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水稻病害孢子多光譜衍射識別與病害源定位方法研究
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國家自然科學基金(面上)項目(32171895),、國家重點研發(fā)計劃項目(2019YFC1606600-03)、江蘇大學農(nóng)裝學部項目(NZXB20200205),、水稻生物學國家重點實驗室開放項目(20200302)和湛江市科技計劃項目(2021A05235)


Multispectral Diffraction Identification of Rice Disease Spores and Localization Method of Disease Source
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    摘要:

    水稻真菌病害主要依賴真菌孢子在空氣中進行傳播,。然而各種水稻病害孢子的形態(tài)相近,傳統(tǒng)孢子捕捉儀和顯微圖像法難以對其進行區(qū)分,。為了能夠準確識別目標病害孢子并進行病害源定位,,提出了一種水稻病害孢子多光譜衍射識別與病害源定位方法。為了解決傳統(tǒng)衍射方法無法識別形態(tài)相似的缺點,,設計了一種大視場,、無透鏡的多光譜衍射成像傳感器。通過分析病害孢子衍射指紋圖譜,,解析稻瘟病菌,、稻曲病菌孢子多光譜衍射成像特征規(guī)律。融合孢子的形態(tài)特征和吸收特性,,提出指紋分離強度和相對峰差兩個特征參數(shù),,建立孢子的多光譜衍射識別模型,。通過仿真計算實驗分析孢子傳播規(guī)律,耦合環(huán)境信息建立孢子傳播過程中的擴散模型,。在無定向風及有定向風條件下分析孢子的空間分布情況,,提出病害爆發(fā)源迭代質心定位算法。實驗結果表明,,本文方法對水稻病害孢子的識別率達到98.5%,,對無定向風條件下的定位誤差最低為4.9%,對有定向風條件下的定位誤差最低為7.1%,。

    Abstract:

    Rice fungal diseases mainly rely on fungal spores for airborne transmission. However, the morphology of various rice disease spores is similar, and it is difficult to distinguish them by traditional spore trap and microscopic image methods. To be able to accurately identify target disease spores and locate the disease source, a multispectral diffraction identification and disease source localization method for rice disease spores was proposed. A large field-of-view, lens-free multispectral diffraction imaging sensor was designed to address the shortcomings of traditional diffraction methods that cannot identify morphological similarities. By analyzing the disease spore diffraction fingerprinting, the multi-spectral diffraction imaging characteristic pattern of rice blast and rice curd spores was analyzed. By integrating the morphological characteristics and absorption properties of spores, two characteristic parameters of fingerprint separation intensity and relative peak difference were proposed to establish the multispectral diffraction identification model of spores. The spore propagation law was analyzed by simulation and calculation experiments, and the diffusion model in the process of spore propagation was established by coupling environmental information. The spatial distribution of spores was analyzed under the conditions of non-directional wind and directional wind, and an iterative plasmodial localization algorithm of the disease outbreak source was proposed. The experimental results showed that the recognition rate of rice disease spores reached 98.5%, and the localization error was as low as 4.9% for undirected wind conditions and 7.1% for directed wind conditions. This method can provide a reference in locating the source of crop disease outbreaks.

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楊寧,張?zhí)炀?張釗源,張曉東,毛罕平,袁壽其.水稻病害孢子多光譜衍射識別與病害源定位方法研究[J].農(nóng)業(yè)機械學報,2023,54(4):250-258. YANG Ning, ZHANG Tianwei, ZHANG Zhaoyuan, ZHANG Xiaodong, MAO Hanping, YUAN Shouqi. Multispectral Diffraction Identification of Rice Disease Spores and Localization Method of Disease Source[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(4):250-258.

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  • 收稿日期:2022-06-26
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  • 在線發(fā)布日期: 2022-08-23
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