# An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

arXiv: https://arxiv.org/abs/2006.01228 · HTML: https://mlsanity.com/paper/2006.01228/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models](https://mlsanity.com/paper/2208.02707/) (2022)
- [The TerraByte Client: providing access to terabytes of plant data](https://mlsanity.com/paper/2203.13691/) (2022)
- [Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture](https://mlsanity.com/paper/2108.05789/) (2021)
- [Generative diffusion models for agricultural AI: plant image generation, indoor-to-outdoor translation, and expert preference alignment](https://mlsanity.com/paper/2512.19632/) (2025)
- [Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture](https://mlsanity.com/paper/2211.02972/) (2022)
- [A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images](https://mlsanity.com/paper/2306.09418/) (2023)
- [Plant Species Recognition with Optimized 3D Polynomial Neural Networks and Variably Overlapping Time-Coherent Sliding Window](https://mlsanity.com/paper/2203.02611/) (2022)
- [Investigating classification learning curves for automatically generated and labelled plant images](https://mlsanity.com/paper/2205.10955/) (2022)
- [A Case for Dataset Specific Profiling](https://mlsanity.com/paper/2208.03315/) (2022)
- [Real-Time On-the-Go Annotation Framework Using YOLO for Automated Dataset Generation](https://mlsanity.com/paper/2512.01165/) (2025)

## Abstract (arXiv)

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and plant segmentation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.