Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

IF 4.6 4区 农林科学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Sishi Chen, Xuqi Lu, Hongda Fang, Anand Babu Perumal, Ruyue Li, Lei Feng, Mengcen Wang, Yufei Liu
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Abstract

Bakanae disease, caused by Fusarium fujikuroi, poses a significant threat to rice production and has been observed in most rice-growing regions. The disease symptoms caused by different pathogens may vary, including elongated and weak stems, slender and yellow leaves, and dwarfism, as example. Bakanae disease is likely to cause necrosis of diseased seedlings, and it may cause a large area of infection in the field through the transmission of conidia. Therefore, early disease surveillance plays a crucial role in securing rice production. Traditional monitoring methods are both time-consuming and labor-intensive and cannot be broadly applied. In this study, a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae disease. Phenotypic data were obtained on the 9th, 15th, and 21st day after rice infection to explore the physiological and biochemical performance, which helps to deepen the research on the disease mechanism. Hyperspectral data were obtained over these same periods of infection, and a deep learning model, named Rice Bakanae Disease-Visual Geometry Group (RBD-VGG), was established by leveraging hyperspectral imaging technology and deep learning algorithms. Based on this model, an average accuracy of 92.2% was achieved on the 21st day of infection. It also achieved an accuracy of 79.4% as early as the 9th day. Universal characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field surveillance. Collectively, the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease, thereby providing an efficient avenue for disease prevention and control.

利用深度学习和高光谱成像对水稻包虫病进行早期监测
由 Fusarium fujikuroi 引起的 Bakanae 病对水稻生产构成严重威胁,在大多数水稻种植区都有发生。不同病原体引起的疾病症状可能各不相同,例如茎细长而脆弱、叶片细长而发黄以及矮化。Bakanae 病很可能导致病苗坏死,并通过分生孢子的传播在田间造成大面积感染。因此,早期病害监测对保障水稻生产起着至关重要的作用。传统的监测方法既费时又费力,无法广泛应用。本研究结合高光谱成像技术和深度学习算法,实现了对感染包枯病的水稻秧苗的原位检测。研究获取了水稻感染后第9天、第15天和第21天的表型数据,探究其生理生化表现,有助于深化病害机理研究。在这些相同的感染期获得了高光谱数据,并利用高光谱成像技术和深度学习算法建立了一个名为 "水稻白叶枯病-视觉几何组(RBD-VGG)"的深度学习模型。基于该模型,在感染的第 21 天,平均准确率达到 92.2%。早在第 9 天,准确率也达到了 79.4%。提取的通用特征波长提高了使用便携式光谱设备进行现场监测的可行性。总之,该模型为监测包虫病提供了一种高效、非破坏性的监测方法,从而为疾病预防和控制提供了一条有效途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
7.70
自引率
2.80%
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