Design and development of unmaned aerial vehicle for capturing normalized difference vegetation index (NDVI) / Mark Angelo B. Bañaria, [and 5 others].

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Dissertation note: Undergraduate Theses (BSEE) - Electrical Engineering. Camarines Sur Polytechnic Colleges, 2024. Abstract: This study, titled "Design and Development of Unmanned Aerial Vehicle for Capturing Normalized Difference Vegetation Index," aimed to assist the agricultural sector by developing a UAV equipped with a multispectral imaging system. The researchers designed and fabricated a UAV capable of capturing NDVI images to monitor crop health, detect plant stress, diseases, and nutrient deficiencies across large agricultural areas. Applying Remote Sensing Theory, Vegetation Indices Theory, and Agricultural Management Theory, the researchers employed descriptive and developmental methods during the planning, design, fabrication, and testing phases. A parafoil-based UAV platform integrated with a multispectral camera, Raspberry Pi, and NDVI-processing algorithms was successfully developed. Field tests demonstrated the UAV's ability to capture high-resolution images and generate accurate NDVI data, providing farmers with real-time crop health assessments. Although challenges were encountered in maneuvering and landing, the UAV consistently achieved successful flights and effective NDVI image acquisition. The study concludes that the UAV can significantly support precision agriculture, improve productivity, and recommends further enhancements in flight control and battery efficiency for broader applications.
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Unpublished Materials Unpublished Materials Main Library Undergraduate Thesis Section UTH BSEE B111d 2024 (Browse shelf(Opens below)) 1-1 Not for loan 006877UM

Undergraduate Theses (BSEE) - Electrical Engineering. Camarines Sur Polytechnic Colleges, 2024.

Includes bibliographical references.

This study, titled "Design and Development of Unmanned Aerial Vehicle for Capturing Normalized Difference Vegetation Index," aimed to assist the agricultural sector by developing a UAV equipped with a multispectral imaging system. The researchers designed and fabricated a UAV capable of capturing NDVI images to monitor crop health, detect plant stress, diseases, and nutrient deficiencies across large agricultural areas. Applying Remote Sensing Theory, Vegetation Indices Theory, and Agricultural Management Theory, the researchers employed descriptive and developmental methods during the planning, design, fabrication, and testing phases. A parafoil-based UAV platform integrated with a multispectral camera, Raspberry Pi, and NDVI-processing algorithms was successfully developed. Field tests demonstrated the UAV's ability to capture high-resolution images and generate accurate NDVI data, providing farmers with real-time crop health assessments. Although challenges were encountered in maneuvering and landing, the UAV consistently achieved successful flights and effective NDVI image acquisition. The study concludes that the UAV can significantly support precision agriculture, improve productivity, and recommends further enhancements in flight control and battery efficiency for broader applications.

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