基于光谱传感器的番茄花生芽坏死病毒实时检测和严重程度评估装置

IF 4.2 2区 计算机科学 Q2 ROBOTICS
Karishma Kumari, Roaf Parray, Y. B. Basavaraj, Samarth Godara, Indra Mani, Rajeev Kumar, Tapan Khura, Susheel Sarkar, Rajeev Ranjan, Hasan Mirzakhaninafchi
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引用次数: 0

摘要

利用基于机器学习的方法开发了一种用于检测和估计番茄植株(Solanum lycopersicum L.)花生芽坏死病毒(GBNV)病害严重程度的设备。该研究包括给番茄植株接种 GBNV,监测形态和光谱特征的变化,评估机器学习算法(决策树 [DT] 分类器)对疾病严重程度的分析和分类,以及开发和验证用于疾病检测和严重程度评估的设备。光谱数据分析揭示了反射率的独特模式,在 680 和 760 纳米波段观察到明显的峰值,而 900 纳米波段以上的反射率仍然较低且保持不变。利用机器学习技术,特别是 DT 模型,根据光谱数据对疾病严重程度进行了分类,准确率很高(训练准确率为 95.01%,测试准确率为 93.65%)。该模型确定近红外波段与疾病严重程度高度相关(相关系数为 0.82)。此外,还开发了一种集成了光谱传感器、有机发光二极管显示器和 Raspberry Pi 3B 的紧凑型手持设备,用于实时估计疾病严重程度。该设备性能稳定,即使在没有可见症状的情况下,也能准确预测不同生长阶段的病害严重程度。此外,通过反转录聚合酶链反应获得的疾病严重程度百分比也用于验证该设备估算的准确性。该设备反应灵敏,估计响应时间从几毫秒到几秒钟不等,有助于在农业环境中进行及时干预。总之,这种将光谱分析、机器学习和设备开发相结合的跨学科方法,为农业中高效的疾病监测和管理提供了一种前景广阔的解决方案,有助于增强作物健康和粮食安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectral sensor-based device for real-time detection and severity estimation of groundnut bud necrosis virus in tomato

A machine learning-based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near-infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light-emitting diode display, and Raspberry Pi 3B was developed for real-time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.

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来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
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