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| 1 | A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting显示文摘The yield of cereal crops such as sorghum(Sorghum bicolor L.Moench)depends on the distribution of crop-heads in varying branching arrangements.Therefore,counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field.However,measuring such phenotypic traitsmanually is an extremely labor-intensive process and suffers from low efficiency and human errors.Moreover,the process is almost infeasible for large-scale breeding plantations or experiments.Machine learning-based approaches like deep convolutional neural network(CNN)based object detectors are promising tools for efficient object detection and counting.However,a significant limitation of such deep learningbased approaches is that they typically require a massive amount of hand-labeled images for training,which is still a tedious process.Here,we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images.We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance(��2 between human count and machine count is 0.88)by using a semitrained CNN model(i.e.,trained with limited labeled data)to perform synthetic annotation.In addition,we also visualize key features that the network learns.This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes. | Sambuddha Ghosal Bangyou Zheng Scott CChapman Andries BPotgieter David RJordan Xuemin Wang Asheesh KSingh Arti Singh Masayuki Hirafuji Seishi Ninomiya Baskar Ganapathysubramanian Soumik Sarkar Wei Guo | 2019 | Plant Phenomics2019，1，1： | 16 |
| 2 | Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean显示文摘The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques,which is now a critical component of mainstream cultivar development pipelines.However,advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs.Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements.Random forest,a machine learning(ML)algorithm,was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation(CV)scenarios consistent with breeding challenges.To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives,feature importance in tandem with a genetic algorithm(GA)technique allowed selection of a subset of phenotypic traits,specifically optimal wavebands.The results illuminated the capability of fusingML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping(e.g.,seed yield harvest).While we illustrate with soybean,this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective。 | Kyle Parmley Koushik Nagasubramanian Soumik Sarkar Baskar Ganapathysubramanian Asheesh K.Singh | 2019 | Plant Phenomics2019，1，1： | 5 |
| 3 | Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters显示文摘We report a root system architecture(RSA)traits examination of a larger scale soybean accession set to study trait genetic diversity.Suffering from the limitation of scale,scope,and susceptibility to measurement variation,RSA traits are tedious to phenotype.Combining 35,448 SNPs with an imaging phenotyping platform,292 accessions(replications=14)were studied for RSA traits to decipher the genetic diversity.Based on literature search for root shape and morphology parameters,we used an ideotypebased approach to develop informative root(iRoot)categories using root traits.The RSA traits displayed genetic variability for root shape,length,number,mass,and angle.Soybean accessions clustered into eight genotype-and phenotype-based clusters and displayed similarity.Genotype-based clusters correlated with geographical origins.SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits,while diverse accession could infuse useful genetic variation for these traits.Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods,enabling trait cataloging for breeding and research applications.The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains. | Kevin G.Falk Talukder Zaki Jubery Jamie A.O’Rourke Arti Singh Soumik Sarkar Baskar Ganapathysubramanian Asheesh K.Singh | 2020 | Plant Phenomics2020，2，1： | 3 |
