Work

Yield Estimation of Major Crops in Butuan City, Agusan del Norte Using Geospatial Techniques

Year: 2024

Type: conference-paper

Abstract: This study uses LiDAR derivatives and Landsat 8 OLI/TIRS for yield estimation of major crops in Butuan City, Agusan del Norte, Philippines.Yield estimation is crucial for crop management, food security, and economic impact.Remote sensing techniques and GIS were used for crop yield estimation. eCognition software classified photos, while LiDAR derivatives like Canopy Height Model (CHM), Digital Surface Model (DSM), and Normalized Digital Surface Model (nDSM) were produced.Vegetation objects were classified into High, Medium, and Low Elevation Groups based on LiDAR nDSM heights.Regression analysis developed the allometric equation for yield estimation.The study achieved 94.36% overall accuracy in map classification and yield estimations of 84.5% for banana, 87.9% for coconut, 72.5% for corn, and 86.4% for mango.Average production was 58.414 tons/ha for bananas, 1,001.15tons/ha for coconut, 8.48 tons/ha for corn, and 1.256 tons/ha for mango.

Source: Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES)

DOI: 10.5109/7323281

Authors: Edgardo Ricardo B. Sajonia Jr, Anamarie P. Sajonia

Institutions: Caraga State University, Combustion Research and Flow Technology (United States)

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Life on land

Open Access status: hybrid

OpenAlex ID: W4404653661