The Crop Physiology and Modelling Cluster employs a wide range of advanced tools and technologies to enhance our understanding of crop physiology, model crop responses, and drive sustainable agricultural practices. Through the utilization of these tools, we aim to optimize crop performance, improve resource use efficiency, and enhance resilience to abiotic stresses.
High-Throughput Phenotyping Platforms (HTP): We have developed and implemented state-of-the-art HTP platforms to efficiently assess crop performance and responses to abiotic stresses. These platforms include LeasyScan, a "camera to plant" technology that enables rapid characterization of adaptation traits within a short timeframe. Additionally, our Lysimeter facility with a rainout shelter allows us to impose various stress conditions and evaluate plant responses accurately.
Computational Modelling and Simulation: We utilize advanced computational modelling and simulation tools to predict and understand crop growth, development, and responses under different environmental scenarios. By integrating climate data, soil characteristics, and crop physiology, we can simulate and forecast crop performance, enabling informed decision-making for farmers and stakeholders.
Sensor-Based Technologies: We employ cutting-edge sensor-based technologies to assess critical crop parameters and nutritional traits. These technologies include near-infrared spectroscopy (NIRS) for quick and non-destructive assessment of macronutrients, X-ray fluorescence (XRF) for micronutrient analysis, and computer tomography for post-harvest trait evaluation. By using these sensors, we can rapidly assess crop health, nutrient status, and post-harvest quality.
UAV-Based Field Phenotyping: We are exploring the use of Unmanned Aerial Vehicles (UAVs) for field phenotyping, enabling rapid data collection over large areas. UAVs equipped with advanced sensors and imaging technologies allow us to capture detailed information on crop growth, stress responses, and spatial variability. This approach enhances data collection efficiency and provides valuable insights for crop monitoring and management.
Data Analytics and Artificial Intelligence (AI): We leverage data analytics and AI techniques to process and analyze large datasets generated from phenotyping, genotyping, and environmental monitoring. These tools enable us to extract valuable patterns, trends, and correlations, enhancing our understanding of crop physiology and supporting decision-making processes.
Through the utilization of these tools and technologies, the Crop Physiology and Modelling Department at ICRISAT is at the forefront of research and innovation in crop physiology. By combining scientific expertise, advanced phenotyping platforms, computational modelling, and data analytics, we aim to develop sustainable agricultural practices, improve crop performance, and contribute to the resilience and productivity of farming systems in dryland regions.