| 4 | Variation and dispersal of PM_(10) and PM_(2.5) during COVID-19 lockdown over Kolkata metropolitan city,India investigated through HYSPLIT model显示文摘The higher concentration of PM_(10) and PM_(2.5) in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications.The main objective is to find out the spatiotemporal variation and dispersal of PM_(10) and PM_(2.5) along with COVID-19 infection in the dusty city Kolkata.The consecutive two years PM_(10) and PM_(2.5) data of different stations have been obtained from State Pollution Control Board,Govt.of West Bengal.Forward trajectory analysis has been done through HYSPLIT(Hybrid Single Particle Lagrangian Integrated Trajectory)model to find the path and direction of air particles.The result showed that the various meteorological or environmental factors(such as temperature,relative humidity,wind,wind speed,pressure and gusty wind)and geographical location regulate the spatiotemporal variation of PM_(10) and PM_(2.5).These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city.During summer(both pre and lockdown years),the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m.The HYSPLIT model clearly specified that the particles dispersed from south,south-west to north and north east direction due to strong wind.The constant magnification of PM_(10) and PM_(2.5) in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths.In Kolkata,the one of the crucial reasons of high infection and deaths(COVID-19)is co-morbidity of people. | Biswajit Bera Sumana Bhattacharjee Nairita Sengupta Soumik Saha | 2022 | Geoscience Frontiers2022，13，1： | 2 |
| 5 | The in vitro effect of certain fungicides, insecticides, and biopesticides on awcelial growth in the biocontrol fungus Trichoderma harzianum显示文摘 | SOUMIK S PRADEEPA N A JAY D | 2010 | Turk J Biol2010，34，4： | 1 |
| 6 | Fauh diagnosis and isolation in aircraft gas turbine engines显示文摘 | SOUMIK SARKAR KUSHAL MUKHERJEE ASOK RAY | 2008 | Ameri- can Control Conference2008，11，6： | 1 |
| 7 | Interpretable deep learning for guided microstructure-property explorations in photovoltaics显示文摘The microstructure determines the photovoltaic performance of a thin film organic semiconductor film.The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate,thus making microstructure optimization challenging.Here,we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks.We characterize this approach in terms of two critical metrics,its generalizability(has it learnt a reasonable map?),and its intepretability(can it produce meaningful microstructure characteristics that influence its prediction?).A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration.We illustrate this by using the surrogate model for both manual exploration(that verifies known domain insight)as well as automated microstructure optimization.We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems. | Balaji Sesha Sarath Pokuri Sambuddha Ghosal Apurva Kokate Soumik Sarkar Baskar Ganapathysubramanian | 2019 | npj Computational Materials2019，，1： | 1 |
| 8 | Sulfasalazine induced lichen planus in a patient of rheumatoid arthritis显示文摘 | Sangita Ghosh Vijay Jain Soumik Chaudhuri Satyavir Mathur | 2013 | Indian Journal of Dermatology, Venereology, and Leprology2013，，4： | 1 |
| 9 | Intestinal Alkaline Phosphatase Prevents Antibiotic-Induced Susceptibility to Enteric Pathogens显示文摘 | Sayeda Nasrin Alam Halim Yammine Omeed Moaven Rizwan Ahmed Angela K. Moss Brishti Biswas Nur Muhammad Rakesh Biswas Atri Raychowdhury Kanakaraju Kaliannan Sathi Ghosh Madhury Ray Sulaiman R. Hamarneh Soumik Barua Nondita S. Malo Atul K. Bhan Madhu S. Malo | 2014 | Annals of Surgery2014，，4： | 1 |
| 10 | The in vitro effect of certain fungicides, insecticides, and biopesticides on mycelial growth in the biocontrol fungus Tfichoderma harzianum显示文摘 | Soumik S Pradeepa N Ajay D | 2010 | Turk J Biol2010，4，34： | 1 |
| 11 | Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline(SNAP)显示文摘Nodules form on plant roots through the symbiotic relationship between soybean(Glycine max L.Merr.)roots and bacteria(Bradyrhizobium japonicum)and are an important structure where atmospheric nitrogen(N2)is fixed into bioavailable ammonia(NH3)for plant growth and development.Nodule quantification on soybean roots is a laborious and tedious task;therefore,assessment is frequently done on a numerical scale that allows for rapid phenotyping,but is less informative and suffers from subjectivity.We report the Soybean Nodule Acquisition Pipeline(SNAP)for nodule quantification that combines RetinaNet and UNet deep learning architectures for object(i.e.,nodule)detection and segmentation.SNAP was built using data from 691 unique roots from diverse soybean genotypes,vegetative growth stages,and field locations and has a good model fit(R2=0:99).SNAP reduces the human labor and inconsistencies of counting nodules,while acquiring quantifiable traits related to nodule growth,location,and distribution on roots.The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors,and their interactions on nodulation from an early development stage.The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars,as well as enhanced insight into the plant-Bradyrhizobium relationship. | Talukder Zaki Jubery Clayton N.Carley Arti Singh Soumik Sarkar Baskar Ganapathysubramanian Asheesh K.Singh | 2021 | Plant Phenomics2021，3，1： | 1 |
