Plant Stress

Plant stress research focuses on understanding and mitigating the negative impacts of various environmental and biotic factors on crop yields. Current research employs machine learning, particularly deep learning models like convolutional neural networks and reinforcement learning, along with multimodal large language models, to analyze high-throughput phenotyping data from diverse sensors and improve stress detection and management strategies. These advancements aim to optimize crop production through precision agriculture techniques, such as targeted pesticide application and improved seed treatments using nanomaterials, ultimately enhancing food security and resource efficiency. Interpretable machine learning methods are increasingly used to understand the complex interactions between plant stress and mitigation strategies.