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基于混合像元分解的分蘗期水稻基本苗數(shù)量估測(cè)方法研究
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江蘇省農(nóng)業(yè)科技自主創(chuàng)新資金項(xiàng)目(CX(21)3061)、國(guó)家自然科學(xué)基金項(xiàng)目(61901194、52309051)、江蘇大學(xué)第22批大學(xué)生科研課題立項(xiàng)項(xiàng)目(22A249)和江蘇省優(yōu)勢(shì)學(xué)科項(xiàng)目(PAPD-2018-87)


Estimation of Rice Basic Seedling Number Based on Mixed Pixel Decomposition
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    摘要:

    基本苗數(shù)量是反映水稻健康水平的重要依據(jù),在分蘗期精準(zhǔn)估測(cè)水稻基本苗數(shù)量可以指導(dǎo)后期的施肥量,從而調(diào)控水稻的最佳分蘗數(shù)。同時(shí),對(duì)水稻長(zhǎng)勢(shì)監(jiān)測(cè)和產(chǎn)量預(yù)測(cè)具有非常重要的意義。針對(duì)傳統(tǒng)田間人工統(tǒng)計(jì)基本苗數(shù)量耗時(shí)長(zhǎng)、成本高等問(wèn)題,以江蘇大學(xué)附屬農(nóng)場(chǎng)鎮(zhèn)江潤(rùn)果農(nóng)場(chǎng)分蘗期水稻為研究對(duì)象,利用大疆無(wú)人機(jī)(M600 Pro型)搭載多光譜相機(jī)(Rededge-MX型)獲取水稻分蘗期多光譜數(shù)據(jù),對(duì)原始圖像進(jìn)行圖像拼接、輻射校正、幾何校正等預(yù)處理操作,根據(jù)像元純度系數(shù)提取土壤端元和植被端元,建立波譜庫(kù),然后按照完全約束最小二乘法的方法執(zhí)行混合像元分解,構(gòu)建植被覆蓋度和水稻基本苗數(shù)量的回歸模型。該研究方法獲得的模型決定系數(shù)R2為0.891,均方根誤差RMSE為4.6株/m2。而傳統(tǒng)的像元二分法模型(基于NDVI、VDVI和GNDVI植被指數(shù)計(jì)算植被覆蓋度),其決定系數(shù)R2為0.834、0.744、0.642,其RMSE為5.7、7.1、8.4株/m2。試驗(yàn)結(jié)果表明,基于完全約束最小二乘法的混合像元分解模型評(píng)價(jià)指標(biāo)均優(yōu)于像元二分法模型。本文基于混合像元分解方法有效提高了水稻基本苗統(tǒng)計(jì)精度,并且生成了水稻基本苗數(shù)量反演圖,可以直觀統(tǒng)計(jì)基本苗數(shù)量,為分蘗期水稻補(bǔ)苗、間苗提供指導(dǎo)。

    Abstract:

    The basic seedling number is an important basis to reflect the health level of rice. Accurately estimating the basic seedling number at tillering stage can guide the fertilizer and nitrogen amount in later stage, so as to regulate the optimal tillering number of rice. At the same time, it is of great significance for rice growth monitoring and yield forecasting. Considering that traditional manual field statistics on the number of basic seedlings are time-consuming and costly, this experiment took rice at tillering stage in Zhenjiang Runguo Farm, affiliated farm of Jiangsu University, as the research object, and used DJI UAV (M600 Pro) equipped with multi-spectral camera (Rededge-MX) to obtain multi-spectral data of rice at tillering stage. After image splicing, radiometric correction, geometric correction and other pretreatment operations were carried out on the original image, the soil end elements and vegetation end elements were extracted according to the pixel purity coefficient, and the spectral library was established. Then the mixed pixel decomposition was performed according to the fully constrained least square method, and the regression model of vegetation coverage and the number of basic rice seedlings was constructed. The model determination coefficient obtained by this method was 0.891, and the root mean square error RMSE was 4.6 plants/m2. In the traditional pixel dichotomy model (based on NDVI, VDVI and GNDVI vegetation index), the determination coefficients of R2 were 0.834, 0.744 and 0.642, and the RMSE were 5.7 plants/m2, 7.1 plants/m2 and 8.4 plants/m2. The experimental results showed that the evaluation indexes of the model based on the hybrid pixel decomposition method were superior to the pixel dichotomy model. The statistical accuracy of rice basic seedlings can be effectively improved based on the decomposition of mixed pixel decomposition, and the inverse map of rice basic seedling number is generated, which can directly count the basic seedling number and provide guidance for rice seedling replacement and thinning at tillering stage.

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朱文靜,戴世元,馮展康,段凱文,邵長(zhǎng)鋒,魏新華.基于混合像元分解的分蘗期水稻基本苗數(shù)量估測(cè)方法研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(6):202-209. ZHU Wenjing, DAI Shiyuan, FENG Zhankang, DUAN Kaiwen, SHAO Changfeng, WEI Xinhua. Estimation of Rice Basic Seedling Number Based on Mixed Pixel Decomposition[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(6):202-209.

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  • 收稿日期:2023-10-31
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  • 在線發(fā)布日期: 2024-06-10
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