Gerrit Polder

Calibration and characterisation of imaging spectrographs

Spectrograph-based spectral imaging systems provide images with a large number of contiguous spectral channels per pixel. This paper describes the calibration and characterisation of such systems. The relation between pixel position and measured wavelength has been determined using three different wavelength calibration sources. Results indicate that for spectral calibration, a source with very narrow peaks, such as a HgAr source, is preferred to narrow band filters. A third-order polynomial model gives an appropriate fit 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 is lower than in the green and red part. This is due to a decreased quantum efficiency of the sensor, a smaller transmission coefficient of the spectrograph, as well as low output power of the illuminant. Increasing the amount of blue light, using an additional fluorescent tube with a special coating considerably increases the SNR. Furthermore, the spatial and spectral resolution of the system has been determined in relation to the wavelength. These can be used to choose appropriate binning factors to decrease the image size without losing information. In our case this could reduce the image size by a factor of 60 or more.

SPICY: towards automated phenotyping of large pepper plants in the greenhouse

Most high-throughput systems for automated plant phenotyping involve a fixed recording cabinet to which plants are transported. However, important greenhouse plants like pepper are too tall to be transported. In this research we developed a system to automatically measure plant characteristics of tall pepper plants in the greenhouse. With a device equipped with multiple cameras, images of plants are recorded at a 5cm interval over a height of 3m. Two types of features are extracted: (1) features from a 3D reconstruction of the plant canopy; and (2) statistical features derived directly from RGB images. The experiment comprised 151 genotypes of a recombinant inbred population of pepper, to examine the heritability and quantitative trait loci (QTL) of the features. Features extracted from the 3D reconstruction of the canopy were leaf size and leaf angle, with heritabilities of 0.70 and 0.56 respectively. Three QTL were found for leaf size, and one for leaf angle. From the statistical features, plant height showed a good correlation (0.93) with manual measurements, and QTL were in accordance with QTL of manual measurements. For total leaf area, the heritability was 0.55, and two of the three QTL found by manual measurement were found by image analysis.

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 (

A robot to detect and control broad-leaved dock ( Rumex obtusifolius L.) in grassland

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Detection of the tulip breaking virus (TBV) in tulips using optical sensors

>90xa0% 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.

Measuring Ripening of Tomatoes Using Imaging Spectrometry

Volunteer potato is a perennial weed that is difficult to control in crop rotations. It was our objective to build a small, low-cost robot capable of detecting volunteer potato plants in a cornfield and thus demonstrate the potential for automatic control of this weed. We used an electric toy truck as the basis for our robot. We developed a fast row-recognition algorithm based on the Hough transform and implemented it using a webcam. We developed an algorithm that detects the presence of a potato plant based on a combination of size, shape, and color of the green elements in an image and implemented it using a second webcam. The robot was able to detect potatoes while navigating autonomously through experimental and commercial cornfields. In a first experiment, 319 out of 324 images were correctly classified (98.5%) as showing, or not showing, a potato plant. In a second experiment, 126 out of 141 images were correctly classified (89.4%). Detection of a potato plant resulted in an acoustic signal, but future robots may be fitted with weed control equipment, or they may use a global positioning system to map the presence of weed plants so that regular equipment can be used for control. Nomenclature: Corn, Zea mays L, Potato, Solanum tuberosum L. Additional index words: Autonomous navigation, autonomous weeding, glyphosate, machine-vision, site-specific weed control. Abbreviations: DIPlib, Delft image-processing library; DSP, digital signal processor; GPS, global positioning system; JPEG, Joint Photographic Experts Group; NiMh, nickle metal hydride; PC, personal computer.

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

Broad-leaved dock is a common and troublesome grassland weed with a wide geographic distribution. In conventional farming the weed is normally controlled by using a selective herbicide, but in organic farming manual removal is the best option to control this weed. The objective of our work was to develop a robot that can navigate a pasture, detect broad-leaved dock, and remove any weeds found. A prototype robot was constructed that navigates by following a predefined path using centimeter-precision global positioning system (GPS). Broad-leaved dock is detected using a camera and image processing. Once detected, weeds are destroyed by a cutting device. Tests of aspects of the system showed that path following accuracy is adequate but could be improved through tuning of the controller or adoption of a dynamic vehicle model, that the success rate of weed detection is highest when the grass is short and when the broad-leaved dock plants are in rosette form, and that 75% of weeds removed did not grow back. An on-farm field test of the complete system resulted in detection of 124 weeds of 134 encountered (93%), while a weed removal action was performed eight times without a weed being present. Effective weed control is considered to be achieved when the center of the weeder is positioned within 0.1 m of the taproot of the weed—this occurred in 73% of the cases. We conclude that the robot is an effective instrument to detect and control broad-leaved dock under the conditions encountered on a commercial farm.

Automated estimation of leaf area development in sweet pepper plants from image analysis

This paper shows how stereo and Time-of-Flight (ToF) images can be combined to estimate dense depth maps in order to automate plant phenotyping. We focus on some challenging plant images captured in a glasshouse environment, and show that even the state-of-the-art stereo methods produce unsatisfactory results. By developing a geometric approach which transforms depth information in a ToF image to a localised search range for dense stereo, a global optimisation strategy is adopted for producing smooth and discontinuity-preserving results. Since pixel-by-pixel depth data are unavailable for our images and many other applications, a quantitative method accounting for the surface smoothness and the edge sharpness to evaluate estimation results is proposed. We compare our method with and without ToF against other state-of-the-art stereo methods, and demonstrate that combining stereo and ToF images gives superior results.