Detection of Vegetation Areas Attacked By Pests and Diseases Based on Adaptively Weighted Enhanced Global and Local Deep Features

Dai, Yanshuai; Shen, Li; Lei, Tianjie; Qiao, Wenfan

doi:10.1109/igarss.2019.8898517

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Utilizing CNNs, VHR aerial images (10 cm) were used to detect pine wood nematodes [29]. Vegetation regions infested with numerous pests and diseases were also discovered using VHR-UAV (0.5 m) images [30]. Duarte [31] used UAVs to detect and quantify eucalyptus longhorned borer damage to eucalyptus trees.…”

Section: Introductionmentioning

confidence: 99%

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Al-Mulla,

Ali,

Parimi

2023

Sustainability

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Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.

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Section: Introductionmentioning

confidence: 99%

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Al-Mulla,

Ali,

Parimi

2023

Sustainability

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“…Remote sensing image (RSI) target detection is one of the hot-research problems in the (RSI) interpretation field, which relates to many essential and fundamental applications in both military and civilian fields [1][2][3]. In the specific applications, remote sensing target detection can be applied in ship location [4], vehicle counting [5], disaster rescue [6], target attacks [7], warfare analysis [8], and so on. However, compared with the ordinary optical images, remote sensing images have the characteristics of complex background, high target density, small target size, and large feature similarity between different categories targets [9,10], which pose many challenges to the accurate detection and localization of different target in remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

FESSD: Feature Enhancement Single Shot MultiBox Detector Algorithm for Remote Sensing Image Target Detection

Guo¹,

Wang²,

Zhang³

2022

Preprint

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Automatic target detection of remote sensing images (RSI) plays an important role in military reconnaissance, disaster monitoring, and target rescue. The core task of remote sensing target detection is to judge the target categories and complete precise location. However, the existing target detection algorithms have limited accuracy and weak generalization capability for remote sensing images with complex backgrounds. To achieve accurate detection of different categories targets in remote sensing images, this study presents a novel feature enhancement single shot multibox detector (FESSD) algorithm for remote sensing target detection. The FESSD introduces feature enhancement module and attention mechanism into the convolution neural networks (CNN) model, which can effectively enhance the feature extraction ability and nonlinear relationship between different convolution features. Specifically, the feature enhancement module is used to extract the multi-scale feature information, and enhance the model nonlinear learning ability; the self-learning attention mechanism (SAM) is used to expand the convolution kernel local receptive field, which makes the model extract more valuable features. In addition, the nonlinear relationship between different convolution features is enhanced using the feature pyramid attention mechanism (PAM). The advantage of FESSD over other state-of-the-art target detection methods is validated by experiments on the presented seven-class target detection dataset (SD-RSI) and the public DIOR dataset.

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Plant Stresses and Pest Attack Detection Using Convolutional Neural Networks with Advance Preprocessing Techniques in Chili Plants

Jayasuriya

Kotapolage

Gamage

et al. 2021

Lecture Notes in Networks and Systems

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Detection of Vegetation Areas Attacked By Pests and Diseases Based on Adaptively Weighted Enhanced Global and Local Deep Features

Cited by 7 publications

References 4 publications

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

Detection and Analysis of Dubas-Infested Date Palm Trees Using Deep Learning, Remote Sensing, and GIS Techniques in Wadi Bani Kharus

FESSD: Feature Enhancement Single Shot MultiBox Detector Algorithm for Remote Sensing Image Target Detection

Plant Stresses and Pest Attack Detection Using Convolutional Neural Networks with Advance Preprocessing Techniques in Chili Plants

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