Leadership

Mason Earles

Assistant Professor of Viticulture & Enology and Biological & Agricultural Engineering, UC Davis

Co-PI and Lead of Agricultural Production Cluster

Focus · Plant AI and Biophysics

jmearles@ucdavis.edu LinkedIn Website

AIFS projects

AIFS publications

About Mason

Mason is an Assistant Professor at UC Davis. He is also Co-PI and Lead of Agricultural Production Cluster for AIFS. During his doctoral and post-doctoral research, he developed computational techniques for 3D image processing and biophysical modeling in plants. His work integrated these techniques with custom instrumentation, shedding light on CO2 diffusion, photosynthesis, H2O transport, and carbohydrate metabolism across plant species.

At UC Davis, he leads the Plant AI and Biophysics Lab, aiming to create affordable AI systems that offer new insights into plant biology, enabling more precise and sustainable agricultural practices. His lab focuses on developing deep learning-powered agricultural sensing and automation systems to monitor plant biophysical status accurately, focusing on stress, productivity, and yield prediction. Further, his lab investigates plant biophysical mechanisms at various levels, seeking to understand their scalability and impact on agricultural systems while emphasizing interpretability over black-box predictions.

14 projects

Project involvement

Year 4

AgML: Open-Source Infrastructure to Accelerate Scaling of Agricultural AI Technologies

#Core AI Technologies#Agricultural Production

Year 4

Developing an AI-Enabled Toolkit for Routine Integration of Quality Traits into Molecular Breeding Strategies

#Molecular Breeding

Year 3

Developing an AI-Enabled Toolkit for Routine Integration of Quality Traits into Molecular Breeding Strategies

