Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

被引:67
作者
Zubler, Alanna V. [1 ]
Yoon, Jeong-Yeol [1 ]
机构
[1] Univ Arizona, Dept Biosyst Engn, Tucson, AZ 85721 USA
来源
BIOSENSORS-BASEL | 2020年 / 10卷 / 12期
关键词
abiotic stress; plant disease; fluorescence; hyperspectral imaging; thermography; RGB imaging; smartphone imaging; support vector machine (SVM); artificial neural network (ANN); machine learning; CHLOROPHYLL FLUORESCENCE; NEURAL-NETWORK; WINTER-WHEAT; DISEASE DETECTION; SPECTRAL INDEXES; DROUGHT STRESS; WATER-STRESS; MOSAIC-VIRUS; BLUE-GREEN; REFLECTANCE;
D O I
10.3390/bios10120193
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.
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页数:27
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