Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress.We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress.Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species.
Many plants become limp or droop through heat, loss of water, or disease. This is also known as wilting. In this paper, we examine plant wilting caused by bacterial infection. In particular, we want to design a metric for wilting based on images acquired of the plant. A quantifiable wilting metric will be useful in studying bacterial wilt and identifying resistance genes. Since there is no standard way to estimate wilting, it is common to use ad hoc visual scores. This is very subjective and requires expert knowledge of the plants and the disease mechanism. Our solution consists of using various wilting metrics acquired from RGB images of the plants. We also designed several experiments to demonstrate that our metrics are effective at estimating wilting in plants.
Cases of emergence of novel plant-pathogenic strains are regularly reported that reduce the yields of crops and trees. However, the molecular mechanisms underlying such emergence are still poorly understood. The acquisition by environmental non-pathogenic strains of novel virulence genes by horizontal gene transfer has been suggested as a driver for the emergence of novel pathogenic strains. In this study, we tested such an hypothesis by transferring a plasmid encoding the type 3 secretion system (T3SS) and four associated type 3 secreted proteins (T3SPs) to the non-pathogenic strains of Xanthomonas CFBP 7698 and CFBP 7700, which lack genes encoding T3SS and any previously known T3SPs. The resulting strains were phenotyped on Nicotiana benthamiana using chlorophyll fluorescence imaging and image analysis. Wild-type, non-pathogenic strains induced a hypersensitive response (HR)-like necrosis, whereas strains complemented with T3SS and T3SPs suppressed this response. Such suppression depends on a functional T3SS. Amongst the T3SPs encoded on the plasmid, Hpa2, Hpa1 and, to a lesser extent, XopF1 collectively participate in suppression. Monitoring of the population sizes in planta showed that the sole acquisition of a functional T3SS by non-pathogenic strains impairs growth inside leaf tissues. These results provide functional evidence that the acquisition via horizontal gene transfer of a T3SS and four T3SPs by environmental non-pathogenic strains is not sufficient to make strains pathogenic. In the absence of a canonical effector, the sole acquisition of a T3SS seems to be counter-selective, and further acquisition of type 3 effectors is probably needed to allow the emergence of novel pathogenic strains.
Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental results show the viability of both our proposed ground truth approach and deep learning based stem segmentation.
Abstract. Breakthrough imaging technologies are a potential solution to the plant phenotyping bottleneck in marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA), however, advances in computational methods to digitally model spatial disposition of root system networks are urgently required.We extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results. Significant root architectural traits such as number, length, growth angle, elongation rate and branching map can be easily extracted from the skeleton. The curve-skeleton extraction is computed based on a constrained Laplacian smoothing algorithm. This skeletal structure drives the registration procedure in temporal series. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University in West Lafayette (IN, USA). Three samples of tomato root at 2 different times and three samples of corn root at 3 different times were scanned. The skeleton is able to accurately match the shape of the RSA based on a visual inspection.The results based on a visual inspection confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.
Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA).Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001.The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.
Dans le contexte actuel de reduction de l’utilisation des produits phytosanitaires, les produits de biocontrole representent une solution efficace mais qui se heurte encore a certaines problematiques. La premiere partie de cette etude concernait un antagoniste (AGX) de Botrytis cinerea qui presentait certaines difficultes pour le controle de cet agent pathogene sur vigne. Un pathosysteme a du etre mis en place afin de tester differentes voies d’ameliorations representees par l’ajout d’adjuvants, la combinaison d’antagonistes, l’ajout de substances actives a faibles risques et l’action preventive d’AGX. Les resultats ont montre que l’addition de bicarbonate de potassium et l’action preventive representaient des solutions d’amelioration interessantes. La deuxieme partie de cette etude a porte quant a elle sur l’etude preliminaire de Plasmopara viticola et avait pour objectif final la construction d’un pathosysteme permettant de reproduire les symptomes de l’infection. Celle-ci a permis dans un premier temps la mise au point de protocoles permettant l’entretien d’un agent pathogene ainsi que l’obtention d’un inoculum exempt de contaminant. Dans un second temps elle a assure la construction d’un pathosysteme Plasmopara viticola / disque foliaire permettant d’etudier l’impact de la quantite lumineuse ainsi que de la presence de glucose dans le milieu gelose sur le developpement de l’agent pathogene.
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