272 research outputs found
Design of fuzzy PI controller for CSI fed induction motor drive / Piush Kumar, Vineeta Agarwal and Asheesh K. Singh
No full textIn this paper the closed loop control of CSI fed induction motor is investigated using fuzzy logic controller. A slip control scheme has been used for the induction motor. The evaluation of the fuzzy logic controller behavior is made through computer simulation with MATLAB coding. The starting transient of the motor is investigated for different operating speeds with different load torque. It has been found that for fuzzy controller, once the parameters are selected for a specified load and speed it will work for all other load and speed whereas for conventional PI controller, the parameters have to be changed for each load and speed setting. The fuzzy controller reduces the over shoot value of speed by approximately 2 to 5 % while for dc link current it reduces around 4% as compared to conventional PI controller. This reduces the rating of the devices used in the drive system. It has also been found that the system shows some what slow response with less oscillation so that fuzzy controller can be used where smooth operation is required
Genetic Control and Geo-Climate Adaptation of Pod Dehiscence Provide Novel Insights into Soybean Domestication
No full textLoss of pod dehiscence was a key step in soybean [Glycine max (L.) Merr.] domestication. Genome-wide association analysis for soybean shattering identified loci harboring Pdh1, NST1A and SHAT1-5. Pairwise epistatic interactions were observed, and the dehiscent Pdh1 overcomes resistance conferred by NST1A or SHAT1-5 locus. Further candidate gene association analysis identified a nonsense mutation in NST1A associated with pod dehiscence. Geographic analysis showed that in Northeast China (NEC), indehiscence at both Pdh1 and NST1A were required in cultivated soybean, while indehiscent Pdh1 alone is capable of preventing shattering in Huang-Huai-Hai (HHH) valleys. Indehiscent Pdh1 allele was only identified in wild soybean (Glycine soja L.) accession from HHH valleys suggesting that it may have originated in this region. No specific indehiscence was required in Southern China. Geo-climatic investigation revealed strong correlation between relative humidity and frequency of indehiscent Pdh1 across China. This study demonstrates that epistatic interaction between Pdh1 and NST1A fulfills a pivotal role in determining the level of resistance against pod dehiscence, and humidity shapes the distribution of indehiscent alleles. Our results give further evidence to the hypothesis that HHH valleys was at least one of the origin centers of cultivated soybean.This article is published as Jiaoping Zhang, Asheesh K Singh, Genetic Control and Geo-Climate Adaptation of Pod Dehiscence Provide Novel Insights into Soybean Domestication, G3 Genes|Genomes|Genetics, Volume 10, Issue 2, 1 February 2020, Pages 545–554, https://doi.org/10.1534/g3.119.400876
Meta-GWAS for quantitative trait loci identification in soybean
No full textWe report a meta-Genome Wide Association Study involving 73 published studies in soybean [Glycine max L. (Merr.)] covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 peaks at 393 unique loci. Using stringent criteria to detect significant marker-trait associations, 59 candidate genes were identified, including 17 agronomic traits loci, 19 for seed-related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.This article is published as Johnathon M Shook, Jiaoping Zhang, Sarah E Jones, Arti Singh, Brian W Diers, Asheesh K Singh, Meta-GWAS for quantitative trait loci identification in soybean, G3 Genes|Genomes|Genetics, Volume 11, Issue 7, July 2021, jkab117, https://doi.org/10.1093/g3journal/jkab117
Usefulness of interpretability methods to explain deep learning based plant stress phenotyping
No full textDeep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training models that are interpretable -i.e. that justify their classification decisions by visually highlighting image features that were crucial for classification decisions. The expectation is that trained network models utilize image features that mimic visual cues used by plant pathologists. In this work, we compare some of the most popular interpretability methods: Saliency Maps, SmoothGrad, Guided Backpropogation, Deep Taylor Decomposition, Integrated Gradients, Layer-wise Relevance Propagation and Gradient times Input, for interpreting the deep learning model. We train a DenseNet-121 network for the classification of eight different soybean stresses (biotic and abiotic). Using a dataset consisting of 16,573 RGB images of healthy and stressed soybean leaflets captured under controlled conditions, we obtained an overall classification accuracy of 95.05 \%. For a diverse subset of the test data, we compared the important features with those identified by a human expert. We observed that most interpretability methods identify the infected regions of the leaf as important features for some -- but not all -- of the correctly classified images. For some images, the output of the interpretability methods indicated that spurious feature correlations may have been used to correctly classify them. Although the output explanation maps of these interpretability methods may be different from each other for a given image, we advocate the use of these interpretability methods as `hypothesis generation' mechanisms that can drive scientific insight.This is a pre-print of the article Nagasubramanian, Koushik, Asheesh K. Singh, Arti Singh, Soumik Sarkar, and Baskar Ganapathysubramanian. "Usefulness of interpretability methods to explain deep learning based plant stress phenotyping." arXiv preprint arXiv:2007.05729 (2020). Posted with permission.</p
