%, 8.18%, 5.70%, 5.62%, 5.65% and 7.74%; the RMSE100 of SICM were 7.33%, 8.17%, 6.05%, 6.15%, 6.45% and 10.50%; the NNCM and SICM kernel density distribution curves were closer to the ground truth, and the median, quartile, maximum, and minimum deviations did not exceed 0.5%. These indicated that both the proposed methods can correct the plant height traits on the grain size of breeding plots measured by UAV. The two models proposed had high accuracy and strong robustness. NNCM was suitable for the scene of random sampling of ground truth on the ground, while SICM was used for plant height detection of largescale farmland, and different methods were selected according to the using conditions.

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吳婷婷,劉昕哲,聶睿琪,劉佳,武璐,李濤.基于細(xì)粒度校正的育種小區(qū)小麥株高無(wú)人機(jī)測(cè)量方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2023,54(6):158-167. WU Tingting, LIU Xinzhe, NIE Ruiqi, LIU Jia, WU Lu, LI Tao. UAV Measurement of Plant Height of Breeding Wheat Based on Fine-scale Correction[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(6):158-167.

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  • 收稿日期:2022-10-29
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  • 在線發(fā)布日期: 2023-04-12
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