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基于TOPSIS和BP神經(jīng)網(wǎng)絡(luò)的高標準農(nóng)田綜合識別
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國家高技術(shù)研究發(fā)展計劃(863計劃)項目(2013AA10230103)


Multi-characteristic Comprehensive Recognition of Well-facilitied Farmland Based on TOPSIS and BP Neural Network
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    摘要:

    為提高耕地綜合生產(chǎn)能力,,適應(yīng)農(nóng)業(yè)現(xiàn)代化發(fā)展需求,,我國提出了高標準農(nóng)田建設(shè)的重大戰(zhàn)略部署,。高標準農(nóng)田的識別是建設(shè)前選址和建設(shè)后評價的基礎(chǔ),。本文以耕地圖斑為基本單元,融合遙感影像等多源數(shù)據(jù),從本底條件,、空間形態(tài),、建設(shè)水平、生態(tài)防護等方面,,構(gòu)建農(nóng)田綜合質(zhì)量多特性表征體系,,采用逼近理想點排序法(TOPSIS)進行初步評價,再以人機交互的方式選取各質(zhì)量等級農(nóng)田的真值樣本,,進一步采用BP神經(jīng)網(wǎng)絡(luò)算法修正各特性權(quán)值,得到農(nóng)田綜合質(zhì)量的精確評價結(jié)果,,實現(xiàn)高標準農(nóng)田識別,。以吉林省大安市為研究區(qū),研究結(jié)果表明:基于多特性表征體系的農(nóng)田綜合質(zhì)量評價方法精度達到96%以上,;研究區(qū)高標準農(nóng)田面積廣大,,主要分布在耕地集中連片、道路通達,、生態(tài)防護良好,、具有農(nóng)業(yè)現(xiàn)代化生產(chǎn)優(yōu)勢的東北部、中北部,、西北部邊緣和部分南部區(qū)域,;當?shù)匾褌浒傅母邩藴兽r(nóng)田和未備案、有潛力的高質(zhì)量農(nóng)田區(qū)域均得到有效識別,。

    Abstract:

    China puts forward the major strategic deployment of constructing well-facilitied farmland vigorously to improve the overall production capacity of farmland and adapt to the development of agricultural modernization. The recognition of well-facilitied farmland is foundation of site selection before constructing and evaluation after constructing. The well-facilitied farmland was understood from the point of view of production demand and recognized based on the evaluation of farmland comprehensive quality. Firstly,,the characteristics of farmland comprehensive quality was analyzed from a lot of angles, such as background condition, spatial shape, construction level, ecological protection and so on, by fusing the multi-source data and taking the farmland patches as the basic units. The description system of farmland comprehensive quality was built by using five characteristics, including soil productivity, land contiguous, field shape, road accessibility and ecological protection. Secondly, it assumed that these five characteristics were the same important for farmland comprehensive quality, so the weights were all made as 0.20 and the preliminary evaluation results were got by TOPSIS method. Thirdly, the true-value samples were acquired by using the combined method of preliminary evaluation results and man-machine interactive optimization. The man-machine interactive optimization was achieved by spatial overlay between the preliminary evaluation results and the farmland utilization grade from the farmland-grading work in China. And then BP neural network was used to fix the feature weights. Fourthly, the final accurate comprehensive quality evaluation results were got and the recognition of the well-facilitied farmland was achieved. Finally, Daan City in Jilin Province was taken as the study area. The research results showed that the accuracy of the method to evaluate farmland comprehensive quality was above 96%, basing on the multi-characteristic description system. The well-facilitied farmland was widely distributed in the study area. The well-facilitied farmland mainly concentrated in northeast, north, edge of northwest and part of the southern region. These regions had the advantage of agricultural modernization, such as concentrated farmland, villages, roads and forest. The well-facilitied farmland which was registered with the law and the prospective high-quality farmland which was not registered with the law were both recognized effectively. The above result had strong consistency on the spatial distribution with the preliminary evaluation results, but the former refined the comprehensive quality results of partial farmland based on the relative importance of each characteristic. The research result can provide scientific reference and technical support for regulation, protection and construction of well-facilitied farmland.

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呂雅慧,鄖文聚,張超,朱德海,楊建宇,陳英義.基于TOPSIS和BP神經(jīng)網(wǎng)絡(luò)的高標準農(nóng)田綜合識別[J].農(nóng)業(yè)機械學(xué)報,2018,49(3):196-204. Lü Yahui, YUN Wenju, ZHANG Chao, ZHU Dehai, YANG Jianyu, CHEN Yingyi. Multi-characteristic Comprehensive Recognition of Well-facilitied Farmland Based on TOPSIS and BP Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(3):196-204.

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  • 收稿日期:2017-07-19
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  • 在線發(fā)布日期: 2018-03-10
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