A Dataset for Forestry Pest Identification

Liu, Bing; Liu, Luyang; Zhuo, Ran; Chen, Weidong; Duan, Rui; Wang, Guishen

doi:10.3389/fpls.2022.857104

Cited by 4 publications

(11 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2018) ), but they have problems such as few pest species, being unable to apply to the actual scene, and the data set is not open to the public. However, the recently published Forestry Pest dataset has solved the above problems well Liu et al. (2022a) .…”

Section: Methodsmentioning

confidence: 99%

“…At present, there are many datasets related to the Forestry Pest identification task (such as Hong et al (2021); Chen et al (2019); Sun et al (2018)), but they have problems such as few pest species, being unable to apply to the actual scene, and the data set is not open to the public. However, the recently published Forestry Pest dataset has solved the above problems well Liu et al (2022a). The dataset contains 7163 images and 31 forest pests.…”

Section: Datasetmentioning

confidence: 99%

“…Therefore, we use the dataset of Liu et al (2022a) for training. In order to ensure the training results, the Forestry Pest dataset is randomly divided according to the following proportion: (Train: Val=9:1): Test=9:1.…”

Section: Datasetmentioning

confidence: 99%

“…Therefore, finding a public and suitable forestry pest dataset is difficult. However, the forestry pest dataset recently published by Liu et al. (2022a) , which contains 31 pests and more than 7,000 images, lays a good foundation for the training of target detectors in this field.…”

Section: Related Workmentioning

confidence: 99%

“…The recently published forestry pest dataset solves the above problems well. The dataset contains 7163 images and 31 species of forestry pests Liu et al. (2022a) .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Skip DETR: end-to-end Skip connection model for small object detection in forestry pest dataset

et al. 2023

Self Cite

View full text Add to dashboard Cite

Object detection has a wide range of applications in forestry pest control. However, forest pest detection faces the challenges of a lack of datasets and low accuracy of small target detection. DETR is an end-to-end object detection model based on the transformer, which has the advantages of simple structure and easy migration. However, the object query initialization of DETR is random, and random initialization will cause the model convergence to be slow and unstable. At the same time, the correlation between different network layers is not strong, resulting in DETR is not very ideal in small object training, optimization, and performance. In order to alleviate these problems, we propose Skip DETR, which improves the feature fusion between different network layers through skip connection and the introduction of spatial pyramid pooling layers so as to improve the detection results of small objects. We performed experiments on Forestry Pest Datasets, and the experimental results showed significant AP improvements in our method. When the value of IoU is 0.5, our method is 7.7% higher than the baseline and 6.1% higher than the detection result of small objects. Experimental results show that the application of skip connection and spatial pyramid pooling layer in the detection framework can effectively improve the effect of small-sample obiect detection.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The recently published forestry pest dataset solves the above problems well. The dataset contains 7163 images and 31 species of forestry pests Liu et al. (2022a) .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Skip DETR: end-to-end Skip connection model for small object detection in forestry pest dataset

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

An Improved Deep Learning Model Implementation for Pest Species Detection

Agarwal,

Kalita,

Dubey

et al. 2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning

Liu

Guo

et al. 2023

Forests

View full text Add to dashboard Cite

Infestations or parasitism by forestry pests can lead to adverse consequences for tree growth, development, and overall tree quality, ultimately resulting in ecological degradation. The identification and localization of forestry pests are of utmost importance for effective pest control within forest ecosystems. To tackle the challenges posed by variations in pest poses and similarities between different classes, this study introduced a novel end-to-end pest detection algorithm that leverages deep convolutional neural networks (CNNs) and a transfer learning technique. The basic architecture of the method is YOLOv5s, and the C2f module is adopted to replace part of the C3 module to obtain richer gradient information. In addition, the DyHead module is applied to improve the size, task, and spatial awareness of the model. To optimize network parameters and enhance pest detection ability, the model is initially trained using an agricultural pest dataset and subsequently fine-tuned with the forestry pest dataset. A comparative analysis was performed between the proposed method and other mainstream target detection approaches, including YOLOv4-Tiny, YOLOv6, YOLOv7, YOLOv8, and Faster RCNN. The experimental results demonstrated impressive performance in detecting 31 types of forestry pests, achieving a detection precision of 98.1%, recall of 97.5%, and mAP@.5:.95 of 88.1%. Significantly, our method outperforms all the compared target detection methods, showcasing a minimum improvement of 2.1% in mAP@.5:.95. The model has shown robustness and effectiveness in accurately detecting various pests.

show abstract

A Dataset for Forestry Pest Identification

Cited by 4 publications

References 34 publications

Skip DETR: end-to-end Skip connection model for small object detection in forestry pest dataset

Skip DETR: end-to-end Skip connection model for small object detection in forestry pest dataset

An Improved Deep Learning Model Implementation for Pest Species Detection

Detection of Forestry Pests Based on Improved YOLOv5 and Transfer Learning

Contact Info

Product

Resources

About