Current Status and Applications of Remote Sensing in Plant Pest and Disease Detection

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 72

Special Issue Editors


E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: unmanned farm information perception and equipment; crop remote sensing and non-destructive testing; intelligent paddy field agricultural equipment
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Horticulture, University of Georgia, 1111 Plant Sciences Building, Athens, GA 30602, USA
Interests: controlled-environment agriculture (CEA); plant phenotyping; indoor remote sensing; hyperspectral imaging; interpretable machine learning; imagery data mining
School of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: plant phenotyping; hyperspectral imaging; precision agriculture; plant sensor

Special Issue Information

Dear Colleagues,

Plant pests and diseases threaten agriculture by reducing yields, degrading food quality, and causing economic losses, jeopardizing global food security. Early detection and management are crucial for sustaining crop production. Remote sensing, with its efficiency, non-destructive nature, and real-time capabilities, is a powerful tool for monitoring plant health, offering advantages in accuracy, scale, and efficiency. Widely applied in agriculture, it enhances disease detection and management. Based on the above, we are pleased to announce a Special Issue of Agronomy on “Current Status and Applications of Remote Sensing in Plant Pest and Disease Detection”, which will focus on the following:

  • The current state of remote sensing technologies in detecting plant pests and diseases;
  • The integration of multispectral, hyperspectral, and thermal imaging techniques for accurate pest and disease identification;
  • Innovative applications of remote sensing in crop health monitoring across various agricultural environments;
  • Challenges in achieving reliable pest and disease detection under diverse field conditions.

Prof. Dr. Jinfeng Wang
Dr. Zhihang Song
Dr. Jian Jin
Guest Editors

Manuscript Submission Information

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Keywords

  • remote sensing
  • plant pests
  • plant diseases
  • crop health monitoring
  • hyperspectral imaging
  • multispectral imaging
  • precision agriculture
  • early detection
  • machine learning
  • pest and disease management

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