本文選題：葉片　切入點(diǎn)：植物種類　出處：《浙江農(nóng)業(yè)學(xué)報(bào)》2017年12期 　論文類型：期刊論文

【摘要】：為了提高植物種類的識(shí)別率,采用葉片圖像算法。首先建立植物種類特征模型,包括植物葉片顏色特征、形狀特征、紋理特征;然后確定徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的輸入層、輸出層、隱含層之間的關(guān)系;接著對(duì)徑向基函數(shù)個(gè)數(shù)、中心及寬度優(yōu)化,基于梯度下降方法對(duì)權(quán)重參數(shù)計(jì)算,自適應(yīng)調(diào)節(jié)學(xué)習(xí)率;最后給出了植物種類識(shí)別過程。實(shí)驗(yàn)仿真選擇植物葉片顏色特征、形狀特征、紋理特征的特征量分別為6、7、7個(gè),其中本文算法對(duì)植物種類識(shí)別的三個(gè)組合特征平均識(shí)別率為93.5%,高于單個(gè)特征、兩個(gè)組合特征的平均識(shí)別率,形狀特征對(duì)識(shí)別率所起的作用最大。
[Abstract]:In order to improve the recognition rate of plant species, the leaf image algorithm is used. Firstly, the plant species feature model is established, including the color feature, shape feature, texture feature of plant leaf, and then the input layer of radial basis function neural network is determined. The relationship between the output layer and the hidden layer, then the number, center and width of the radial basis function are optimized, the weight parameters are calculated based on gradient descent method, and the learning rate is adjusted adaptively. Finally, the process of plant species recognition is given. The number of color features, shape features and texture features of plant leaves are 6 7 and 7 respectively. The average recognition rate of the three combined features of this algorithm is 93.5, which is higher than that of a single feature. The average recognition rate of the two combined features and the shape feature play the most important role in the recognition rate.
【作者單位】： 黃河水利職業(yè)技術(shù)學(xué)院;
【基金】：基金項(xiàng)目:中國國家專利(公開號(hào)CN202189701U) 河南省科學(xué)技術(shù)成果(豫科鑒委字2013年第201號(hào))
【分類號(hào)】：Q94;TP391.41
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本文編號(hào)：1613705