UAV-Based Multispectral Imagery for Estimating Cassava Tuber Yields

Abstract

This experiment studies the feasibility of tuber yield prediction in cassava fields using multispectral imagery based on unmanned arial vehicle. The imageries of a cassava field were taken monthly, four times. The cassava’s height, normalized difference vegetation index (NDVI), simple ratio vegetation index (RVI), and chlorophyll vegetation index (CI_RedEdge) were calculated. Yield models were developed using Simple linear regression with vegetation indices (VIs), canopy area, and average height from 3 methods: excluded soil pixels (1), zero soil pixels (2), and included soil pixels (3). The results show the average height and canopy area from method (1) provides the highest R² 0.87 and 0.65. VIs values from method (3) gives R² 0.58, 0.57, and 0.50 for NDVI, CI_RedEdge, and RVI.