Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models

no code implementations • 4 Aug 2022 • Amogh Joshi, Dario Guevara, Mason Earles

Our experiments guide us in developing a number of approaches to improve data efficiency when training agricultural deep learning models, without large-scale modifications to existing pipelines.

Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression

1 code implementation • 6 Dec 2021 • Pranav Raja, Alex Olenskyj, Hamid Kamangir, Mason Earles

Here, we introduce a relatively simple convolutional neural network (CNN) model that accepts multiple sensor inputs and predicts multiple continuous trait outputs - i. e. a multi-input, multi-output CNN (MIMO-CNN).

regression

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

1 code implementation • 30 Aug 2021 • Zhenghao Fei, Alex Olenskyj, Brian N. Bailey, Mason Earles

To enable more data efficient and generalizable neural network models in agriculture, we propose a method that generates photorealistic agricultural images from a synthetic 3D crop model domain into real world crop domains.

Domain Adaptation Generative Adversarial Network +3