| 12 | Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications显示文摘Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops.The objective of this study is to develop a machine learning(ML)approach adept at soybean(Glycine max L.(Merr.))pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot.To meet this goal,we developed a multiview image-based yield estimation framework utilizing deep learning architectures.Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions.We used data from controlled imaging environment in field,as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation.Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars. | Luis GRiera Matthew ECarroll Zhisheng Zhang Johnathon MShook Sambuddha Ghosal Tianshuang Gao Arti Singh Sourabh Bhattacharya Baskar Ganapathysubramanian Asheesh K.Singh Soumik Sarkar | 2021 | Plant Phenomics2021，3，1： | 1 |
| 13 | Arsenic trioxide and lead acetate induce apoptosis in adult rat hepatic stem cells 显示文摘 | Soumik A Sonali R Atish R | 2009 | Cell Biol Toxicol2009，25，： | 1 |
| 14 | 3D convolutional selective autoencoder for instability detection in combustion systems显示文摘While analytical solutions of critical(phase)transitions in dynamical systems are abundant for simple nonlinear systems,such analysis remains intractable for real-life dynamical systems.A key example is thermoacoustic insta-bility in combustion,where prediction or early detection of the onset of instability is a hard technical challenge,which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries.The instabilities arising in combustion chambers of engines are mathematically too complex to model.To address this issue in a data-driven manner instead,we propose a novel deep learning architecture called 3D convolutional selective autoencoder(3D-CSAE)to detect the evolution of self-excited oscillations using spatiotemporal data,i.e.,hi-speed videos taken from a swirl-stabilized combustor(laboratory surrogate of gas turbine engine combustor).3D-CSAE consists of filters to learn,in a hierarchical fashion,the complex visual and dynamic features related to combustion instability from the training videos(i.e.,two spatial dimensions for the image frames and the third dimension for time).We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions.We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video.The proposed model clearly shows performance improvement in detecting the precursors and the onset of instability.The machine learning-driven results are verified with physics-based off-line measures.Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions. | Tryambak Gangopadhyay Vikram Ramanan Adedotun Akintayo Paige K Boor Soumalya Sarkar Satyanarayanan R Chakravarthy Soumik Sarkar | 2021 | Energy and AI2021，4，2： | 1 |
| 15 | Pnpla3I148M knockin mice accumulate PNPLA3 on lipid droplets and develop hepatic steatosis显示文摘 | Eriks Smagris Soumik BasuRay John Li Yongcheng Huang Ka‐man V. Lai Jesper Gromada Jonathan C. Cohen Helen H. Hobbs | 2015 | Hepatology2015，，： | 1 |
| 16 | Pnpla31148M knockin miceaccumulate PNPLA3 on lipid droplets and develop hepatic steatosis显示文摘 | ERIKS S SOUMIK B JOHN L | 2015 | Hepatology2015，61，1： | 1 |
| 17 | Interfacial composition and characterization of a quaternary water-in-oil mixed surfactant(cationic of different alkyl chain lengths+polyoxyethylene type nonionic)microemulsions in absence and presence of inorganic salts显示文摘 | Soumik B Kaushik K Bidyut K | 2013 | Colloids and Surfaces A:Physicochemical and Engineering Aspects2013，433，： | 1 |
| 18 | Physicochemical studies of mixed surfactant microemulsions with isopropyl myristate as oil显示文摘 | Soumik B Kaushik K Swapan K | 2013 | Journal of Colloid and Interface Science2013，402，： | 1 |
| 19 | Formation,thermodynamic properties,microstructures and antimicrobial activity of mixed cationic/non-ionic surfactant microemulsions with isopropyl myristate as oil显示文摘 | Soumik B Kaushik K Sajal D | 2014 | Journal of Colloid and Interface Science2014，430，： | 1 |
| 20 | Generalization of Hilbert transform for symbolic analysis of noisy signals显示文摘 | Soumik S Kushal M Asok R | 2009 | Signal Processing2009，89，6： | 1 |
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