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February 14, 2026AgricultureOpen Access

Detection and Precision Application Path Planning for Cotton Spider Mite Based on UAV Multispectral Remote Sensing

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Authors

HZHua ZhuoMYMei YangBWBei Wu

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Overview

Develops UAV multispectral sensing for monitoring and precision spraying of cotton spider mites, suggesting enhanced pest control efficiency.

Key Points

  • The research aims to improve detection and spraying efficiency for cotton spider mites using UAV multispectral remote sensing and optimized path planning.
  • Utilized UAV-mounted cameras to acquire multispectral imagery from cotton fields.
  • Evaluated vegetation indices through feature optimization for pest detection.
  • Compared performance of various machine learning and deep learning models for classification accuracy.
  • Proposed an improved NSGA-III algorithm for multi-objective path optimization in pesticide spraying.
  • Validated the model's effectiveness through field tests and ablation studies.
  • Random Forest model achieved 85.47% accuracy and an AUC of 0.912 for spider mite detection.
  • Improved path optimization algorithm reduced IGD by 59.94% and increased HV by 5.90% compared to standard NSGA-III.
  • Field tests demonstrated 98.5% coverage of infested areas with minimal path repetition of 3.6%.

Cite This Study

Zhuo et al. (2026) studied this question.

synapsesocial.com/papers/699010ce2ccff479cfe570e6https://doi.org/10.3390/agriculture16040424
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Assessing the Severity of Verticillium Wilt in Cotton Fields and Constructing Pesticide Application Prescription Maps Using Unmanned Aerial Vehicle (UAV) Multispectral Images2024 · 23 citations
  2. 2Remote Sensing and AI-Driven Sustainable Cotton Farming for a Resilient Future2026
  3. 3Optimizing pesticide delivery in cotton: impact of unmanned aerial spraying system configurations on canopy deposition and implications for pest control efficacy2026
  4. 4AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application2024 · 5 citations
  5. 5Effect of UAV operational parameters on target and non-target spray deposition and efficacy of spider mites and thrips control in cotton2026