Drought Stress

Drought stress research focuses on developing accurate and timely methods for detecting and assessing drought impacts on various systems, from agricultural crops to water resources. Current research employs advanced machine learning techniques, including deep learning architectures like Vision Transformers and Convolutional Neural Networks, often combined with support vector machines, to analyze diverse data sources such as satellite imagery, sensor data, and even social media posts. These efforts aim to improve drought prediction, enable proactive mitigation strategies, and enhance the resilience of agriculture and water management in the face of climate change. The resulting improved monitoring and prediction capabilities have significant implications for food security and sustainable resource management.