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基于人工神經(jīng)網(wǎng)絡(luò)的田間秸稈覆蓋率檢測(cè)系
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System for Residue Cover Rate in Field Based on BP Neural Network
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    摘要:

    以VC++為工具，田間實(shí)拍圖像為研究對(duì)象，在分析田間秸稈和土壤紋理特征差別的基礎(chǔ)上，設(shè)計(jì)了BP神經(jīng)網(wǎng)絡(luò)秸稈覆蓋率檢測(cè)系統(tǒng)。該系統(tǒng)采用了神經(jīng)網(wǎng)絡(luò)與紋理特征相結(jié)合的方法提取秸稈，并以紋理特征熵值為標(biāo)準(zhǔn)建立了網(wǎng)絡(luò)輸入層學(xué)習(xí)樣本選取準(zhǔn)則。人工模擬和田間試驗(yàn)表明，設(shè)計(jì)的BP神經(jīng)網(wǎng)絡(luò)秸稈覆蓋率檢測(cè)系統(tǒng)對(duì)田間秸稈的識(shí)別率達(dá)90%以上，秸稈覆蓋率計(jì)算誤差可控制在5%以內(nèi)；與傳統(tǒng)的拉繩法相比，檢測(cè)效率提高

    Abstract:

    50～120倍。According to the analyses of the texture differences between straw and soil, a new BP neural network measuring system for residue cover rate is designed. By taking the filed photos as the research objectives, this system was developed through VC++ programming tools. Straws were detected by combining the texture features and BP neural network. Selection standard of learning samples for input nodes was constructed based on the entropy in the system. Artificial simulation and field testing indicated that the new measuring system could detect over 90% of the straws in the field and control the counting error of residue cover rate under 5%. Compared with the traditional manual measuring, the measuring efficiency in the new system could be improved by 50～120 times. 

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李洪文,李慧,何進(jìn),李世衛(wèi).基于人工神經(jīng)網(wǎng)絡(luò)的田間秸稈覆蓋率檢測(cè)系[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2009,40(6):58-62. System for Residue Cover Rate in Field Based on BP Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(6):58-62.

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