# Synthetic Crop-Weed Image Generation and its Impact on Model Generalization

Garen Boyadjian, Cyrille Pierre, Johann Laconte, Riccardo Bertoglio

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Garen Boyadjian, Cyrille Pierre, Johann Laconte, Riccardo Bertoglio. Synthetic Crop-Weed Image Generation and its Impact on Model Generalization. IROS 2025 Workshop on Agricultural Robotics and Automation: Driving Innovation in Agri-Food Systems, Oct 2025, Hangzhou, China. hal-05344674

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Distributed under a Creative Commons Attribution 4.0 International License
Synthetic Crop-Weed Image Generation
and its Impact on Model Generalization

Garen Boyadjian*, Cyrille Pierre*, Johann Laconte*, and Riccardo Bertoglio*

* INRAE, UR TSCF, Aubière, France

{garen.boyadjian, cyrille.pierre, johann.laconte, riccardo.bertoglio}@inrae.fr

**Abstract**— Precise semantic segmentation of crops and weeds is necessary for agricultural weeding robots. However, training deep learning models requires large annotated datasets, which are costly to obtain in real fields. Synthetic data can reduce this burden, but the gap between simulated and real images remains a challenge. In this paper, we present a pipeline for procedural generation of synthetic crop-weed images using Blender, producing annotated datasets under diverse conditions of plant growth, weed density, lighting, and camera angle. We benchmark several state-of-the-art segmentation models on synthetic and real datasets and analyze their cross-domain generalization. Our results show that training on synthetic images leads to a sim-to-real gap of 10%, surpassing previous state-of-the-art methods. Moreover, synthetic data demonstrates good generalization properties, outperforming real datasets in cross-domain scenarios. These findings highlight the potential of synthetic agricultural datasets and support hybrid strategies for more efficient model training.

I. INTRODUCTION

Managing or eliminating weeds that compete with crops for nutrients, water, sunlight, and space is essential in agriculture [1]. Machine learning, especially deep learning, is a powerful tool for distinguishing crops from unwanted weeds [2], [3], [4]. These models can equip autonomous agricultural robots with the capacity to mechanically destroy the weeds without using herbicides. However, deep learning models need a large number of annotated images to train effectively, and producing these annotations is costly and time-consuming. Multiple images must be collected under different environmental conditions such as varying weather, plant sizes, and times of day. Each image then requires manual pixel-level annotation to label ground, crop, and weed areas, which serves as ground truth for training the model. Synthetic images have proven to be a promising alternative to real ones because they are automatically labeled and can be generated in unlimited quantities [5], [6], [7]. In this work, we leverage Blender and the CropCraft<fnref n="1" /> tool to generate 3D models of crop rows containing various types of weeds. We then generate synthetic images (Figure 1) simulating an agricultural robot equipped with a downward-facing camera.

This work has been funded by the french National Research Agency (ANR), under the grant ANR-22-LCV2-0002, attributed to the joint laboratory Agrivia (https://anr.fr/Projet-ANR-22-LCV2-0002). It has also received the support of the French government research program "Investissements d'Avenir" through the IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25), the IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01) and the RobotEx Equipment of Excellence (ANR-10-EQPX-44).

<figure><img src="image_2.png" /><figcaption>(a) Afternoon, tall maize, low weed density</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>(b) Afternoon, small maize, medium weed density, 30° camera angle</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(c) Noon, tall maize, high weed density</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(d) Night, small maize, medium weed density</figcaption></figure>

<caption>Fig. 1: Examples of synthetic images generated with CropCraft under different conditions of time of day, maize growth stage, weed density, and camera angle.</caption>

These images are used to train deep learning models to distinguish crops from weeds, and their accuracy is compared with models trained on real images. Our generation pipeline leads to a sim-to-real gap of 10%, which is lower than previous state-of-the-art approaches [8], which reported a sim-to-real gap of approximately 20% (mIoU). Furthermore, we provide new insights into combining synthetic and real images. Previous studies suggested that adding a few real images to synthetic datasets improves accuracy. We show, however, that comparable performance can be achieved with just a small set of real images, while synthetic data provides a clear benefit for cross-domain generalization. In summary, our contributions are: (1) a pipeline for generating synthetic images from procedurally generated 3D models of crop rows with weeds, achieving a reduced sim-to-real gap, and (2) a comparative analysis of deep learning models for crop-weed segmentation trained on synthetic and real images, with a focus on generalization.

