REMOTE SENSING BASED PEST MONITORING INFORMATION SYSTEM
Keywords:
Remote sensing, pest monitoring, precision agriculture, satellite imagery, machine learning, GIS, early warning system, crop health, UAV monitoring, smart farming.Abstract
Pest infestations significantly threaten agricultural productivity and food security worldwide. Conventional monitoring methods based on field observations are often labor-intensive, time-consuming, and limited in spatial coverage, leading to delayed detection and inefficient pest management. This study presents a remote sensing-based pest monitoring information system that integrates multispectral satellite imagery, machine learning algorithms, and geographic information systems (GIS) for early detection and continuous monitoring of pest-induced crop stress. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were extracted from time-series data to identify abnormal changes in crop conditions. Random Forest and Convolutional Neural Network models were applied to improve classification accuracy and spatial mapping of pest distribution. The proposed system provides real-time monitoring and early warning capabilities, enabling timely and targeted pest control interventions. Experimental results demonstrate reliable detection performance and effective spatial visualization, supporting sustainable pest management practices, reducing pesticide use, and enhancing agricultural productivity. The findings highlight the potential of remote sensing technologies to improve decision-making efficiency in precision agriculture.
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