The hype in spectral imaging

Hyperspectral imaging is currently a very well-known and much used technology for measuring features in different fields, such as chemistry, geology, medicine, food and agriculture, either spaceborne (satellites), airborne (drones) or at close proximity (e.g. field scanning, industrial sorting lines or microscopy). Its background is two-fold, and it can be considered as a special case of spectroscopy (“imaging spectroscopy”) or a special case of imaging (“spectral imaging”). Current practice is to use adjectives such as multi and hyper added to “spectral imaging” in order to characterise the number of wavelength bands. In this paper we propose the community to use scientifically sound terminology, like “imaging spectroscopy” or “spectral imaging”, without using ambiguous adjectives. Further, we encourage the community to define and agree upon clear adjectives to describe the number of variables in the naming of our imaging technique

Klimaat sturen op de inhoud van het blad

Growers would like to know the status of their crop to determine climate strategy and crop management practices. Chemical composition of the crop can now only be determined by sampling leaves or fruits, send this to a laboratory and wait for the analysis. In this project, we aimed to use hyperspectral imaging to determine the contents of sugars and starch, dry matter percentage, chlorophyll and nutrient composition in the crop. The results are promising. Hyperspectral cameras are very well capable to estimate the concentrations of sugars in leaves and fruits, chlorophyll content, dry matter percentage and specific leaf area. This allows the growers to adjust their climate settings or crop management based on these hyperspectral images

Effect of varying UAV height on the precise estimation of potato crop growth

A phenotyping pipeline utilising DeepLab was developed for precisely estimating the height, volume, coverage and vegetation indices of European and Japanese varieties. Using this pipeline, the effect of varying UAV height on the precise estimation of potato crop growth properties was evaluated. A UAV fitted with a multispectral camera was flown at a height of 15 m and 30 m in an experimental field where various varieties of potatoes were grown. The properties of plant height, volume and NDVI were evaluated and compared with the manually obtained parameters. Strong linear correlations with R2 of 0.803 and 0.745 were obtained between the UAV obtained plant heights and manually estimated plant height when the UAV was flown at 15 m and 30 m respectively. Furthermore, high linear correlations with an R2 of 0.839 and 0.754 were obtained between the UAV-estimated volume and manually estimated volume when the UAV was flown at 15 m and 30 m respectively. For the vegetation indices, there were no observable differences in the NDVI values obtained from the UAV flown at the two heights. Furthermore, high linear correlations with R2 of 0.930 and 0.931 were obtained between UAV-estimated and manually measured NDVI at 15 m and 30 m respectively. It was found that UAV flown at the lower height had a higher ground sampling distance thus increased resolution leading to more precise estimation of both the height and volume of crops. For vegetation indices, flying the UAV at a higher height had no effect on the precision of NDVI estimates

Non‐destructive automatic leaf area measurements by combining stereo and time‐of‐flight images

Leaf area measurements are commonly obtained by destructive and laborious practice. This study shows how stereo and time‐of‐flight (ToF) images can be combined for non‐destructive automatic leaf area measurements. The authors focus on some challenging plant images captured in a greenhouse environment, and show that even the state‐of‐the‐art stereo methods produce unsatisfactory results. By transforming depth information in a ToF image to a localised search range for dense stereo, a global optimisation strategy is adopted for producing smooth results that preserve discontinuity. They also use edges of colour and disparity images for automatic leaf detection and develop a smoothing method necessary for accurately estimating surface area. In addition to show that combining stereo and ToF images gives superior qualitative and quantitative results, 149 automatic measurements on leaf area using the authors system in a validation trial have a correlation of 0.97 with true values and the root‐mean‐square error is 10.97 cm2, which is 9.3% of the average leaf area. Their approach could potentially be applied for combining other modalities of images with large difference in image resolutions and camera positions

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain

Calibration and characterization of spectral imaging systems

Spectral image sensors provide images with a large number of contiguous spectral channels per pixel. This paper describes the calibration of spectrograph based spectral imaging systems. The relation between pixel position and measured wavelength was determined using three different wavelength calibration sources. Results indicate that for spectral calibration a source with very small peaks, such as a HgAr source, is preferred to narrow band filters. A second order polynomial model gives a better fit than a linear model for the pixel to wavelength mapping. The signal to noise ratio (SNR) is determined per wavelength. In the blue part of the spectrum, the SNR was lower than in the green and red part. This is due to a decreased quantum efficiency of the CCD, a smaller transmission coefficient of the spectrograph, as well as poor performance of the illuminant. Increasing the amount of blue light, using additional fluorescent tube with special coating increased the SNR considerably. Furthermore, the spatial and spectral resolution of the system are determined. These can be used to choose appropriate binning factors to decrease the image size without losing information

Close Range Spectral Imaging for Disease Detection in Plants Using Autonomous Platforms: a Review on Recent Studies

Purpose of ReviewA short introduction to the spectral imaging (SI) of plants along with a comprehensive overview of the recent research works related to disease detection in plants using autonomous phenotyping platforms is provided. Key benefits and challenges of SI for plant disease detection on robotic platforms are highlighted.Recent FindingsSI is becoming a potential tool for autonomous platforms for non-destructive plant assessment. This is because it can provide information on the plant pigments such as chlorophylls, anthocyanins and carotenoids and supports quantification of biochemical parameters such as sugars, proteins, different nutrients, water and fat content. A plant suffering from diseases will exhibit different physicochemical parameters compared with a healthy plant, allowing the SI to capture those differences as a function of reflected or absorbed light.SummaryPotential of SI to non-destructively capture physicochemical parameters in plants makes it a key technique to support disease detection on autonomous platforms. SI can be broadly used for crop disease detection by quantification of physicochemical changes in the plants

Header for SPIE use Comparison of multispectral images across the Internet

Comparison in the RGB domain is not suitable for precise color matching, due to the strong dependency of this domain on factors like spectral power distribution of the light source and object geometry. We have studied the use of multispectral or hyperspectral images for color matching, since it can be proven that hyperspectral images can be made independent of the light source (color constancy) and object geometry (normalized color constancy). Hyperspectral images have the disadvantage that they are large compared to regular RGB-images, which makes it infeasible to use them for image matching across the Internet. For red roses, it is possible to reduce the large number of bands (>100) of the spectral images to only three bands, the same number as of an RGB-image, using Principal Component Analysis, while maintaining 99 % of the original variation. The obtained PCA-images of the roses can be matched using for example histogram cross correlation. From the principal coordinates plot, obtained from the histogram similarity matrices of twenty images of red roses, the discriminating power seems to be better for normalized spectral images than for color constant spectral images and RGB-images, the latter being recorded under highly optimized standard conditions

Automated Detection of Mycosphaerella Melonis Infected Cucumber Fruits

In this paper we present a novel method for automated detection of Mycosphaerella melonis infected cucumber fruits. The two-step method consists of machine learning approach using: shape based features extracted from cucumber color images and light transmission spectra based features. The automated detection rate was compared to the manual detection rate of the human workers. Our automated method reached the 95% detection accuracy, which is comparable to the manual detection accuracy of 96%