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UAVWeedSegmentation

This repository provides the source code for "Deep Learning-based Early Weed Segmentation using UAV Images of Sorghum Fields".

Figure 1: Sharpness assessment for the dataset used in this study. The calculated sharpness matches the manual assessment of this dataset, as images 04, 05, 06 and 10 from the trainval dataset were relatively sharper as the other captures. The test-set captures were degraded with different degrees of motion blur with 01, 02 and 03 being blurred the most. The color code matches in the sub-figures. a Relative Sharpness Value based on TVoL. Lower values indicate a higher degree of motion blur. Most captures in the dataset were blurred, but there were also some relatively sharper images present. b Flight plan of the used dataset showing the location of the captures. Gray dots indicate captures that were not annotated. Relatively sharp captures were located at the edges of the flight plan, where the drone was flying slower to rotate, as indicated by the limegreen, brown, pink, yellow and cyan dots. c Example patches from the captures, where color of the border indicates the corresponding capture.

Results on the Test-Set

The results of the best performing model (UNet + ResNet-34) are shown.

Per-class Metrics

class precision recall f1-score support
Background 99.80 99.93 99.86 137909280
Sorghum 91.58 86.10 88.76 1249145
Weed 87.64 72.71 79.48 574567
macro avg 93.01 86.25 89.37 139732992
weighted avg 99.68 99.69 99.68 139732992

Confusion Matrix

The confusion matrix shows per-class accuracies for Background (BG), Sorghum (S) and Weed (W).

Confusion Matrix

Figure 2: Normalized confusion matrix by support size in percent shows the pixel-based classification results on the hold-out test-set. Background/soil is denoted by BG, sorghum by S and weed by W. a Test images with a high degree of motion blur (test_01 to test_03, as indicated in Figure 1). b Test images with less degree of motion blur (test_04 to test_07, as indicated in Figure 1).

Example Predictions

Predictions are based on a hold-out test-set.

Example Patches with Predictions

Figure 3: Qualitative results on the hold-out test-set. Image patches of size 400x400 pixel² are cropped from each test image to show more details. Background (BG) is colored in gray, sorghum (S) in blue and weed (W) pixels in orange. The difference map shows only the misclassifications between ground truth and prediction. a-c Examples with a high degree of motion blur. a The general shape of weeds is predicted correctly. b Large weed plants that could not be removed before sowing the field. Most pixels are predicted correctly. There are small artifacts visible, which are due to the patching process. c Weeds intersecting with sorghum plants were predicted correctly. d-f Examples with a low degree of motion blur showing weeds and sorghum plants from different captures. The general shape is predicted correctly. d Patch from test_04. e Patch from test_05. f Patch from test_06.

Requirements

  • Python 3.8
  • PyTorch 1.11.0
  • Compatible Graphics Card with CUDA >9 and cuDNN installed

Installation

  1. Clone this project
git clone git@github.com:grimmlab/UAVWeedSegmentation.git
  1. Install requirements
cd UAVWeedSegmentation
pip install -r requirements.txt

  1. Download and install pytorch with GPU support that is compatible with your GPU

  2. Download trained model from Mendeley Data (https://doi.org/10.17632/4hh45vkp38.5) and paste the unzipped model in /models. Rename the model to model_unet_resnet34_dil0_bilin1_retrained.pt to match the parsing of the script predict_testset.

Predict on new UAV images

Now, we can predict weeds in new UAV images using following script:

python3 save_patches.py
python3 predict_testset.py [model_path] [subset]
# example
python3 save_patches.py
python3 predict_testset.py models/model_unet_resnet34_dil0_bilin1_retrained.pt test

where:

  • model_path is the path to the trained model .pt file
  • subset is the name of the folder in /data to predict on.

Compare Predictions with Ground Truth

For final evaluation, we compare the models predictions with the Ground Truth of a hold-out test set that was annotated manually by agronomy experts. This script generates Figures 3 and 5 of our paper. These figures are saved in /results/predictions/<subset>.

python3 compare_predictions.py [subset] [--bbch]
# example for comparing predictions and ground truth on the hold-out test set
python3 compare_predictions.py test
# example for comparing predictions and ground truth on the additional test images
python3 compare_predictions.py test_different_bbch --bbch 15
python3 compare_predictions.py test_different_bbch --bbch 19

where:

  • subset is the name of the folder in /data to predict on.
  • bbch is the BBCH stage of the UAV captures. Only used for subset test_different_bbch and can be either 15 or 19.

Train new Models

  1. Generate patches from images and annotations
python3 save_patches.py
  1. Train DL Model
python3 train.py [architecture] [feature_extractor]
# example
python3 train.py fcn resnet50

where:

  • architecture is either "fcn8s", "fcn16s", "fcn32s", "unet" or "dlplus"
  • feature_extractor is either "resnet18", "resnet34", "resnet50" or "resnet101"
  • --replace_stride_with_dilation, if using dilation

Examine Training Databases

When using the script train.py, a database will be saved in results/ containing all trials in this study. There is an own database for each feature extractor and architecture. The studies trained for the publication are saved in several subfolders in results/studies can be examined to select the best model architecture and feature extractor. Therefore, change the path in the script compare_studies.py. Additionally, Table 3 of the Paper can be generated using this script.

python3 compare_studies.py

Re-train on the complete trainval set

A freshly initialized model will be trained on the complete training and validation set using the best hyperparameters and model architecture.

python3 retrain.py [architecture] [feature_extractor]
# example
python3 retrain.py unet resnet34

Publication and Citation

Deep Learning-based Early Weed Segmentation using Motion Blurred UAV Images of Sorghum Fields
N Genze, R Ajekwe, Z Güreli, F Haselbeck, M Grieb, DG Grimm
Computers and Electronics in Agriculture, 2022 (https://doi.org/10.1016/j.compag.2022.107388)

@article{GENZE2022107388, title = {Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields}, journal = {Computers and Electronics in Agriculture}, volume = {202}, pages = {107388}, year = {2022}, issn = {0168-1699}, doi = {https://doi.org/10.1016/j.compag.2022.107388}, url = {https://www.sciencedirect.com/science/article/pii/S0168169922006962}, author = {Nikita Genze and Raymond Ajekwe and Zeynep Güreli and Florian Haselbeck and Michael Grieb and Dominik G. Grimm}, keywords = {Deep learning, Weed detection, Weed segmentation, UAV, Precision agriculture} }