Advancing Our Understanding of Charcoal Rot in Soybeans
No full textCharcoal rot [Macrophomina phaseolina (Tassi) Goid] of soybean [Glycine max (L.) Merr.] is an important but commonly misidentified disease, and very few summary articles exist on this pathosystem. Research conducted over the past 10 yr has improved our understanding of the environment conducive to disease development, host resistance, and improved disease diagnosis and management. This article summarizes the currently available research with an emphasis on disease management.This article is published as Romero Luna, Martha P., Daren Mueller, Alemu Mengistu, Asheesh K. Singh, Glen L. Hartman, and Kiersten A. Wise. "Advancing Our Understanding of Charcoal Rot in Soybeans." Journal of Integrated Pest Management 8, no. 1 (2017): 8. doi: 10.1093/jipm/pmw020. Posted with permission.</p
A deep learning framework to discern and count microscopic nematode eggs
No full textIn order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybean yield loss is negatively correlated with the density of SCN eggs that are present in the soil. While there has been progress in automating extraction of egg-filled cysts and eggs from soil samples counting SCN eggs obtained from soil samples using computer vision techniques has proven to be an extremely difficult challenge. Here we show that a deep learning architecture developed for rare object identification in clutter-filled images can identify and count the SCN eggs. The architecture is trained with expert-labeled data to effectively build a machine learning model for quantifying SCN eggs via microscopic image analysis. We show dramatic improvements in the quantification time of eggs while maintaining human-level accuracy and avoiding inter-rater and intra-rater variabilities. The nematode eggs are correctly identified even in complex, debris-filled images that are often difficult for experts to identify quickly. Our results illustrate the remarkable promise of applying deep learning approaches to phenotyping for pest assessment and management.This article is published as Akintayo, Adedotun, Gregory L. Tylka, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, and Soumik Sarkar. "A deep learning framework to discern and count microscopic nematode eggs." Scientific Reports 8 (2018): 9145. doi: 10.1038/s41598-018-27272-w.</p
Machine Learning Approach for Prescriptive Plant Breeding
No full textWe explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.This article is published as Parmley, Kyle A., Race H. Higgins, Baskar Ganapathysubramanian, Soumik Sarkar, and Asheesh K. Singh. "Machine Learning Approach for Prescriptive Plant Breeding." Scientific Reports 9 (2019): 17132. DOI: 10.1038/s41598-019-53451-4. Posted with permission.</p
UAS-Based Plant Phenotyping for Research and Breeding Applications
No full textUnmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.This article is published as Wei Guo, Matthew E. Carroll, Arti Singh, Tyson L. Swetnam, Nirav Merchant, Soumik Sarkar, Asheesh K. Singh, Baskar Ganapathysubramanian. UAS-Based Plant Phenotyping for Research and Breeding Applications. Plant Phenomics. 2021;2021:DOI:10.34133/2021/9840192
AAC Congress Durum Wheat
No full textCongress durum wheat (Triticum turgidum L. subsp. durum (Desf.) Husn.) is adapted to the durum production area of the Canadian prairies. Averaged over three years, AAC Congress yielded significantly more grain than Strongfield and AC Navigator. AAC Congress had protein concentration significantly lower than Strongfield but significantly higher than Brigade. AAC Congress is eligible for grades of Canada Western Amber Durum. It has lower grain cadmium concentration and higher yellow pigment concentration than the check cultivars, except AAC Cabri.This is a manuscript of an article published as Ruan, Yuefeng, Asheesh K. Singh, R. M. DePauw, Ron Knox, Tom N. McCaig, Richard Douglas Cuthbert, Brent McCallum, Thomas Fetch, and Brian L. Beres. "AAC Congress Durum Wheat." Canadian Journal of Plant Science. doi: 10.1139/CJPS-2017-0149. Posted with permission.</p
Plant disease identification using explainable 3D deep learning on hyperspectral images
No full textBackground
Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide.
Results
Based on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D DCNN has a classification accuracy of 95.73% and an infected class F1 score of 0.87. Using the concept of a saliency map, we visualize the most sensitive pixel locations, and show that the spatial regions with visible disease symptoms are overwhelmingly chosen by the model for classification. We also find that the most sensitive wavelengths used by the model for classification are in the near infrared region (NIR), which is also the commonly used spectral range for determining the vegetative health of a plant.
Conclusion
The use of an explainable deep learning model not only provides high accuracy, but also provides physiological insight into model predictions, thus generating confidence in model predictions. These explained predictions lend themselves for eventual use in precision agriculture and research application using automated phenotyping platforms.This article is published as Nagasubramanian, Koushik, Sarah Jones, Asheesh K. Singh, Soumik Sarkar, Arti Singh, and Baskar Ganapathysubramanian. "Plant disease identification using explainable 3D deep learning on hyperspectral images." Plant Methods 15, no. 1 (2019): 98. DOI: 10.1186/s13007-019-0479-8. Posted with permission.</p
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