II. RELATED WORK

A. 3D Simulation Platforms

One way to generate synthetic images involves procedural generation using game engines and 3D rendering. These

<footnote n="1">¹https://github.com/Romea/cropcraft</footnote>
tools allow full control over lighting, weather, object posi-
tioning, and other environmental factors. Di Cicco et al. [8]
used Unreal Engine as the graphics engine to procedurally
generate sugar beet synthetic images with different species
of weeds. They used a kinematic tree model to generate a
wide variety of realistic leaves and plants by modulating and
randomizing parameters, allowing the creation of different
species and growth stages from a few base textures. Later,
Carbone et al. [9] extended the previous approach by adding
the NIR channel to the synthetic RGB images. Both studies
demonstrated that such synthetic data could effectively train
or supplement small real datasets. The main limitation of
generating synthetic images through 3D simulators is the
availability of high-quality plant models, as well as textures
and plant edges that can appear overly sharp or repetitive,
reducing the overall realism of the images.

B. *Cut and Paste*

Another common technique is the cut-and-paste approach,
where real plant instances are extracted and superimposed
onto diverse backgrounds. Hu et al. [10] introduced an image
synthesis pipeline that combined cut-and-paste synthesis
with "Noisy Student Training." A unique aspect of their
work was the use of a color matching algorithm between
plant instances and target backgrounds. Sapkota et al. [11]
explored various synthetic image generation strategies for
precision weed detection and biomass estimation in cotton.
Their study investigated the effect of crop row arrangement,
plant instance diversity, and clipping methods (manual vs.
automatic), finding that real plant instance-based synthetic
images could achieve comparable detection accuracy to real
images and that crop row orientation in synthetic images was
beneficial compared to random orientation. Picon et al. [12]
developed a method to generate synthetic plant communities
from images of individual plants, mimicking the distribution
of real field images. A key limitation of the cut-and-paste
approach is the lack of control over environmental factors
such as lighting, which often results in unrealistic blending
between plants and background. Another important drawback
is its reliance on real data, sometimes requiring annotations.

C. *Generative AI*

Generative Adversarial Networks (GANs) have been
widely used for synthetic data augmentation in crop-weed
segmentation. Fawakherji et al. [13] proposed a GAN-based
strategy generating specific synthetic crop instances super-
imposed on real images, later extended [14] with a condi-
tional GAN (cGAN) for multi-spectral (RGB+NIR) images,
conditioning on object shape to improve segmentation and
generalization. Espejo-Garcia et al. [15] combined DCGANs
with agricultural transfer learning, enhancing performance by
fine-tuning pre-trained models with GAN-generated images.
Chong et al. [16] introduced unsupervised crop-weed label
generation via StyleGAN2 style mixing and label refinement,
improving segmentation across new fields and platforms
without manual labeling. More recently, diffusion models
have shown superior image synthesis capabilities. Chen et

al. [17] demonstrated their high-quality, diverse weed image
generation, outperforming GANs. Deng and Lu [18] used
ControlNet-stable diffusion for multi-class weed detection,
producing images with broad intra-class and background
variations. Modak and Stein [19], [20] combined SAM with
Stable Diffusion to generate realistic training images, boost-
ing mean Average Precision for compact CNNs like YOLO
nano. Ramos et al. [21] proposed a diffusion-DINOv2 clus-
tering pipeline, efficiently generating synthetic data with-
out retraining or complex prompt engineering. Generative
AI-based methods face limitations such as their reliance on
real (and sometimes annotated) data, the risk of producing
unrealistic samples, high computational costs, and limited
fine-grained control over the generation process.

III. MATERIALS AND METHODS

In this section, we describe the datasets, experimental
setup, and deep learning methodologies employed in this
study. In Section III-A we present the synthetic and real-
world datasets, detailing the image generation process and
dataset characteristics. In Section III-B we then outline the
experimental design, including the training strategies and
evaluation metrics adopted to assess model performance.
Finally, in Section III-C we provide the training protocol
and architecture details of the deep learning models tested,
along with the data augmentation and preprocessing steps
applied.

