Drone-Based Remote Sensing for Crop Health Monitoring in Tropical Agriculture

Asamoah Oppong Zadok1*, & Kofi Afari2
1Department of Agricultural Economics & Extension, University of Cape Coast, Cape Coast, Ghana
2Forest and Horticultural Crops Research Center, CBAS, University of Ghana, Kade, Ghana.

Abstract

Crop health monitoring is a critical component of agricultural productivity in tropical regions, where high rainfall variability, pest pressure, and rapid crop growth cycles complicate field-based assessment. Traditional crop monitoring methods, including manual scouting and satellite-based observation, often face limitations related to cost, spatial resolution, cloud cover, and timeliness. In response to these challenges, drone-based remote sensing has emerged as a complementary approach for capturing high-resolution, field-level data suitable for crop health assessment. This paper presents a structured synthesis of existing research on drone-based remote sensing for crop health monitoring in tropical agriculture. The review examines how unmanned aerial vehicles have been used to assess crop vigor, stress, and disease through multispectral, thermal, and RGB imaging. Particular attention is given to the types of sensors employed, vegetation indices applied, and analytical methods used to interpret crop health indicators under tropical conditions. The reviewed studies indicate that drone-based remote sensing can support early detection of crop stress, spatial variability analysis, and targeted field management when implemented under appropriate operational conditions. However, adoption in tropical agriculture remains constrained by regulatory barriers, operational costs, data processing complexity, and environmental factors such as cloud cover and high humidity. By synthesizing existing evidence through a tropical-specific lens, this review clarifies the practical conditions under which drone-based remote sensing can support crop health monitoring beyond experimental settings. The paper identifies key limitations in existing studies and highlights research gaps related to scalability, validation under farmer-managed conditions, and integration with routine farm decision-making in tropical systems.

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