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.

Microwave and millimeter wave reflectometry, a form of continuous-wave surface penetrating radar, is an emerging non-destructive inspection technique that is suitable for non-metallic pipelines. This article shows a K-band microwave reflectometry instrument implemented onto an in-line pipecrawling robot, which raster-scanned cracks and external wall loss on a high-density polyethylene (HDPE) pipe of diameter 150 mm and wall thickness 9.8 mm. The pipe was scanned with three environments surrounding the pipe that approximated the use cases of over-ground HDPE pipelines, plastic-lined metal pipes, and undersea HDPE pipelines. The instrument was most sensitive when cracks were oriented parallel to its magnetic (H) plane. Any small variation in the standoff distance between the instrument's probe antenna and the pipe wall, which was not easy to avoid, was found to obscure the image. To mitigate this problem, a sensitivity analysis showed that an optimal frequency can be chosen at which standoff distance can vary by up to ±0.75 mm within a certain range without distorting the indications of defects on the image.

Abstract-A multispectral imaging system is presented, using components that will support its deployment within the world of small-holder agriculture. An active narrowband illumination setup was selected, which allowed a low-cost broadband image sensor to be used. The preliminary set-up has been demonstrated with droughted tomato plants as a proof of concept. The results demonstrated a 5, 28 and 90% deterioration after day 1, 2 and 3 respectively; calculated by the disease/water stress index. Initial analysis showed that for specific applications the device be used in lieu of high-cost diffraction gratings, however additional innovation is required to negate unwanted sensing phenomena.

No abstract