Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection. - Zhenghao Fei; Alex Olenskyj; Brian N. Bailey; Mason Earles (ICCVW, 2021)

Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression. - Pranav Raja; Alex Olenskyj; Hamid Kamangir; Mason Earles (CoRR, 2021)

Special report: AI Institute for next generation food systems (AIFS). - Ilias Tagkopoulos; Stephen F. Brown; Xin Liu 002; Qing Zhao; Tarek I. Zohdi; J. Mason Earles; Nitin Nitin; Daniel E. Runcie; Danielle G. Lemay; Aaron D. Smith; Pamela C. Ronald; Hao Feng; et al. (Comput. Electron. Agric., 2022)

A workflow for segmenting soil and plant X-ray CT images with deep learning in Googles Colaboratory. - Devin A. Rippner; Pranav Raja; Mason Earles; Alexander Buchko; Mina Momayyezi; Fiona Duong; Dilworth Parkinson; Elizabeth Forrestel; Ken Shackel; Andrew J. McElrone (CoRR, 2022)

Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models. - Amogh Joshi 004; Dario Guevara; Mason Earles (CoRR, 2022)

End-to-end deep learning for directly estimating grape yield from ground-based imagery. - Alexander G. Olenskyj; Brent S. Sams; Zhenghao Fei; Vishal Singh; Pranav V. Raja; Gail M. Bornhorst; J. Mason Earles (Comput. Electron. Agric., 2022)

An Open Source Simulation Toolbox for Annotation of Images and Point Clouds in Agricultural Scenarios. - Dario Guevara; Amogh Joshi 004; Pranav Raja; Elizabeth Forrestel; Brian N. Bailey; Mason Earles (ISVC (1), 2023)

DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots. - Taeyeong Choi; Dario Guevara; Grisha Bandodkar; Zifei Cheng; Chonghan Wang; Brian N. Bailey; Mason Earles; Xin Liu 002 (CoRR, 2023)

DAVIS-Ag: A Synthetic Plant Dataset for Prototyping Domain-Inspired Active Vision in Agricultural Robots. - Taeyeong Choi; Dario Guevara; Zifei Cheng; Grisha Bandodkar; Chonghan Wang; Brian N. Bailey; Mason Earles; Xin Liu 002 (CASE, 2024)

VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture. - Heesup Yun; Sassoum Lo; Christine H. Diepenbrock; Brian N. Bailey; J. Mason Earles (CVPR Workshops, 2024)

The AIFS Institute: Building a better food system through AI. - Ilias Tagkopoulos; J. Mason Earles; Danielle G. Lemay; Xin Liu 002; Nitin Nitin; Aaron D. Smith; Tarek I. Zohdi; Stephen F. Brown (AI Mag., 2024)

CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision Transformers. - Hamid Kamangir; Brent S. Sams; Nick Dokoozlian; Luis Sanchez 001; J. Mason Earles (CoRR, 2024)

Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability. - Siddhartha Bhattacharya; Aarham Wasit; Mason Earles; Nitin Nitin; Luyao Ma; Jiyoon Yi (CoRR, 2024)

Large-scale spatio-temporal yield estimation via deep learning using satellite and management data fusion in vineyards. - Hamid Kamangir; Brent S. Sams; Nick Dokoozlian; Luis Sanchez 001; J. Mason Earles (Comput. Electron. Agric., 2024)

iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2, 959 Crop and Weed Species. - Naitik Jain; Amogh Joshi 004; Mason Earles (CVPR Workshops, 2025)

California Crop Yield Benchmark: Combining Satellite Image, Climate, Evapotranspiration, and Soil Data Layers for County-Level Yield Forecasting of Over 70 Crops. - Hamid Kamangir; Mona Hajiesmaeeli; J. Mason Earles (CVPR Workshops, 2025)

AGILE: A Diffusion-Based Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification. - Earl Ranario; Lars Lundqvist; Heesup Yun; Brian N. Bailey; J. Mason Earles (CVPR Workshops, 2025)

Predicting crop yield lows through the highs via binned deep imbalanced regression: A case study on vineyards. - Hamid Kamangir; Brent S. Sams; Nick Dokoozlian; Luis Sanchez 001; J. Mason Earles (Int. J. Appl. Earth Obs. Geoinformation, 2025)

Strawberry fruit yield forecasting using image-based time-series plant phenological stages sequences. - Andrés Montes de Oca; Troy Magney; Stavros G. Vougioukas; Dario Racano; Alejandro Torrez-Orozco; Steven A. Fennimore; Frank N. Martin; Mason Earles (Comput. Electron. Agric., 2025)

iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species. - Naitik Jain; Amogh Joshi 004; Mason Earles (CoRR, 2025)

AgRowStitch: A High-fidelity Image Stitching Pipeline for Ground-based Agricultural Images. - Isaac Kazuo Uyehara; Heesup Yun; Earl Ranario; Mason Earles (CoRR, 2025)