Artificial Intelligence and Machine Learning for Agriculture Analytics
AI/ML plays a crucial role in agriculture analytics, especially in geospatial crop monitoring and mapping. In your research-based talk, machine learning models will be covered for classifying crops using multi-sensor temporal datasets such as optical and SAR data. Talk will cover various types of issues while mapping specific crops as well as monitoring at sowing/harvesting and crop-burning fields. Issues can be within crop heterogeneity, spectral overlap, mixed pixel problem, minimum training data use, etc. AI techniques improve the detection of crop phenology and growth stages using time-series satellite imagery. Transfer learning can be applied to improve crop mapping accuracy with limited ground truth data. AI supports the detection and monitoring of stubble burning using satellite-based indicators. Integration of geospatial datasets with ML enables efficient acreage estimation and crop type discrimination. Advanced analytics help in understanding spatial variability for better resource and water management. Overall, AI/ML strengthens precision agriculture, food security assessment, and policy-level decision support.
Dr. Anil Kumar
Group Head, Geo-spatial Technology & Program Outreach at The Indian Space Research Organisation (ISRO)
Anil Kumar is a scientist/engineer “G” and the Group Head Geo-spatial Technology and Program Outreach of Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun, India. He received his BTech degree in civil engineering from IET affiliated to the University of Lucknow, India, and his M.E. degree as well as his PhD in soft computing from the Indian Institute of Technology, Roorkee, India. So far, he has guided 13 PhD theses, and six more are in progress. He has also guided several dissertations of MTech, MSc, BTech, and postgraduate diploma courses. He always loves to work with PhD scholars, Master’s, and Graduate students for their research work, and motivates them to adopt a research-oriented professional career. He received the Pisharoth Rama Pisharoty award for contributing state of the art fuzzy based algorithms for earth observation data. His current research interests are in the area of soft computing-based machine learning, deep learning for single date and temporal multi-sensor remote sensing data for specific class identification and mapping through in-house development of the SMIC tool. He also works in the area of digital photogrammetry, GPS/GNSS, and LiDAR. He has written two books on ‘Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification’ & Multi-Sensor and Multi-Temporal Remote Sensing-Specific Single Class Mapping with CRC Press. Won a challenge like Kritagya DA & FW Hackathon conducted by the Ministry of Agriculture, New Delhi, India, and Finalist of ‘ISRO immersion Challenge AI for Space and Geospatial conducted by NRSC-ISRO, Hyderabad, and IIIT Hyderabad.
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