Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks

Hu, Gensheng; Zhu, Yanqiu; Wan, Mingzhu; Bao, Wenxia; Zhang, Yan; Liang, Dong; Yin, Cunjun

doi:10.1080/10106049.2020.1864025

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…Despite these works, more research is need, as there are no significant studies about this topic at ground level which focus on the detection of tree trunks with deep learning models and their evaluation with well-known metrics in the object detection domain. Furthermore, the majority of works related to forest tree detection are focused on performing the detection with Light Detection and Ranging (LiDaR) data alone [ 26 , 27 , 28 , 29 , 30 ], with aerial high-resolution multispectral imagery alone [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] or with a combination of both [ 41 , 42 , 43 , 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.

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Section: Introductionmentioning

confidence: 99%

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

2021

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“…Another way of data augmentation is to use deep learning algorithms to generate new unseen artificial data, including different types of Generative Adversarial Networks (GANs). For example, the authors in Hu et al [ 38 ], Hu et al [ 37 ] used DCGAN architecture to increase data size by generating new unseen data to improve the model performance. Also, to overcome the lack of data to train an efficient deep learning model, the authors in Tetila et al [ 75 ] applied dropout and data augmentation techniques using the Keras module.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed approach overcomes traditional machine learning methods providing an F1-score of 86.3% and a recall of 95.7% against recall rates of 78.3% and 65.2% for SVM and AdaBoost classifiers, respectively (Table 3 ). Similarly, in Hu et al [ 38 ], a combination between MobileNet, Faster R-CNN, Augmentor, and DCGAN architectures was adopted to recognize diseased pinus trees from UAV. DCGAN model was used to increase the number of images used in the training process, while MobileNet architecture was used to reduce the complex background information, such as roads, soils, and shadows that have some feature similarities with the targeted pine tree disease; then, Faster R-CNN was used to detect diseased pine trees.…”

Section: Deep Learning Algorithms To Identify Crop Diseases From Uav-...mentioning

confidence: 99%

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

et al. 2022

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The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers’ naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.

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“…Thus, DL is also used in PWD monitoring, mainly with semantic segmentation and object detection models. Object detection models, such as the faster region convolutional neural network and You Only Look Once version 3 with variety backbones, have been widely applied for PWD identification [26][27][28][29]. These models can provide object-level detection of infected pines, but only define bounding boxes around the detected pines.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

et al. 2021

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Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.

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Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks

Cited by 13 publications

References 20 publications

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

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