US scientists use machine learning for real-time crop disease alerts

Purdue University researchers are leveraging advanced AI and machine learning technologies to transform agriculture and environmental management. Their innovations include real-time crop disease detection using semi-supervised models that identify rare diseases from limited data, enabling faster outbreak responses and reduced chemical usage. These AI tools are designed to run efficiently on low-power devices such as drones and autonomous tractors, facilitating on-the-ground, real-time monitoring without relying on constant connectivity. Additionally, Purdue scientists are using AI to analyze urban ecosystems through remote sensing data and LiDAR imagery, uncovering patterns invisible to the naked eye to improve urban living conditions. In agriculture, AI is also being applied to enhance crop yield predictions and climate resilience. For example, machine learning ensembles simulate rice yields under future climate scenarios, improving accuracy significantly. Tools like the “Netflix for crops” platform recommend optimal crops based on soil and water data, aiding farmers and policymakers in making informed, data-driven decisions. Furthermore, Purdue developed an AI-powered medical robot capable of swimming inside a cow’s stomach to