Saliency-driven robotic networks for Spatiotemporal plant Phenotyping

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

The objective of this project is to build a network of ground robots that can collect multi-modal data in research farms for high throughput modular plant phenotyping. The robotic network will have the following capabilities: (a) Navigate in a farm to collect data with minimal human intervention during operation, (b) Autonomous decision making i.e, it can take its own decisions for maximizing the value of information of the acquired data, (c) Scalable in terms of the size of the farmland, and (d) Work in collaboration with humans to improve their situational awareness in multi-dimensional genome wide studies. Our approach will leverage opportunistic sensing, task partitioning and scout-task allocation, machine learning, and spatio-temporal importance map building, to enable resolution of the above science questions that cannot be addressed without the use of robotic systems. The outcomes of this research would benefit a broad spectrum of the agricultural community, from plant scientists to small scale farmers in developing countries to domestic large scale farming operations. This approach allows biologists and geneticists to identify useful loci across development stages and environments, and speed the discovery process while providing valuable insights into the gene functions. Our broader outcomes extend to training the next generation of roboticists that understand and contribute to the societally critical problem of agriculture improvement.