A. *Datasets*

1) *Synthetic Data:* We generated synthetic images along with their corresponding annotated versions using CropCraft and Blender<fnref n="2" />. Blender is an open-source 3D graphics software capable of simulating physics, applying realistic textures, and rendering images of created scenes through its built-in camera. CropCraft is a procedural world generator developed by the TSCF unit at INRAE. It enables the creation of 3D models of agricultural fields and is specifically designed for testing agricultural robotics applications. Users can specify parameters such as the number of plant rows, the number of plants per row, plant types, plant height, and weed density. CropCraft then relies on Blender as its rendering engine to generate 3D models, assign textures, and place them in the scene. In this study, we focused on maize (*Zea mays*) as the target crop, although other plant models are available in CropCraft and custom models can also be added. For weeds, we used three species in the simulations: *Portulaca oleracea*, *Polygonum aviculare*, and *Taraxacum officinale*. A total of 1500 synthetic images were generated with varying characteristics to ensure a high degree of diversity in the dataset. We varied maize plant height (small, medium, large), weed density (low, medium, high), time of day (noon, afternoon, night), and camera angle (top-down view, 30° inclination to the right). This resulted in six subsets of synthetic images, each consisting of 250 images and their corresponding annotations. The resolution

<footnote n="2">²https://www.blender.org</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 2: Examples of real images collected in maize fields during the ROSE and ACRE challenges. The datasets were acquired in different years with different robots and cameras.</figcaption></figure>

of the synthetic images is 1920×1080 (width × height). The annotations are semantic segmentation masks with three classes: crop, weeds, and background. Figure 1 illustrates some examples of the generated synthetic images.

2) Real Data: To evaluate the accuracy of models trained on synthetic images, we collected two datasets of real images captured by robots in agricultural fields. The first dataset comes from the ROSE [22] and ACRE [23], [24] challenges, where autonomous weeding robots were benchmarked during real field campaigns. It consists of 1000 images collected in Montoldre, France, depicting maize plants together with several weed species, including *Lolium perenne*, *Sinapis arvensis*, *Matricaria chamomilla*, and *Chenopodium album*. An important point to note is that the weed species present in the real images are not the same as those used in the synthetic dataset, since these species are not yet available in CropCraft. The real images were acquired by three different robots, using different cameras, and across different years (2019, 2021, and 2022). Image resolutions vary across subsets, ranging from 1024×768 (lowest) to 5184×3456 (highest). All images were manually annotated with semantic segmentation labels distinguishing crop, weeds, and background. This dataset will hereafter be referred to as *Real Montoldre*. Examples of these images are shown in Figure 2.

To further test the generalization of our models, we also included another public dataset, namely the CropAndWeed dataset [25]. From this dataset, we selected only the maize images, which contain multiple weed species such as *Portulaca oleracea*, *Polygonum aviculare*, *Sinapis arvensis*, *Matricaria chamomilla*, and *Chenopodium album*. A total of 1836 maize images were used, with a resolution of 960×544. This dataset is diverse in terms of lighting conditions, plant sizes, soil appearance, and weed densities. It will hereafter be referred to as *Real Steininger*. The number of pixels per class in the three datasets is as follows (in millions): for the synthetic data, 2627M background, 191M crop, and 291M weed; for the *Real Montoldre* dataset, 5177M background, 679M crop, and 219M weed; and for the *Real Steininger* dataset, 3731M background, 75M crop, and 29M weed.

<table><caption>TABLE I: Test IoU performance of different segmentation models trained on synthetic data.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">#Params (M)</th><th rowspan="2">Encoder</th><th colspan="3">IoU (%)</th></tr><tr><th>Avg</th><th>Crop</th><th>Weed</th></tr></thead><tbody><tr><td>DeepLabV3+</td><td>26.7</td><td>ResNet-50</td><td>94.1</td><td>95.4</td><td>87.7</td></tr><tr><td>SegFormer</td><td>87.6</td><td>SwinV2-B</td><td>93.6</td><td>94.4</td><td>87.2</td></tr><tr><td>SegFormer</td><td>88.3</td><td>ConvNeXt-B</td><td>95.3</td><td><strong>96.9</strong></td><td>89.7</td></tr><tr><td>DeepLabV3+</td><td>89.4</td><td>ConvNeXt-B</td><td>94.9</td><td>96.0</td><td>89.5</td></tr><tr><td>UPerNet</td><td>97.7</td><td>ConvNeXt-B</td><td><strong>95.8</strong></td><td><strong>96.9</strong></td><td><strong>91.2</strong></td></tr><tr><td>DPT</td><td>107.8</td><td>SwinV2-B</td><td>94.1</td><td>95.2</td><td>87.8</td></tr></tbody></table>

