# Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming

arXiv: https://arxiv.org/abs/2407.14119 · HTML: https://mlsanity.com/paper/2407.14119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Towards Principled Methods for Training Generative Adversarial Networks](https://mlsanity.com/paper/1701.04862/) (2017)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming](https://mlsanity.com/paper/1709.03329/) (2017)
- [Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs](https://mlsanity.com/paper/1802.08960/) (2018)
- [Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection](https://mlsanity.com/paper/1612.03019/) (2016)
- [ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network](https://mlsanity.com/paper/1709.00938/) (2017)
- [Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming](https://mlsanity.com/paper/2009.05750/) (2020)

## Built on by (most cited first, 1 of 1)

- [Synthetic Data Augmentation for Enhanced Chicken Carcass Instance Segmentation](https://mlsanity.com/paper/2507.18558/) (2025)

## Abstract (arXiv)

The use of deep learning methods for precision farming is gaining increasing interest. However, collecting training data in this application field is particularly challenging and costly due to the need of acquiring information during the different growing stages of the cultivation of interest. In this paper, we present a method for data augmentation that uses two GANs to create artificial images to augment the training data. To obtain a higher image quality, instead of re-creating the entire scene, we take original images and replace only the patches containing objects of interest with artificial ones containing new objects with different shapes and styles. In doing this, we take into account both the foreground (i.e., crop samples) and the background (i.e., the soil) of the patches. Quantitative experiments, conducted on publicly available datasets, demonstrate the effectiveness of the proposed approach. The source code and data discussed in this work are available as open source.