5 papers
Are vision-language models ready to zero-shot replace supervised classification models in agriculture?
Earl Ranario, Mason J. Earles
Vision-language models (VLMs) are increasingly proposed as general-purpose solutions for visual recognition tasks, yet their reliability for agricultural decision support remains p…
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, Mason Earles
California is a global leader in agricultural production, contributing 12.5% of the United States total output and ranking as the fifth-largest food and cotton supplier in the worl…
AgRowStitch: A High-fidelity Image Stitching Pipeline for Ground-based Agricultural Images
Isaac Kazuo Uyehara, Heesup Yun, Earl Ranario +1
Agricultural imaging often requires individual images to be stitched together into a final mosaic for analysis. However, agricultural images can be particularly challenging to stit…
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, Mason Earles
Accurate identification of crop and weed species is critical for precision agriculture and sustainable farming. However, it remains a challenging task due to a variety of factors -…
Enhancing AI microscopy for foodborne bacterial classification via adversarial domain adaptation across optical and biological variability
Siddhartha Bhattacharya, Aarham Wasit, Mason Earles +3
Rapid detection of foodborne bacteria is critical for food safety and quality, yet traditional culture-based methods require extended incubation and specialized sample preparation.…