## B. Experimental Setup

The task addressed in this study is semantic segmentation, i.e., classifying each pixel of an input image into one of three classes: crop, weed, or background. To this end, we first trained several semantic segmentation models with different encoders on the synthetic dataset in order to identify the most suitable configuration for our use case. Model performance was evaluated using the Intersection over Union (IoU), defined as the ratio between the area of overlap and the area of union between the predicted segmentation and the ground truth. Specifically, we report the mean IoU (mIoU) over the three classes, as well as the IoU for the crop class and the weed class separately. Once the best-performing model was identified, it was trained and tested on different combinations of datasets. Regarding the training sets, we experimented with three strategies: (i) training solely on synthetic data, (ii) training solely on real data, and (iii) training on a mix of real and synthetic data. In particular, two mixed datasets were constructed by varying the number of real images included (20 and 700). This allowed us to investigate the effect of gradually increasing the amount of real data, from very small to relatively large subsets. A similar analysis was carried out with the *Real Montoldre* dataset, where the model was trained exclusively on subsets of 20 and 700 real images to assess the added value of synthetic data.

## C. Deep Learning Models Training

Each dataset was randomly split into training, validation, and test sets, with proportions of 70%, 10%, and 20%, respectively. For the *Real Montoldre* dataset (1000 images), the training portion (700 images) was further split to create a subset of 20 images used in the experiments described above. All models were trained under the same hyperparameter settings on a single NVIDIA Tesla V100 GPU with 32 GB of VRAM. Training was performed for 5k iterations using the AdamW optimizer, with a learning rate of 0.001 and a weight decay of 0.001. A cosine annealing learning rate scheduler and a weighted cross-entropy loss were employed, with class weights of 1, 2, and 4 for background, crop, and weed, respectively. The batch size was set to 8. During training, images were augmented using a combination of transformations, including random resizing, random cropping, horizontal and vertical flips, shifting/scaling/rotating, color jittering, random brightness and contrast adjustments,
<table><caption>TABLE II: Test IoU performance of models trained on different datasets and tested across domains.</caption><thead><tr><th rowspan="2">Training Mode</th><th colspan="3">Synthetic</th><th colspan="3">Real Montoldre</th><th colspan="3">Real Steininger [25]</th></tr><tr><th>Avg</th><th>Crop</th><th>Weed</th><th>Avg</th><th>Crop</th><th>Weed</th><th>Avg</th><th>Crop</th><th>Weed</th></tr></thead><tbody><tr><td>Only synthetic data (1050 images)</td><td>96.1</td><td>97.5</td><td>91.9</td><td>70.0</td><td>76.1</td><td>38.5</td><td>73.9</td><td>73.1</td><td>49.4</td></tr><tr><td>Synthetic + 20 Real M. images</td><td>-</td><td>-</td><td>-</td><td>76.3</td><td>83.7</td><td>50.4</td><td>73.2</td><td>70.6</td><td>49.7</td></tr><tr><td>Synthetic + 700 Real M. images</td><td>-</td><td>-</td><td>-</td><td>80.5</td><td>85.6</td><td>59.9</td><td>76.9</td><td>76.3</td><td>55.1</td></tr><tr><td>Real Montoldre 20 images</td><td>-</td><td>-</td><td>-</td><td>77.8</td><td>84.9</td><td>52.4</td><td>63.8</td><td>64.6</td><td>27.6</td></tr><tr><td>Real Montoldre 700 images</td><td>-</td><td>-</td><td>-</td><td><b>80.6</b></td><td><b>86.1</b></td><td><b>59.8</b></td><td>73.8</td><td>73.3</td><td>48.9</td></tr><tr><td>Real Steininger 1285 images</td><td>-</td><td>-</td><td>-</td><td>57.2</td><td>64.9</td><td>18.4</td><td><b>83.1</b></td><td><b>87.8</b></td><td><b>62.2</b></td></tr></tbody></table>

