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

arXiv: https://arxiv.org/abs/2208.02707 · HTML: https://mlsanity.com/paper/2208.02707/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 20 of 20)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [An open access repository of images on plant health to enable the development of mobile disease diagnostics](https://mlsanity.com/paper/1511.08060/) (2015)
- [DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning](https://mlsanity.com/paper/1810.05726/) (2018)
- [PlantDoc: A Dataset for Visual Plant Disease Detection](https://mlsanity.com/paper/1911.10317/) (2019)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [MinneApple: A Benchmark Dataset for Apple Detection and Segmentation](https://mlsanity.com/paper/1909.06441/) (2019)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming](https://mlsanity.com/paper/1709.03329/) (2017)
- [A Public Image Database for Benchmark of Plant Seedling Classification Algorithms](https://mlsanity.com/paper/1711.05458/) (2017)
- [An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture](https://mlsanity.com/paper/2006.01228/) (2020)
- [Multispecies fruit flower detection using a refined semantic segmentation network](https://mlsanity.com/paper/1809.10080/) (2018)
- [Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection](https://mlsanity.com/paper/2108.13344/) (2021)
- [The Power of Transfer Learning in Agricultural Applications: AgriNet](https://mlsanity.com/paper/2207.03881/) (2022)
- [A methodology for detection and localization of fruits in apples orchards from aerial images](https://mlsanity.com/paper/2110.12331/) (2021)

## Built on by (most cited first, 7 of 7)

- [iNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species](https://mlsanity.com/paper/2503.20068/) (2025)
- [AgriChat: A Multimodal Large Language Model for Agriculture Image Understanding](https://mlsanity.com/paper/2603.16934/) (2026)
- [CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture](https://mlsanity.com/paper/2305.10084/) (2023)
- [Are vision-language models ready to zero-shot replace supervised classification models in agriculture?](https://mlsanity.com/paper/2512.15977/) (2025)
- [SegRAG: Training-Free Retrieval-Augmented Semantic Segmentation](https://mlsanity.com/paper/2605.17630/) (2026)
- [Enabling Plant Phenotyping in Weedy Environments using Multi-Modal Imagery via Synthetic and Generated Training Data](https://mlsanity.com/paper/2509.19208/) (2025)
- [Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap](https://mlsanity.com/paper/2609.09417/) (2026)

## Abstract (arXiv)

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 were originally fit to more general, non-agricultural datasets. This lack of agriculture-specific fine-tuning potentially increases training time and resource use, and decreases model performance, leading an overall decrease in data efficiency. To overcome this limitation, we collect a wide range of existing public datasets for three distinct tasks, standardize them, and construct standard training and evaluation pipelines, providing us with a set of benchmarks and pretrained models. We then conduct a number of experiments using methods which are commonly used in deep learning tasks, but unexplored in their domain-specific applications for agriculture. 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. Our results demonstrate that even slight training modifications, such as using agricultural pretrained model weights, or adopting specific spatial augmentations into data processing pipelines, can significantly boost model performance and result in shorter convergence time, saving training resources. Furthermore, we find that even models trained on low-quality annotations can produce comparable levels of performance to their high-quality equivalents, suggesting that datasets with poor annotations can still be used for training, expanding the pool of currently available datasets. Our methods are broadly applicable throughout agricultural deep learning, and present high potential for significant data efficiency improvements.