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蔬菜中大腸桿菌的機(jī)器視覺快速檢測
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“十一五”國家科技支撐計劃資助項(xiàng)目(51105167)和工程仿生教育部重點(diǎn)實(shí)驗(yàn)室開放基金資助項(xiàng)目（K201207A）


Rapid Detection Based on Machine Vision for Escherichia coli in Vegetables
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    摘要:

    為了適應(yīng)蔬菜等農(nóng)產(chǎn)品對大腸桿菌快速檢測的需求，提出采用形態(tài)特征參數(shù)及染色后菌體區(qū)域的顏色特征參數(shù)統(tǒng)計值對大腸桿菌進(jìn)行快速識別,，同時提出采用主成分神經(jīng)網(wǎng)絡(luò)作為預(yù)測模型來提高識別能力,。提取了Hu’s不變矩,、形狀因子,、密集度,、飽和度等14個具有尺度,、平移,、旋轉(zhuǎn)不變性的特征參數(shù),，提取主成分建立了基于主成分的3層BP神經(jīng)網(wǎng)絡(luò)模型,。將其與普通神經(jīng)網(wǎng)絡(luò)模型比較的結(jié)果表明,，主成分神經(jīng)網(wǎng)絡(luò)簡化了網(wǎng)絡(luò)結(jié)構(gòu)、減少了訓(xùn)練時間和計算量,、提高了識別的正確率,，對大腸桿菌的識別正確率達(dá)到91.33%。

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    In order to adapt to the requests of onsite rapid detection technique of Escherichia coli (E.coli) for the safety of agricultural products, a rapid E.coli recognition method based on shape and color feature parameters was proposed. Principal component neural network was used to improve the recognition ability. Principal component analysis was applied to the 14 extracted feature parameters, including Hu’s moment invariants, shape factor, denseness and saturation, et al. A three-layer BP neural network model based on the principal components was constructed. Compared with traditional BP neural network, the configuration of the principal component neural network was simpler, the training time was shorter and the recognition accuracy was higher. The recognition accuracy of the principal component neural network can arrived at 91.33%. 

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丁筠,殷涌光,王旻.蔬菜中大腸桿菌的機(jī)器視覺快速檢測[J].農(nóng)業(yè)機(jī)械學(xué)報,2012,43(2):134-139,145. Ding Yun, Yin Yongguang, Wang Min. Rapid Detection Based on Machine Vision for Escherichia coli in Vegetables[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(2):134-139,145.

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  • 在線發(fā)布日期: 2012-02-17
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