Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape

Abstract

BackgroundThe use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance.ResultsThe proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation.ConclusionsThe ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability.

Monitoring Plant Status and Fertilization Strategy through Multispectral Images

“…Image processing techniques with multispectral cameras from visible to near-infrared spectrum are also being used to provide non-destructive plant phenotype image datasets. These approaches have allowed more precise and real-time, high throughput, and high-resolution data for the modelling and prediction of plant growth and morphological development in different conditions, with recent applications in plant health analysis [28][29][30].…”

Plant Disease Detection and Classification by Deep Learning

“…For early detection of plant diseases, several imaging techniques like multispectral imaging [91], thermal imaging, fluorescence and hyperspectral imaging are used [92]. Among them, hyperspectral imaging (HSI) is the focus of recent research.…”

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

“…Multispectral imaging involves data collection using a camera or other sensing device to produce image data in specified wavelength or waveband regions, while multispectral spectroscopy produces spectral data for specified wavebands. Both multispectral imaging and multispectral spectroscopy have been successfully used to identify plant stresses; for example, multispectral imaging was used to detect leaf spot disease in oilseed rape [54], gray mold in tomato leaves [55], and nutrient deficiencies in tomato plants [56], while multispectral spectroscopy was used to detect nitrogen deficiency stress in maize [57], drought stress in tomato plants [58], and nitrogen deficiency in canola plants [59]. Multispectral techniques offer more affordable sensors than their hyperspectral counterparts; however, they do not provide as much information about the plant and its environment due to the broader wavebands.…”