Deep learning approach to classify Tiger beetles of Sri Lanka

Abeywardhana, D. L.; Dangalle, C. D.; Nugaliyadde, Anupiya; Mallawarachchi, Yashas

doi:10.1016/j.ecoinf.2021.101286

Cited by 10 publications

(11 citation statements)

References 27 publications

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“…Combining web sources and on-site images could be more effective. When the number of photographs is insufficient, this approach requires more time and resources than using web sources only [14]- [16]. According to Takimoto et al [14], field collection took two years (2017 and 2018).…”

Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

“…According to Takimoto et al [14], field collection took two years (2017 and 2018). While Hossain et al [15] and Abeywardhana et al [16] do not specify how much time has passed, Takimoto et al [14] mentioned the use of a RICOH WG-4 digital camera alongside the Google search engine. However, Hossain et al [15] utilized a mobile phone to shoot images in the field along with the Google search engine.…”

Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

“…However, Hossain et al [15] utilized a mobile phone to shoot images in the field along with the Google search engine. The web source is not mentioned by Abeywardhana et al [16], although a digital camera and a mobile phone were used.…”

Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

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A Systematic Literature Review on Insect Detection in Images

Souifi¹,

Mdhaffar²,

Rodriguez³

et al. 2022

Signal, Image Processing and Embedded Systems Trends

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Due to the advancements of deep learning (DL), particularly in the areas of visual object detection and convolutional neural networks (CNN), insect detecetion in images has received a lot of attention from the research community in the last few years. This paper presents a systematic review of the literature on the topic of insect detection as a case of object detection in images. It covers 50 research papers on the subject and responds to three research questions: i) type of dataset used; ii) detection technique used; iii) insect location. The paper also provides a summary of existing methods used for insect detection.

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Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

Section: Combination Of Web Sources and On-site Imagesmentioning

confidence: 99%

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A Systematic Literature Review on Insect Detection in Images

Souifi¹,

Mdhaffar²,

Rodriguez³

et al. 2022

Signal, Image Processing and Embedded Systems Trends

View full text Add to dashboard Cite

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“…However, recent advances in deep learning techniques have made it possible to efficiently recognize diseases and pests in complex disease symptoms, and such techniques are being successfully applied in many fields [2]. Recently, machine and deep learning techniques have been applied in many studies on disease and pest recognition methods, among which deep convolutional neural network (DCNN) algorithms have shown a good performance [1,[3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears

Yin

Dong

et al. 2022

Agriculture

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Plant diseases are a major concern in the agricultural sector; accordingly, it is very important to identify them automatically. In this study, we propose an improved deep learning-based multi-plant disease recognition method that combines deep features extracted by deep convolutional neural networks and k-nearest neighbors to output similar disease images via query image. Powerful, deep features were leveraged by applying fine-tuning, an existing method. We used 14,304 in-field images with six diseases occurring in apples and pears. As a result of the experiment, the proposed method had a 14.98% higher average similarity accuracy than the baseline method. Furthermore, the deep feature dimensions were reduced, and the image processing time was shorter (0.071–0.077 s) using the proposed 128-sized deep feature-based model, which processes images faster, even for large-scale datasets. These results confirm that the proposed deep learning-based multi-plant disease recognition method improves both the accuracy and speed when compared to the baseline method.

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“…Tracking was implemented to reduce the potential for counting moths more than once as they walked around on the light trap. A DNN model cause Squeezenet was developed for the detection of tiger beetles [10]. They found that freezing some of the model layers during training improved accuracy to 90%.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application

et al. 2022

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In this study, we have compared YOLOv4, a single-shot detector to Faster-RCNN, a two-shot detector to detect and classify whiteflies on yellow-sticky tape (YST). An IoT remote whitefly monitoring station was developed and placed in a whitefly rearing room. Images of whiteflies attracted to the trap were recorded 2× per day. A total of 120 whitefly images were labeled using labeling software and split into a training and testing dataset, and 18 additional yellow-stick tape images were labeled with false positives to increase the model accuracy from remote whitefly monitors in the field that created false positives due to water beads and reflective light on the tape after rain. The two-shot detection model has two stages: region proposal and then classification of those regions and refinement of the location prediction. Single-shot detection skips the region proposal stage and yields final localization and content prediction at once. Because of this difference, YOLOv4 is faster but less accurate than Faster-RCNN. From the results of our study, it is clear that Faster-RCNN (precision—95.08%, F-1 Score—0.96, recall—98.69%) achieved a higher level of performance than YOLOv4 (precision—71.77%, F-1 score—0.83, recall—73.31%), and will be adopted for further development of the monitoring station.

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Deep learning approach to classify Tiger beetles of Sri Lanka

Cited by 10 publications

References 27 publications

A Systematic Literature Review on Insect Detection in Images

A Systematic Literature Review on Insect Detection in Images

Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears

Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application

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