#Molecular Breeding

22 publications

AIFS publications

Preprint · BioRxiv · 2026
Genetic Mapping and Genomic Prediction for Agronomic, Grain Compositional, and Sensing-enabled Traits in a Cowpea MAGIC Population Along an Environmental Gradient
Berlingeri, Jonathan, Sassoum Lo, Margaret Riggs, Heesup Yun, Hamid Kamangir, Earl Ranario, Isaac K Uyehara, Ismael Mayanja, Astrid Lao, Isaac Dramadri, Patrick Ongom, Ousmane Boukar, Antonia Palkovic, Brian N Bailey, Mason Earles, Bao-Lam Huynh, and Christine H Diepenbrock
Paper page for Genetic mapping and genomic prediction for agronomic, grain compositional, and sensing-enabled traits in a cowpea MAGIC population along an environmental gradient DOI 10.64898/2026.08.04.742818
Preprint · arXiv · 2026
Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments?
Kamangir, Hamid, Jonathan Berlingeri, Earl Ranario, Isaac K Uyehara, Lars Lundqvist, Heesup Yun, Christine H Diepenbrock, Brian N Bailey, and Mason Earles
Paper page for Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments?PDF of Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments?DOI 10.48550/arXiv.2607.28796
Conference Article · Conference on Computer Vision and Pattern Recognition · 2026
Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection
Lundqvist, Lars , Earl Ranario, Hamid Kamangir, Heesup Yun, Christine H Diepenbrock, Brian N Bailey, and Mason Earles
Paper page for Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection PDF of Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection DOI 10.48550/arXiv.2604.09920
Journal Article · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2026
CMAViT: Integrating Climate, Management, and Remote Sensing Data for Crop Yield Prediction With Multimodel Vision Transformers
Kamangir, Hamid, Brent S Sams, Nick Dokoozlian, Luis Sanchez, and Mason Earles
Paper page for CMAViT: Integrating Climate, Management, and Remote Sensing Data for Crop Yield Prediction With Multimodel Vision Transformers PDF of CMAViT: Integrating Climate, Management, and Remote Sensing Data for Crop Yield Prediction With Multimodel Vision Transformers DOI 10.1109/jstars.2026.3669511
Preprint · arXiv · 2025
Are Vision-language Models Ready to Zero-shot Replace Supervised Classification Models in Agriculture?
Ranario, Earl and Mason Earles
Paper page for Are vision-language models ready to zero-shot replace supervised classification models in agriculture?PDF of Are vision-language models ready to zero-shot replace supervised classification models in agriculture?DOI 10.48550/arXiv.2512.15977
Journal Article · Frontiers in Artificial Intelligence · 2025
Enhancing AI Microscopy for Foodborne Bacterial Classification Using Adversarial Domain Adaptation to Address Optical and Biological Variability
Bhattacharya, Siddhartha, Aarham Wasit, Mason Earles, Nitin Nitin, and Jiyoon Yi
Paper page for Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability PDF of Enhancing AI microscopy for foodborne bacterial classification using adversarial domain adaptation to address optical and biological variability DOI 10.3389/frai.2025.1632344
Journal Article · Theoretical and Applied Genetics · 2025
Integration of Crop Modeling and Sensing Into Molecular Breeding for Nutritional Quality and Stress Tolerance
Berlingeri, Jonathan, Abelina Fuentes, Earl Ranario, Heesup Yun, Ellen Rim, Oscar Garrett, Alexander Howard, Mary-Francis LaPorte, Sassoum Lo, Duke Pauli, Jenna Hershberger, Mason Earles, Allen Van Deynze, Charlie Brummer, Richard Michelmore, Christopher Wong, Troy Magney, Pamela Ronald, Daniel Runcie, Brian N Bailey, and Christine H Diepenbrock
Paper page for Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance DOI 10.1007/s00122-025-04984-y
Conference Article · Computer Vision and Pattern Recognition · 2025
AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification
Ranario, Earl, Lars Lundqvist, Heesup Yun, Brian N Bailey, and Mason Earles
Paper page for AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification DOI 10.1109/CVPRW67362.2025.00536
Journal Article · International Journal of Applied Earth Observation and Geoinformation · 2025
Predicting Crop Yield Lows Through the Highs Via Binned Deep Imbalanced Regression: A Case Study on Vineyards
Kamangir, Hamid, Brent S Sams, Nick Dokoozlian, Luis Sanchez, and Mason Earles
Paper page for Predicting crop yield lows through the highs via binned deep imbalanced regression: A case study on vineyards PDF of Predicting crop yield lows through the highs via binned deep imbalanced regression: A case study on vineyards DOI 10.1016/j.jag.2025.104536
Journal Article · Computer Vision and Pattern Recognition · 2025
INatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species
Jain, Naitik, Amogh Joshi, and Mason Earles
Paper page for iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species DOI 10.48550/arXiv.2503.20068
Journal Article · Food Research International · 2025
Deep Learning Enabled Rapid Classification of Yeast Species in Food by Imaging of Yeast Microcolonies
Park, Hyeon W, Mason Earles, and Nitin Nitin
Paper page for Deep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies Project for Deep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies DOI 10.1016/j.foodres.2024.115604
Conference Article · IEEE/RSJ International Conference on Intelligent Robots and Systems · 2024
DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots
Choi, Taeyeong, Dario Guevara, Zifei Cheng, Grisha Bandodkar, Chonghan Wang, Brian N Bailey, Mason Earles, and Xin Liu
PDF of DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots Project for DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots
Journal Article · AI Magazine · 2024
The AIFS Institute: Building a Better Food System Through AI
Tagkopoulos, Ilias, Mason Earles, Danielle G Lemay, Xin Liu, Nitin Nitin, Aaron Smith, Tarek I Zohdi, and Steve Brown
Paper page for The AIFS Institute: Building a better food system through AI PDF of The AIFS Institute: Building a better food system through AI DOI 10.1002/aaai.12164
Journal Article · Computers and Electronics in Agriculture · 2024
Large-scale Spatio-temporal Yield Estimation Via Deep Learning Using Satellite and Management Data Fusion in Vineyards
Kamangir, Hamid, Brent S Sams, Nick Dokoozlian, Luis Sanchez, and Mason Earles
Paper page for Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards PDF of Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards DOI 10.1016/j.compag.2023.108439
Journal Article · International Symposium on Visual Computing · 2023
An Open-Source Simulation Toolbox for Annotation of Images and Point Clouds in Agricultural Scenarios
Guevara, Dario, Amogh Joshi, Pranav Raja, Elisabeth Forrestel, Brian N Bailey, and Mason Earles
Paper page for An Open-Source Simulation Toolbox for Annotation of Images and Point Clouds in Agricultural Scenarios Project for An Open-Source Simulation Toolbox for Annotation of Images and Point Clouds in Agricultural Scenarios DOI 10.1007/978-3-031-47969-4_43
Journal Article · Plant Phenomics · 2023
Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models
Joshi, Amogh, Dario Guevara, and Mason Earles
Paper page for Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models GitHub repository for Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models DOI 10.34133/plantphenomics.0084
Journal Article · Water Research · 2023
AI-enabled Biosensing for Rapid Pathogen Detection: From Liquid Food to Agricultural Water
Yi, Jiyoon, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, and Mason Earles
Paper page for AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water PDF of AI-enabled biosensing for rapid pathogen detection: From liquid food to agricultural water DOI 10.1016/j.watres.2023.120258
Journal Article · Applied and Environmental Microbiology · 2022
Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging
Ma, Luyao, Jiyoon Yi, Nicharee Wisuthiphaet, Mason Earles, and Nitin Nitin
Paper page for Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging Project for Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging DOI 10.1128/aem.01828-22
Journal Article · Frontiers in Plant Science · 2022
Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution Via Proximal RGB-D Imagery and End-to-End Deep Learning
Buxbaum, Nicolas, Johann H Lieth, and Mason Earles
Paper page for Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution via Proximal RGB-D Imagery and End-to-End Deep Learning Project for Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution via Proximal RGB-D Imagery and End-to-End Deep Learning DOI 10.3389/fpls.2022.758818
Journal Article · Computers and Electronics in Agriculture · 2022
Special Report: AI Institute for Next Generation Food Systems (AIFS)
Tagkopoulos, Ilias, Steve Brown, Xin Liu, Qing Zhao, Tarek I Zohdi, Mason Earles, Nitin Nitin, Daniel Runcie, Danielle G Lemay, Aaron Smith, Pamela Ronald, Hao Feng, and Gabriel Youtsey
Paper page for Special report: AI Institute for next generation food systems (AIFS)Project for Special report: AI Institute for next generation food systems (AIFS)DOI 10.1016/j.compag.2022.106819
Preprint · arXiv · 2021
Simultaneously Predicting Multiple Plant Traits From Multiple Sensors Via Deformable CNN Regression
Raja, Pranav, Alexander Olenskyj, Hamid Kamangir, and Mason Earles
Paper page for Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression Project for Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression DOI 10.48550/arxiv.2112.03205
Conference Article · International Conference on Computer Vision · 2021
Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection
Fei, Zhenghao, Alexander Olenskyj, Brian N Bailey, and Mason Earles
Paper page for Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection Project for Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection DOI 10.1109/ICCVW54120.2021.00147

