Jon Nielsen

MAMP (microbe-associated molecular pattern) triggered immunity in plants

Plants are sessile organisms that are under constant attack from microbes. They rely on both preformed defenses, and their innate immune system to ward of the microbial pathogens. Preformed defences include for example the cell wall and cuticle, which act as physical barriers to microbial colonization. The plant immune system is composed of surveillance systems that perceive several general microbe elicitors, which allow plants to switch from growth and development into a defense mode, rejecting most potentially harmful microbes. The elicitors are essential structures for pathogen survival and are conserved among pathogens. The conserved microbe-specific molecules, referred to as microbe- or pathogen-associated molecular patterns (MAMPs or PAMPs), are recognized by the plant innate immune systems pattern recognition receptors (PRRs). General elicitors like flagellin (Flg), elongation factor Tu (EF-Tu), peptidoglycan (PGN), lipopolysaccharides (LPS), Ax21 (Activator of XA21-mediated immunity in rice), fungal chitin, and β-glucans from oomycetes are recognized by plant surface localized PRRs. Several of the MAMPs and their corresponding PRRs have, in recent years, been identified. This review focuses on the current knowledge regarding important MAMPs from bacteria, fungi, and oomycetes, their structure, the plant PRRs that recognizes them, and how they induce MAMP-triggered immunity (MTI) in plants.

How Important are Crop Spatial Pattern and Density for Weed Suppression by Spring Wheat

Abstract Previous research has shown that both the density and spatial pattern of wheat have an influence on crop growth and weed suppression, but it is not clear what degree of uniformity is necessary to achieve major improvements in weed suppression. Field experiments were performed over 3 yr to investigate the effects of crop density and different spatial distributions on weed suppression. The spatial pattern of spring wheat sown in five patterns and three densities in small weed-infested plots were analyzed with the use of digitized photographs of field plots to describe the locations of individual wheat plants as x and y coordinates. We used a simple quantitative measure, Morisitas index, to measure the degree of spatial uniformity. Increased crop density resulted in reduced weed biomass and increased crop biomass every year, but crop pattern had significant effects on weed and crop biomass in the first year only. Weather conditions during the second and third years were very dry, resulting in very low weed biomass production. We hypothesize that water deficiency increased the importance of belowground relative to aboveground competition by reducing biomass production, making competition more size symmetric, and reducing the effect of crop spatial pattern on weed growth. The results indicate that increased crop density in cereals can play an important role in increasing the crops competitive advantage over weeds, and that spatial uniformity maximizes the effect of density when low resource levels or abiotic stress do not limit total biomass production. Nomenclature: Spring wheat, Triticum aestivum L.

Detecting creeping thistle in sugar beet fields using vegetation indices

In this article, we address the problem of thistle detection in sugar beet fields under natural, outdoor conditions. In our experiments, we used a commercial color camera and extracted vegetation indices from the images. A total of 474 field images of sugar beet and thistles were collected and divided into six different groups based on illumination, scale and age. The feature set was made up of 14 indices. Mahalanobis Distance (MD) and Linear Discriminant Analysis (LDA) were used to classify the species. Among the features, excess green (ExG), green minus blue (GB) and color index for vegetation extraction (CIVE) offered the highest average accuracy, above 90%. The feature set was reduced to four important indices following a PCA analysis, but the classification accuracy was similar to that obtained by only combining ExG and GB which was around 95%, still better than an individual index. Stepwise linear regression selected nine out of 14 features and offered the highest accuracy of 97%. The results of LDA and MD were fairly close, making them both equally preferable. Finally, the results were validated by annotating images containing both sugar beet and thistles using the trained classifiers. The validation experiments showed that sunlight followed by the size of the plant, which is related to its growth stage, are the two most important factors affecting the classification. In this study, the best results were achieved for images of young sugar beet (in the seventh week) under a shade.

Exploiting affine invariant regions and leaf edge shapes for weed detection

We exploit affine invariant regions and leaf edge shapes for weed detection.Data contains field images of sugar beet and thistle.A new local vegetation color descriptor is also introduced.Bag of Visual Words approach is used with SVM classifier.Fusion of leaf color and edge signatures yields 99% accuracy. In this article, local features extracted from field images are evaluated for weed detection. Several scale and affine invariant detectors from computer vision literature along with high performance descriptors were applied. Field dataset contained a total of 474 plant images of sugar beet and creeping thistle, divided into six groups based on illumination, age, and camera to plant distance. To establish a performance baseline, leaf image retrieval potential of the selected features was first assessed on a publicly available leaf database containing flatbed scanned images of 15 tree species. Then a comparison with the field data retrieval highlighted the trade-off due to the field challenges. Adopting a comprehensive approach, edge shape detectors and homogeneous surface detecting affine invariant regions were fused. In order to integrate vegetation indices as local features, a new local vegetation color descriptor was introduced which used various combinations of color indices and offered a very high precision. Retrieval in the field data was evaluated group-wise. Although, the impact of the sunlight was found to be very low on shape features, but relatively higher precisions were obtained for younger plants under a shade (overall more than 80%). The weed detection accuracy was assessed using the Bag-of-Visual-Word scheme with KNN and SVM classifiers. The assessment showed that with an SVM classifier, a fusion of surface color and edge shapes boosted the overall classification accuracy to as high as 99.07% with a very low false negative rate (2%).

Automatic Detection of Thistle-Weeds in Cereal Crops from Aerial RGB Images

Capturing aerial images by Unmanned Aerial Vehicles (UAV) allows gathering a general view of an agricultural site together with a detailed exploration of its relevant aspects for operational actions. Here we explore the challenging task of detecting cirsium arvense, a thistle-weed species, from aerial images of barley-cereal crops taken from 50 m above the ground, with the purpose of applying herbicide for site-specific weed treatment. The methods for automatic detection are based on object-based annotations, pointing out the RGB attributes of the Weed or Cereal classes for an entire group of pixels, referring to a crop area which will have to be treated if it is classified as being of the Weed class. In this way, an annotation belongs to the Weed class if more than half of its area is known to be covered by thistle weeds. Hence, based on object and pixel-level analysis, we compare the use of k-Nearest Neighbours (k-NN) and (feed-forward, one-hidden layer) neural networks, obtaining the best results for weed detection based on pixel-level analysis, based on a soft measure given by the proportion of predicted weed pixels per object, with a global accuracy of over 98%.