Efficient Detection of Cotton Verticillium Wilt by Combining Satellite Time-Series Data and Multiview UAV Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 13547 - 13557

Published: Jan. 1, 2024

As a crucial economic crop, the health status of cotton directly impacts farmers' income and national economy. Therefore, timely accurate detection identification diseases pests are significant importance, aiding in reducing adverse effects on yield quality. Existing research struggles to address balance between resource consumption accuracy disease pest detection. Moreover, often occur beneath canopy, orthorectification drone imagery may result insufficient feature information prolonged processing time, among other issues. To aforementioned issues, this paper proposes precise method for Verticillium wilt based unmanned aerial vehicle multi-angle remote sensing guided by satellite time series monitoring model. Specifically, first, combining Sentinel-1 microwave Sentinel-2 optical images, we constructed model extreme gradient boosting algorithm identify areas affected invasion. Subsequently, after identifying blocks disease, collected multi-spectral data captured from multiple angles vehicles compared different combinations vegetation indices bands. Finally, classification support vector machine radial basis function method. Experimental results indicate that joint time-series achieved OA 81.73% Kappa coefficient 0.63, meeting requirements first stage. Based SVM with RBF optimal band combination, value comprehensive image at -58° angle reached 96.74%, 0.93, second

Language: Английский

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CVW-Etr: A High-Precision Method for Estimating the Severity Level of Cotton Verticillium Wilt Disease

Plants, Journal Year: 2024, Volume and Issue: 13(21), P. 2960 - 2960

Published: Oct. 23, 2024

Cotton verticillium wilt significantly impacts both cotton quality and yield. Selecting disease-resistant varieties using their resistance genes in breeding is an effective economical control measure. Accurate severity estimation of this disease crucial for resistant varieties. However, current methods fall short, slowing the process. To address these challenges, paper introduces CVW-Etr, a high-precision method estimating wilt. CVW-Etr classifies into six levels (L0 to L5) based on proportion segmented diseased leaves lesions. Upon integrating YOLOv8-Seg with MobileSAM, demonstrates excellent performance efficiency limited samples complex field conditions. It incorporates RFCBAMConv, C2f-RFCBAMConv, AWDownSample-Lite, GSegment modules handle blurry transitions between healthy regions variations angle distance during image collection, optimize model's parameter size computational complexity. Our experimental results show that effectively segments lesions, achieving mean average precision (mAP) 92.90% accuracy 92.92% only 2.6M parameters 10.1G FLOPS. Through experiments, proves robust severity, offering valuable insights applications.

Language: Английский

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The authentication of Yanchi tan lamb based on lipidomic combined with particle swarm optimization-back propagation neural network

Food Chemistry X, Journal Year: 2024, Volume and Issue: 24, P. 102031 - 102031

Published: Nov. 22, 2024

Language: Английский

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Application of UAV Multi-spectral Remote Sensing Technology in Vegetation Data Analysis of Power Transmission Project Construction

Published: Dec. 20, 2024

Language: Английский

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