Back to the team

Mason Earles

Project involvement

AgML: Open-Source Infrastructure to Accelerate Scaling of Agricultural AI Technologies

Developing an AI-Enabled Toolkit for Routine Integration of Quality Traits into Molecular Breeding Strategies

Developing an AI-Enabled Toolkit for Routine Integration of Quality Traits into Molecular Breeding Strategies

AIFS publications

Genetic Mapping and Genomic Prediction for Agronomic, Grain Compositional, and Sensing-enabled Traits in a Cowpea MAGIC Population Along an Environmental Gradient

Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments?

Does Your VFM Speak Plant? The Botanical Grammar of Vision Foundation Models for Object Detection

CMAViT: Integrating Climate, Management, and Remote Sensing Data for Crop Yield Prediction With Multimodel Vision Transformers

Are Vision-language Models Ready to Zero-shot Replace Supervised Classification Models in Agriculture?

Enhancing AI Microscopy for Foodborne Bacterial Classification Using Adversarial Domain Adaptation to Address Optical and Biological Variability

Integration of Crop Modeling and Sensing Into Molecular Breeding for Nutritional Quality and Stress Tolerance

AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification

Predicting Crop Yield Lows Through the Highs Via Binned Deep Imbalanced Regression: A Case Study on Vineyards

INatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species

Deep Learning Enabled Rapid Classification of Yeast Species in Food by Imaging of Yeast Microcolonies

DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots

The AIFS Institute: Building a Better Food System Through AI

Large-scale Spatio-temporal Yield Estimation Via Deep Learning Using Satellite and Management Data Fusion in Vineyards

An Open-Source Simulation Toolbox for Annotation of Images and Point Clouds in Agricultural Scenarios

Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models

AI-enabled Biosensing for Rapid Pathogen Detection: From Liquid Food to Agricultural Water

Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging

Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution Via Proximal RGB-D Imagery and End-to-End Deep Learning

Special Report: AI Institute for Next Generation Food Systems (AIFS)

Simultaneously Predicting Multiple Plant Traits From Multiple Sensors Via Deformable CNN Regression

Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection