5G Fieldlab Rural Drenthe : duurzame en autonome onkruidbestrijding

Boeren kijken al een aantal jaren met een schuin oog naar robots om de saaie, herhaaldelijke en soms zware taken van hen over te nemen. Aardappelopslagbestrijding, een relatief simpele maar saaie taak, is één van de taken die boeren graag aan een autonome robot zouden overdragen. Robots om deze taak uit te voeren moesten telkens opgeven omdat het detecteren van aardappelopslagplanten in een suikerbietengewas voor de computer erg lastig bleek. Door state-of-the-art deep learning technologieën toe te passen is WUR er nu wel in geslaagd een robuust detectiealgoritme te bouwen. Vanuit een demo op de kleine Husky robot is gewerkt naar een 3 meter brede, autonome toepassing op de Robotti robot. De rekenintensieve operatie van het herkennen van aardappelopslag- en suikerbietenplanten werd hierbij in de cloud uitgevoerd, waarbij state-of-art 5G verbindingstechnieken gebruikt werden om de te analyseren data snel genoeg in de cloud en terug te kunnen krijgen om tijdig een actuatie te kunnen uitvoeren op de robot. Door nauwe samenwerking tussen KPN en WUR is een herkenningsalgoritme voor aardappel- en suikerbietenplanten ontwikkeld dat in een KPN-cloud omgeving kan draaien. Terwijl de robot met 4km/u over het veld reed werden de foto’s via 5G naar deze cloud gestuurd en werden de analyse-resultaten teruggestuurd naar de robot binnen 0.25 seconde. De spuit-unit met spuitdoppen om de 0.1 m werd daarop door de computer geïnstrueerd welke spuitdop wanneer geactiveerd moest worden om de gedetecteerde aardappelopslagplant te bespuiten. Tijdens toepassing van het algoritme in augustus werden 96% van de aardappelopslagplanten en 3% van de suikerbietenplanten geraakt. Hoewel deze getallen het systeem al zeer dicht richting praktijkintroductie brengen zal het aantal geraakte suikerbieten nog om laag moeten om boeren massaal te overtuigen van de toepasbaarheid van het systeem. Daar waar 5G genoemd wordt in dit verslag betekent dit het gebruik van pré 5G technologie met 5G capaciteit en performance

Genome-Wide Association Study Identifies First Locus Associated with Susceptibility to Cerebral Venous Thrombosis

Objective Cerebral venous thrombosis (CVT) is an uncommon form of stroke affecting mostly young individuals. Although genetic factors are thought to play a role in this cerebrovascular condition, its genetic etiology is not well understood. Methods A genome-wide association study was performed to identify genetic variants influencing susceptibility to CVT. A 2-stage genome-wide study was undertaken in 882 Europeans diagnosed with CVT and 1,205 ethnicity-matched control subjects divided into discovery and independent replication datasets. Results In the overall case-control cohort, we identified highly significant associations with 37 single nucleotide polymorphisms (SNPs) within the 9q34.2 region. The strongest association was with rs8176645 (combined p = 9.15 x 10(-24); odds ratio [OR] = 2.01, 95% confidence interval [CI] = 1.76-2.31). The discovery set findings were validated across an independent European cohort. Genetic risk score for this 9q34.2 region increases CVT risk by a pooled estimate OR = 2.65 (95% CI = 2.21-3.20, p = 2.00 x 10(-16)). SNPs within this region were in strong linkage disequilibrium (LD) with coding regions of the ABO gene. The ABO blood group was determined using allele combination of SNPs rs8176746 and rs8176645. Blood groups A, B, or AB, were at 2.85 times (95% CI = 2.32-3.52, p = 2.00 x 10(-16)) increased risk of CVT compared with individuals with blood group O. Interpretation We present the first chromosomal region to robustly associate with a genetic susceptibility to CVT. This region more than doubles the likelihood of CVT, a risk greater than any previously identified thrombophilia genetic risk marker. That the identified variant is in strong LD with the coding region of the ABO gene with differences in blood group prevalence provides important new insights into the pathophysiology of CVT. ANN NEUROL 2021Peer reviewe

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Improved generalization of a plant-detection model for precision weed control

Lack of generalization in plant-detection models is one of the main challenges preventing the realization of autonomous weed-control systems. This paper investigates the effect of the train and test dataset distribution on the generalization error of a plant-detection model and uses incremental training to mitigate the said error. In this paper, we use the YOLOv3 object detector as plant-detection model. To train the model and test its generalization properties we used a broad dataset, consisting of 25 sub-datasets, sampled from multiple different geographic areas, soil types, cultivation conditions, containing variation in weeds, background vegetation, camera quality and variations in illumination. Using this dataset we evaluated the generalization error of a plant-detection model, assessed the effect of sampling training images from multiple arable fields on the generalization of our plant-detection model, we investigated the relation between the number of training images and the generalization of the plant-detection model and we applied incremental training to mitigate the generalization error of our plant-detection model on new arable fields. It was found that the average generalization error of our plant-detection model was 0.06 mAP. Increasing the number of sub-datasets for training, while keeping the total number of training images constant, increased the variation covered by the training set and improved the generalization of our plant-detection model. Adding more training images sampled from the same datasets increased the generalization further. However, this effect is limited and only holds when the new images cover new variation. Naively adding more images does not prepare the model for specific scenarios outside the training distribution. Using incremental training the model can be adapted to such scenarios and the generalization error can be mitigated. Depending on the discrepancy between the training set and the new field, finetuning on as little as 25 images can already mitigate the generalization error

Autonome aanpak aardappelopslag : Resultaten activiteiten 2019-2020

Het beheersen van aardappelopslag in landbouwgewassen is van groot belang voor de bodemgezondheid en verspreiding van aardappelziekten. De relatief hoge kosten voor handmatige bestrijding maken inzet van high tech oplossingen interessant. In dit project is een Deep Learning herkenningsalgoritme voor aardappelplanten in een tweetal akkerbouwgewassen ontwikkeld. Tevens is het algoritme getest met een prototype van een spotsprayer, een apparaat dat kleine hoeveelheden middel precies op gedetecteerde planten kan spuiten. Dit rapport beschrijft de behaalde resultaten en de verbeterstappen die nodig zijn om de gewenste kwaliteit in de praktijk te realiseren

Application-specific evaluation of a weed-detection algorithm for plant-specific spraying

Robotic plant-specific spraying can reduce herbicide usage in agriculture while minimizing labor costs and maximizing yield. Weed detection is a crucial step in automated weeding. Currently, weed detection algorithms are always evaluated at the image level, using conventional image metrics. However, these metrics do not consider the full pipeline connecting image acquisition to the site-specific operation of the spraying nozzles, which is vital for an accurate evaluation of the system. Therefore, we propose a novel application-specific image-evaluation method, which analyses the weed detections on the plant level and in the light of the spraying decision made by the robot. In this paper, a spraying robot is evaluated on three levels: (1) On image-level, using conventional image metrics, (2) on application-level, using our novel application-specific image-evaluation method, and (3) on field level, in which the weed-detection algorithm is implemented on an autonomous spraying robot and tested in the field. On image level, our detection system achieved a recall of 57% and a precision of 84%, which is a lower performance than detection systems reported in literature. However, integrated on an autonomous volunteer-potato sprayer-system we outperformed the state-of-the-art, effectively controlling 96% of the weeds while terminating only 3% of the crops. Using the application-level evaluation, an accurate indication of the field performance of the weed-detection algorithm prior to the field test was given and the type of errors produced by the spraying system was correctly predicted.</p

Damage in concrete and geomaterials

Risk and clinical outcome of stroke, as for nearly all complex conditions, is polygenic. Discovering influential genetic variants offers the promise of new and personalized treatments that will substantially reduce the devastating effects of stroke on global health. Adequate power to detect multiple genetic risk alleles requires large sample sizes. Although stroke is the second leading cause of death worldwide and a major contributor to adult disability, no individual center can collect sufficient samples on its own. Recognizing this challenge, in 2007, stroke researchers from around the world formed the International Stroke Genetics Consortium (ISGC, http://www.strokegenetics.org). The ISGC mission is to identify genetic factors influencing stroke risk, prognosis, and treatment response by studying patients enrolled at centers around the globe. Although there has been notable early success, much work remains not only to achieve the ultimate goal of personalized medicine in stroke, finding genetic risk alleles, but also, more importantly, to develop comprehensive stroke risk assessments with actionable clinical results. Judging from developments in other complex diseases, such as diabetes mellitus and coronary artery disease, sample sizes of the order of 100 000 to 200 000 will be needed to identify the full range of genetic variation involved in stroke. Achieving such sample sizes requires even larger collaboration