معرفی
Mason Earles is an Assistant Professor in the Department of Biological and Agricultural Engineering at the University of California, Davis, with a joint affiliation in Viticulture & Enology. Prior to academia, he worked as a Data Science Engineer at Apple, focusing on deep learning and computer vision. His research integrates computational techniques with physiological and ecological instrumentation to study plant biophysics, including CO2 diffusion, water transport, and carbohydrate metabolism. He leads the Plant AI and Biophysics Lab, which develops low-cost AI systems for precision agriculture, emphasizing interpretability of biological mechanisms alongside predictive capabilities.
The lab’s core mission involves creating agricultural sensing and automation systems powered by deep learning to monitor plant biophysical states (e.g., stress, productivity) and improve sustainability. Current projects include AgRowStitch (high-fidelity image stitching), California Crop Yield Benchmark (multi-data fusion), and iNatAg (multi-species classification). His work bridges molecular to organismal scales, aiming to translate basic plant science into actionable agricultural strategies.
Key research themes include evapotranspiration modeling, crop yield forecasting, and AI-driven microscopy for pathogen detection. He collaborates closely with industry and policymakers to ensure real-world impact, exemplified by initiatives like the NSF-funded AIFS Institute for next-generation food systems.
