Next Article in Journal
Data-Driven Multiresolution Camera Using the Foveal Adaptive Pyramid
Next Article in Special Issue
Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products
Previous Article in Journal
Optical Microbubble Resonators with High Refractive Index Inner Coating for Bio-Sensing Applications: An Analytical Approach
Previous Article in Special Issue
A Data Transfer Fusion Method for Discriminating Similar Spectral Classes
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(12), 2004; doi:10.3390/s16122004

Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App

1
Department of Agricultural and Environmental Sciences—Production, Land, Agrienergy, Università degli Studi di Milano, via Celoria 2, I-20133 Milan, Italy
2
Cassandra Lab, Università degli Studi di Milano, via Celoria 2, I-20133 Milan, Italy
3
Department of Economics, Management, and Quantitative Methods, Università degli Studi di Milano, via Celoria 2, I-20133 Milan, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Lammert Kooistra
Received: 13 September 2016 / Revised: 17 November 2016 / Accepted: 18 November 2016 / Published: 26 November 2016
(This article belongs to the Special Issue Precision Agriculture and Remote Sensing Data Fusion)
View Full-Text   |   Download PDF [1265 KB, uploaded 26 November 2016]   |  

Abstract

Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six surveys in an experimental site characterized by a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values. During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates (smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct measurements, especially for LAI ranging from 0.13 to 1.41 (R2 = 0.94, RRMSE = 17.27%), whereas the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included (R2 = 0.77, RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of green pruning. The hemispherical photography showed very high values of R2, even in presence of the outlying value (R2 = 0.94), although it showed a marked and quite constant overestimation error (RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in combination with remote sensing. View Full-Text
Keywords: hemispherical photography; leaf area index; plant vigour; smart-app; Vitis vinifera hemispherical photography; leaf area index; plant vigour; smart-app; Vitis vinifera
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Orlando, F.; Movedi, E.; Coduto, D.; Parisi, S.; Brancadoro, L.; Pagani, V.; Guarneri, T.; Confalonieri, R. Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App. Sensors 2016, 16, 2004.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top