CoFly-WeedDB: A UAV image dataset for weed detection and species identification

Krestenitis, Marios; Raptis, Emmanuel K.; Kapoutsis, Athanasios Ch.; Ioannidis, Konstantinos; Kosmatopoulos, Elias B.; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.1016/j.dib.2022.108575

Cited by 15 publications

(6 citation statements)

References 5 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used a publicly available dataset [45], which consists of 201 RGB images acquired with DJI Phantom Pro 4 from a cotton field in Greece by Krestenitsi et al [45]. The cotton field images were acquired by flying the UAV at a height of 5 m so that it provides a close clear view of the cotton field.…”

Section: Datasetmentioning

confidence: 99%

“…The dataset includes a total of 201 RGB images of size 1280 px × 720 px. In the dataset, there are very few pixels of purslane weeds (only 0.27 × 10 6 pixels), whereas the Johnson grass, field bindweed and backgrounds have 1.44 × 10 6 , 7.56 × 10 6 and 175 × 10 6 pixels [45], respectively. It is clear that the dataset is highly imbalanced, which make the automated weed detection task more challenging.…”

Section: Datasetmentioning

confidence: 99%

“…The data used in this study are made public by [45] and can be accessed at https://zenodo.org/record/6697343 (accessed on 2 June 2023). The link for the implementation code can be accessed at https://github.com/dahalsweekar/Deep-Weed-Segmentation (accessed on 22 September 2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study

Shahi,

Dahal,

Sitaula

et al. 2023

Drones

View full text Add to dashboard Cite

Semantic segmentation has been widely used in precision agriculture, such as weed detection, which is pivotal to increasing crop yields. Various well-established and swiftly evolved AI models have been developed of late for semantic segmentation in weed detection; nevertheless, there is insufficient information about their comparative study for optimal model selection in terms of performance in this field. Identifying such a model helps the agricultural community make the best use of technology. As such, we perform a comparative study of cutting-edge AI deep learning-based segmentation models for weed detection using an RGB image dataset acquired with UAV, called CoFly-WeedDB. For this, we leverage AI segmentation models, ranging from SegNet to DeepLabV3+, combined with five backbone convolutional neural networks (VGG16, ResNet50, DenseNet121, EfficientNetB0 and MobileNetV2). The results show that UNet with EfficientNetB0 as a backbone CNN is the best-performing model compared with the other candidate models used in this study on the CoFly-WeedDB dataset, imparting Precision (88.20%), Recall (88.97%), F1-score (88.24%) and mean Intersection of Union (56.21%). From this study, we suppose that the UNet model combined with EfficientNetB0 could potentially be used by the concerned stakeholders (e.g., farmers, the agricultural industry) to detect weeds more accurately in the field, thereby removing them at the earliest point and increasing crop yields.

show abstract

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study

Shahi,

Dahal,

Sitaula

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…This method can provide theoretical support for the rational spraying of herbicides and reduce damage to the environment. Compared with the conventional feature method, the deep learning network has obvious advantages in weed identification accuracy, but its huge computational load and early database preparation make it ineffective [33,34]. Further, it is not easy to promote in actual production, so image processing methods based on conventional features are also constantly improving.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle-Scale Weed Segmentation Method Based on Image Analysis Technology for Enhanced Accuracy of Maize Seedling Counting

Yang,

Zhu,

Zhang

et al. 2024

Agriculture

View full text Add to dashboard Cite

The number of maize seedlings is a key determinant of maize yield. Thus, timely, accurate estimation of seedlings helps optimize and adjust field management measures. Differentiating “multiple seedlings in a single hole” of maize accurately using deep learning and object detection methods presents challenges that hinder effectiveness. Multivariate regression techniques prove more suitable in such cases, yet the presence of weeds considerably affects regression estimation accuracy. Therefore, this paper proposes a maize and weed identification method that combines shape features with threshold skeleton clustering to mitigate the impact of weeds on maize counting. The threshold skeleton method (TS) ensured that the accuracy and precision values of eliminating weeds exceeded 97% and that the missed inspection rate and misunderstanding rate did not exceed 6%, which is a significant improvement compared with traditional methods. Multi-image characteristics of the maize coverage, maize seedling edge pixel percentage, maize skeleton characteristic pixel percentage, and connecting domain features gradually returned to maize seedlings. After applying the TS method to remove weeds, the estimated R2 is 0.83, RMSE is 1.43, MAE is 1.05, and the overall counting accuracy is 99.2%. The weed segmentation method proposed in this paper can adapt to various seedling conditions. Under different emergence conditions, the estimated R2 of seedling count reaches a maximum of 0.88, with an RMSE below 1.29. The proposed approach in this study shows improved weed recognition accuracy on drone images compared to conventional image processing methods. It exhibits strong adaptability and stability, enhancing maize counting accuracy even in the presence of weeds.

show abstract

“…The journal "Data in Brief" features seven relevant articles [11][12][13][14][15][16][17], including one that pertains to UAV images of a cotton field [11], another that focuses on UAV data for avocado classification [12], two that present UAV images obtained over a vineyard [13,14], and one that presents plant and soil data for forage crops [15]. Additionally, there is an article that showcases UAV RGB images of soybean crops [16] and another that features hyperspectral imagery of potato cultivation [17].…”

mentioning

confidence: 99%

A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan

Maulit

Nugumanova

Apayev

et al. 2023

Data

View full text Add to dashboard Cite

This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.

show abstract

CoFly-WeedDB: A UAV image dataset for weed detection and species identification

Cited by 15 publications

References 5 publications

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study

Deep Learning-Based Weed Detection Using UAV Images: A Comparative Study

Unmanned Aerial Vehicle-Scale Weed Segmentation Method Based on Image Analysis Technology for Enhanced Accuracy of Maize Seedling Counting

A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan

Contact Info

Product

Resources

About