UAV-Based Multispectral Imagery for Estimating Cassava Tuber Yields

被引：0

作者：

Rattanasopa K. ^{[1
]}

Saengprachatanarug K. ^{[1
,2
]}

Wongpichet S. ^{[2
]}

Posom J. ^{[1
,2
]}

Saikaew K. ^{[1
]}

Ungsathittavorn K. ^{[3
]}

Pilawut S. ^{[4
]}

Chinapas A. ^{[1
]}

Taira E. ^{[5
]}

机构：

[1] Faculty of Engineering, Khon Kaen University

[2] Applied Engineering for Important Crops of the North East Research Group, Khon Kaen University

[3] Faculty of Engineering, Rajamangala University of Technology, ISAN Khonkaen Campus

[4] Faculty of Agriculture, University of the Ryukyus

来源：

Engineering in Agriculture, Environment and Food | 2022年 / 15卷 / 01期

关键词：

cassava; multispectral; UAV; yield;

D O I：

10.37221/EAEF.15.1_1

中图分类号：

学科分类号：

摘要：

This experiment studies the feasibility of tuber yield prediction in cassava fields using multispectral imagery based on unmanned arial vehicle (UAV). 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 (CIRedEdge) 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, CIRedEdge, and RVI. © 2022 Elsevier B.V.. All rights reserved.

引用

页码：1 / 12

页数：11

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