Dr. Chiranjit Singha
International Center for Agricultural Research in the Dry Areas (ICARDA)
0000-0003-1204-1750
About
Dr. Chiranjit Singha, currently completed Ph.D. at the Visva Bharati University (Central University), Department of Agricultural Engineering, India. His main research interests are the application of Precision agriculture (PA) and the use of Geographic Information Systems (GIS), Remote Sensing (RS) enabled with Machine Learning and Deep learning for applications in the ecological environment, disaster management to enhance the understanding of the Earth observation system science, in the context of geo-environmental and hydrometeorological/Climate Change shifts, which significantly interests him.
Employment
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International Center for Agricultural Research in the Dry Areas (ICARDA) Data-Driven Climate Vulnerability Expert2025 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (50)
- Revolutionizing wheat crop disease prediction: A novel framework on integrating nature-inspired random forest optimization and explainable artificial intelligence (XAI) in Morocco Save
- Hybrid framework of physics-inspired optimization and explainable ensemble learning for irrigation classification mapping in Morocco Save
- Egypt’s Water Security Challenge: Climate Pressures, Hydropolitical Realities and Strategic Adaptation Save
- Review of aquifer storage and recovery opportunities and challenges in India Save
- Optimizing Crop Yield: Analyzing Soil Variability with PCA and Clustering Techniques Save
- Review of climate-resilient agriculture for ensuring food security: Sustainability opportunities and challenges of India Save
- Transforming soil quality index predictions in the Nile River Basin using hybrid stacking machine learning techniques Save
- Leveraging ML to predict climate change impact on rice crop disease in Eastern India Save
- Advancing flood risk assessment: Multitemporal SAR-based flood inventory generation using transfer learning and hybrid fuzzy-AHP-machine learning for flood susceptibility mapping in the Mahananda River Basin Save
- Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud Save