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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

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

Amogh Joshi^1,2,3, Dario Guevara^1,2,3, Mason Earles^1,2,3,3

¹Department of Viticulture and Enology, University of California, Davis, Davis, CA, USA.

Plant Phenomics (Washington, D.C.)

|September 8, 2023

Summary

Improving deep learning for agriculture requires specialized training. Using agriculture-specific pre-trained models and data augmentation significantly boosts performance and reduces training time for computer vision tasks.

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Area of Science:

Agricultural Computer Vision
Deep Learning
Machine Learning

Background:

Deep learning models are standard in agricultural computer vision but often use general datasets for fine-tuning.
This approach can lead to increased training time, resource use, and decreased model performance, reducing data efficiency.

Purpose of the Study:

To enhance data efficiency in training agricultural deep learning models.
To develop and evaluate methods for improving model performance and reducing training time without major pipeline changes.

Main Methods:

Collected and standardized diverse public agricultural datasets for three distinct tasks.
Established standard training and evaluation pipelines with benchmarks and pre-trained models.
Experimented with novel deep learning methods and domain-specific agricultural applications.

Main Results:

Agricultural pre-trained model weights and spatial data augmentations significantly improve model performance and reduce convergence time.
Models trained on low-quality annotations achieve performance comparable to those trained on high-quality data.
Methods are broadly applicable and show potential for substantial data efficiency gains.

Conclusions:

Optimizing deep learning training strategies, even with minor modifications, can yield significant improvements in agricultural computer vision.
The use of agricultural-specific pre-trained models and effective data augmentation are key to enhancing data efficiency.
The findings suggest that lower-quality annotated datasets can still be valuable for training, expanding available data resources.