Dr. Arti Singh

PI - Arti Singh

Mungbean (Vigna radiata) L. Wilczek and Urdbean (Vigna mungo) are important pulse crops worldwide. They are commonly referred to as green gram and black gram. They were produced in America in the 1830’s as Chickasaw pea. Most of the mungbean production in the US is currently in Oklahoma; however, small portion is also grown in Kansas, California, Tennessee, Kentucky, and Texas.

Mungbean and urdbean are short-season (60-90 days) legume crops with good drought tolerance. These crops are used as a food crop particularly for a plant-based protein diet; and from an agronomic perspective, it is useful as a cover crop, a rotational crop, and a double crop. The attractive features of this crop as a food crop are its neutral taste, versatility in applications, high nutrition value, and easy digestibility. Additionally, there is a rapidly growing market demand.

This project was initiated in 2017, and since inception has involved numerous farmers in multiple states and processing companies nationally to set up breeding and research objectives. Approximately 3,000 mungbean and 300 urdbean accessions were obtained from the United States Department of Agriculture (USDA) germplasm collection bank, Griffin, GA, USA. These lines underwent extensive field evaluations in Iowa; and 482 mungbean and 30 urdbean lines with production potential were selected for further research and development. Based on these results and yield testing, new varieties will be recommended to farmers. The 482 mungbean lines were sent for genome sequencing to correlate regions in the genome with specific traits, to identify genes responsible for the traits of interest and SNP markers linked to the trait of interest, so targeted breeding can be done. Examples of traits that were phenotyped included days to flowering, plant height, days to maturity, seed weight, plant growth habit, seed quality, diseases, and pest resistance. Our first paper and its results can be seen here: https://acsess.onlinelibrary.wiley.com/doi/full/10.1002/csc2.20322. Currently, several breeding populations are undergoing advancement and selection to develop new varieties for the US farmers.

Our work suggests that mungbean farming will easily fit in the current mid-west agricultural production system, including no major equipment changes by soybean farmers. More research is needed to understand agronomic practices including standardization of seed rate, planting dates, and crop rotation; food quality trait research on seed protein and other functional characteristics; identification and mitigation strategies of diseases and insect-pests; and genomics and phenomics tools for breeding strategies.  

PI - Soumik Sarkar; Co-PI - Arti Singh, Baskar Ganapathysubramanian, Asheesh Singh and Daren Mueller

The availability of cheap, deployable, and connected sensor technology has produced a data deluge at varying spatial, temporal, and fidelity scales. Currently, algorithms are not well-suited to assimilate multi-scale heterogeneous data, such as continuous temporal data with discrete event driven data, or image data with time-series data; therefore, a major challenge is the lack of standardization which would facilitate the interoperability of different platforms. The objective of this proposal is to develop and deploy an agricultural analytics framework that assimilates multi-scale heterogeneous data and provides actionable information for the control of disease spread in field crops. The proposed framework will: (a) effectively extract actionable information from multi-length/time/frequency scale data at the plant, plot and field levels; and (b) build an efficient decision support system for early detection and mitigation of row crop diseases. We will accomplish the objectives through: (a) using machine learning concepts to extract disease signatures from heterogeneous data within a layered sensing architecture; (b) developing graphical, and probabilistic models for multi-scale data assimilation and prediction of localized disease onset and spread; (c) integration of mathematical, and cyberinfrastructure tools with physical multi-scale sensing platforms for hierarchical active information gathering and subsequent decision-support for mitigation strategies; and (d) deployment of the framework on research farms as well as dissemination of results to the broader farming and research community. The outcomes of this research will benefit a broad spectrum of the agricultural community, from small scale farmers to large scale farming operations and plant scientists. The validated framework will facilitate informed decision-making by the farming community and to accelerate plant breeding to create robust crops under changing climatic conditions. The highly interdisciplinary research activities will help train the next generation of scientists and engineers to tightly integrate cyberinfrastructure advances with agricultural systems.

PI - Soumik Sarkar; Co-PI - Arti Singh, Baskar Ganapathysubramanian, Asheesh Singh

The proposers in collaboration with Prof. Masayuki Hirafuji & team at the University of Tokyo, are organizing the First International workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2018) on October 24, 2018 as a part of the Asia-Pacific Federation for Information Technology in Agriculture and the World Congress on Computers in Agriculture (AFITA/WCCA) being held from October 24, 2018 to October 26, 2018 that will be collocated at the Victor Menezes Convention Centre at the Indian Institute of Technology, Bombay (IITB) in Mumbai, India

Co-PI - Asheesh Singh, Baskar Ganapathysubramanian, Carolyn Lawrence, Senior Personnel - Arti Singh and others

Currently, the highest priorities for agriculture production are to sustainably meet the food/feed/fuel/fiber requirements of a growing population worldwide. Changes in consumption patterns coupled with the rise in population growth and disposable income levels in emerging economies require the development of sustainable agricultural production practices. Towards this goal, plant scientists, engineers, and statisticians are increasingly working to improve efficiency and reduce costs of production. Our team recognizes a transcending opportunity through the use of high throughput phenotyping, image analysis and data analytics to address the issue of increased crop production and sustainability and deliver tools to farmers and plant researchers. Our vision is to tightly integrate this data analytics pipeline with best practices in management and policy that enhance the profitability and sustainability of farms under changing climatic conditions.