UAV remote sensing of spatial variation in banana production

Brian L. Machovina, Kenneth Feeley, Brett J. Machovina

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Remote sensing through Unmanned Aerial Vehicles (UAV) can potentially be used to identify the factors influencing agricultural yield and thereby increase production efficiency. The use of UAV remains largely underutilised in tropical agricultural systems. In this study we tested a fixed-wing UAV system equipped with a sensor system for mapping spatial patterns of photosynthetic activity in banana plantations in Costa Rica. Spatial patterns derived from the Normalised Difference Vegetation Index (NDVI) were compared with spatial patterns of physical soil quality and banana fruit production data. We found spatial patterns of NDVI were significantly positively correlated with spatial patterns of several metrics of fruit yield and quality: bunch weight, number of hands per bunch, length of largest finger, and yield. NDVI was significantly negatively correlated with banana loss (discarded due to low quality). Spatial patterns of NDVI were not correlated with spatial patterns of physical soil quality. These results indicate that UAV systems can be used in banana plantations to help map patterns of fruit quality and yield, potentially aiding investigations of spatial patterns of underlying factors affecting production and thereby helping to increase agricultural efficiency.

Original languageEnglish (US)
Pages (from-to)1281-1287
Number of pages7
JournalCrop and Pasture Science
Volume67
Issue number12
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

Fingerprint

bananas
remote sensing
spatial variation
fruit yield
fruit quality
soil quality
plantations
production economics
fruit growing
Costa Rica
sensors (equipment)
hands
unmanned aerial vehicles
normalized difference vegetation index

Keywords

  • crop productivity
  • Musa
  • NDVI

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Plant Science

Cite this

UAV remote sensing of spatial variation in banana production. / Machovina, Brian L.; Feeley, Kenneth; Machovina, Brett J.

In: Crop and Pasture Science, Vol. 67, No. 12, 01.01.2016, p. 1281-1287.

Research output: Contribution to journalArticle

Machovina, Brian L. ; Feeley, Kenneth ; Machovina, Brett J. / UAV remote sensing of spatial variation in banana production. In: Crop and Pasture Science. 2016 ; Vol. 67, No. 12. pp. 1281-1287.
@article{e49a4ecfd0644c5da09188304bfc5a6b,
title = "UAV remote sensing of spatial variation in banana production",
abstract = "Remote sensing through Unmanned Aerial Vehicles (UAV) can potentially be used to identify the factors influencing agricultural yield and thereby increase production efficiency. The use of UAV remains largely underutilised in tropical agricultural systems. In this study we tested a fixed-wing UAV system equipped with a sensor system for mapping spatial patterns of photosynthetic activity in banana plantations in Costa Rica. Spatial patterns derived from the Normalised Difference Vegetation Index (NDVI) were compared with spatial patterns of physical soil quality and banana fruit production data. We found spatial patterns of NDVI were significantly positively correlated with spatial patterns of several metrics of fruit yield and quality: bunch weight, number of hands per bunch, length of largest finger, and yield. NDVI was significantly negatively correlated with banana loss (discarded due to low quality). Spatial patterns of NDVI were not correlated with spatial patterns of physical soil quality. These results indicate that UAV systems can be used in banana plantations to help map patterns of fruit quality and yield, potentially aiding investigations of spatial patterns of underlying factors affecting production and thereby helping to increase agricultural efficiency.",
keywords = "crop productivity, Musa, NDVI",
author = "Machovina, {Brian L.} and Kenneth Feeley and Machovina, {Brett J.}",
year = "2016",
month = "1",
day = "1",
doi = "10.1071/CP16135",
language = "English (US)",
volume = "67",
pages = "1281--1287",
journal = "Crop and Pasture Science",
issn = "1836-0947",
publisher = "CSIRO",
number = "12",

}

TY - JOUR

T1 - UAV remote sensing of spatial variation in banana production

AU - Machovina, Brian L.

AU - Feeley, Kenneth

AU - Machovina, Brett J.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Remote sensing through Unmanned Aerial Vehicles (UAV) can potentially be used to identify the factors influencing agricultural yield and thereby increase production efficiency. The use of UAV remains largely underutilised in tropical agricultural systems. In this study we tested a fixed-wing UAV system equipped with a sensor system for mapping spatial patterns of photosynthetic activity in banana plantations in Costa Rica. Spatial patterns derived from the Normalised Difference Vegetation Index (NDVI) were compared with spatial patterns of physical soil quality and banana fruit production data. We found spatial patterns of NDVI were significantly positively correlated with spatial patterns of several metrics of fruit yield and quality: bunch weight, number of hands per bunch, length of largest finger, and yield. NDVI was significantly negatively correlated with banana loss (discarded due to low quality). Spatial patterns of NDVI were not correlated with spatial patterns of physical soil quality. These results indicate that UAV systems can be used in banana plantations to help map patterns of fruit quality and yield, potentially aiding investigations of spatial patterns of underlying factors affecting production and thereby helping to increase agricultural efficiency.

AB - Remote sensing through Unmanned Aerial Vehicles (UAV) can potentially be used to identify the factors influencing agricultural yield and thereby increase production efficiency. The use of UAV remains largely underutilised in tropical agricultural systems. In this study we tested a fixed-wing UAV system equipped with a sensor system for mapping spatial patterns of photosynthetic activity in banana plantations in Costa Rica. Spatial patterns derived from the Normalised Difference Vegetation Index (NDVI) were compared with spatial patterns of physical soil quality and banana fruit production data. We found spatial patterns of NDVI were significantly positively correlated with spatial patterns of several metrics of fruit yield and quality: bunch weight, number of hands per bunch, length of largest finger, and yield. NDVI was significantly negatively correlated with banana loss (discarded due to low quality). Spatial patterns of NDVI were not correlated with spatial patterns of physical soil quality. These results indicate that UAV systems can be used in banana plantations to help map patterns of fruit quality and yield, potentially aiding investigations of spatial patterns of underlying factors affecting production and thereby helping to increase agricultural efficiency.

KW - crop productivity

KW - Musa

KW - NDVI

UR - http://www.scopus.com/inward/record.url?scp=85006905529&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006905529&partnerID=8YFLogxK

U2 - 10.1071/CP16135

DO - 10.1071/CP16135

M3 - Article

AN - SCOPUS:85006905529

VL - 67

SP - 1281

EP - 1287

JO - Crop and Pasture Science

JF - Crop and Pasture Science

SN - 1836-0947

IS - 12

ER -