2 citations · 5 across the 8 of their papers we have counts for
8 papers
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.…
DAVIS-Ag: A Synthetic Plant Dataset for Prototyping Domain-Inspired Active Vision in Agricultural Robots
Taeyeong Choi, Dario Guevara, Zifei Cheng +5
In agricultural environments, viewpoint planning can be a critical functionality for a robot with visual sensors to obtain informative observations of objects of interest (e.g., fr…
Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models
Amogh Joshi, Dario Guevara, Mason Earles
In recent years, deep learning models have become the standard for agricultural computer vision. Such models are typically fine-tuned to agricultural tasks using model weights that…
Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression
Pranav Raja, Alex Olenskyj, Hamid Kamangir +1
Trait measurement is critical for the plant breeding and agricultural production pipeline. Typically, a suite of plant traits is measured using laborious manual measurements and th…
Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection
Zhenghao Fei, Alex Olenskyj, Brian N. Bailey +1
Training real-world neural network models to achieve high performance and generalizability typically requires a substantial amount of labeled data, spanning a broad range of variat…