# Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Images

arXiv: https://arxiv.org/abs/2109.02716 · HTML: https://mlsanity.com/paper/2109.02716/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [An Introductory Survey on Attention Mechanisms in NLP Problems](https://mlsanity.com/paper/1811.05544/) (2018)
- [weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming](https://mlsanity.com/paper/1709.03329/) (2017)
- [A Survey of Deep Learning Techniques for Weed Detection from Images](https://mlsanity.com/paper/2103.01415/) (2021)
- [WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming](https://mlsanity.com/paper/1808.00100/) (2018)
- [Vine disease detection in UAV multispectral images with deep learning segmentation approach](https://mlsanity.com/paper/1912.05281/) (2019)
- [Deep Convolutional Neural Network for Plant Seedlings Classification](https://mlsanity.com/paper/1811.08404/) (2018)

## Built on by (most cited first, 5 of 5)

- [Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance](https://mlsanity.com/paper/2405.10391/) (2024)
- [Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities](https://mlsanity.com/paper/2305.01899/) (2023)
- [Transformer-Based Spatio-Temporal Association of Apple Fruitlets](https://mlsanity.com/paper/2503.03200/) (2025)
- [Raw Produce Quality Detection with Shifted Window Self-Attention](https://mlsanity.com/paper/2112.13845/) (2021)
- [Accelerated Sub-Image Search For Variable-Size Patches Identification Based On Virtual Time Series Transformation And Segmentation](https://mlsanity.com/paper/2410.15425/) (2024)

## Abstract (arXiv)

Crop and weed monitoring is an important challenge for agriculture and food production nowadays. Thanks to recent advances in data acquisition and computation technologies, agriculture is evolving to a more smart and precision farming to meet with the high yield and high quality crop production. Classification and recognition in Unmanned Aerial Vehicles (UAV) images are important phases for crop monitoring. Advances in deep learning models relying on Convolutional Neural Network (CNN) have achieved high performances in image classification in the agricultural domain. Despite the success of this architecture, CNN still faces many challenges such as high computation cost, the need of large labelled datasets, ... Natural language processing's transformer architecture can be an alternative approach to deal with CNN's limitations. Making use of the self-attention paradigm, Vision Transformer (ViT) models can achieve competitive or better results without applying any convolution operations. In this paper, we adopt the self-attention mechanism via the ViT models for plant classification of weeds and crops: red beet, off-type beet (green leaves), parsley and spinach. Our experiments show that with small set of labelled training data, ViT models perform better compared to state-of-the-art CNN-based models EfficientNet and ResNet, with a top accuracy of 99.8% achieved by the ViT model.