Gaussian blur, and Gaussian noise. As mentioned, we tested multiple architectures and encoders to identify the best-performing setup. The models evaluated were DeepLabv3+, SegFormer, UPerNet, and DPT, with encoders ResNet-50, SwinV2-B, and ConvNeXt-B. During training, images were randomly cropped to 384×384 pixels. For testing, images were resized and padded to 1536×1536 pixels to enable a fair comparison across models, given the size constraints of the SwinV2 encoder. In the remaining experiments, we used an input size of 1088×1920 pixels, which better matches the original aspect ratio of the images. All results were obtained using the checkpoint from the final training iteration.

## IV. RESULTS AND DISCUSSION

We first evaluated several state-of-the-art semantic segmentation models on the synthetic dataset to identify the most suitable architecture. This choice reflects a scenario where real data are scarce or unavailable. The mIoU scores on the synthetic test set are summarized in Table I. Overall, the models performed similarly, with UPerNet paired with a ConvNeXt-B encoder achieving the highest accuracy. Notably, DeepLabV3+ with a ResNet-50 encoder (27M parameters) delivered results comparable with much larger models using ConvNeXt-B or SwinV2-B encoders (90–100M parameters). This suggests that ResNet-50 offers a practical trade-off between performance and efficiency. Since UPerNet produced the best results overall, we selected it for the subsequent experiments.

We then examined how models trained on synthetic images perform when tested on real datasets. The results, shown in Table II, confirm a clear performance drop when models trained exclusively on synthetic data are applied to real images. This degradation is mainly due to differences in the visual appearance of plants, particularly in shape and texture, between synthetic and real domains. Specifically, mIoU decreased by 10.6% on the *Real Montoldre* dataset and by 9.2% on *Real Steininger*. The decline was especially pronounced for the weed class, likely because the weed species represented in the synthetic and real datasets are not the same. Adding real images to the synthetic training set improved performance on *Real Montoldre*, with accuracy increasing as more real images were included. Remarkably, even with only 20 real images, weed IoU improved by 11.9%. This suggests that small amounts of real data complement the knowledge learned from synthetic images. However, when training solely on these real subsets

(without synthetic data), results were superior. This indicates that to maximize performance on a specific dataset/domain, synthetic data provide little benefit compared to real data.

On the other hand, synthetic images appear valuable for improving generalization. For instance, training only on a small subset of 20 *Real Montoldre* images resulted in weaker performance on *Real Steininger* compared to training on synthetic data. With a larger subset of 700 Montoldre images, cross-domain performance was comparable between real and synthetic training (73.8 for real data and 73.9 for synthetic), indicating that sufficiently large real datasets can also support generalization. However, models trained on *Real Steininger* generalized poorly to *Real Montoldre*, despite *Real Steininger* being the larger dataset. In this case as well, training only on synthetic data led to better performance. A plausible explanation for the superiority of training on *Real Montoldre* is that it provides greater variability, as its images were collected across different platforms and years. This diversity may better promote generalization compared to *Real Steininger*.

## V. CONCLUSIONS

In this work, we investigated the role of procedurally generated synthetic images for crop-weed semantic segmentation. Using Blender and CropCraft, we created a diverse dataset of maize fields with weeds and evaluated multiple segmentation architectures across synthetic and real domains. Our experiments highlighted three key findings: (1) training exclusively on synthetic images leads to a sim-to-real gap of about 10% compared to training solely on real images, outperforming previous state-of-the-art methods; (2) combining synthetic and small subsets of real images does not significantly improve performance when targeting a specific dataset; and (3) synthetic data are particularly valuable for improving cross-domain generalization, outperforming models trained only on real datasets. These results demonstrate the potential of synthetic data as an efficient, scalable resource for agricultural robotics. Future work will focus on style transfer, domain adaptation, and expanding the CropCraft library with additional weed species to further enhance realism and applicability.

## ACKNOWLEDGMENTS

We are grateful to the Mésocentre Clermont-Auvergne of the Université Clermont Auvergne for providing access to computing and storage resource facilities.
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