Estimation of the Foliage Growth in Precision Viticulture from 3D Radar Proximal Sensing

2020 ◽  
Author(s):  
Dominique Henry ◽  
Herve Aubert ◽  
Thierry Veronese
Keyword(s):  
Proximal Sensing ◽  
Precision Viticulture

scholarly journals State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 201
Author(s):  
Marco Ammoniaci ◽  
Simon-Paolo Kartsiotis ◽  
Rita Perria ◽  
Paolo Storchi
Keyword(s):  
New Technologies ◽  
State Of The Art ◽  
Variable Rate ◽  
Proximal Sensing ◽  
Use Of Resources ◽  
Yield And Quality ◽  
Monitoring Tools ◽  
Precision Viticulture ◽  
Monitoring Technologies ◽  
The Right

Precision viticulture (PV) aims to optimize vineyard management, reducing the use of resources, the environmental impact and maximizing the yield and quality of the production. New technologies as UAVs, satellites, proximal sensors and variable rate machines (VRT) are being developed and used more and more frequently in recent years thanks also to informatics systems able to read, analyze and process a huge number of data in order to give the winegrowers a decision support system (DSS) for making better decisions at the right place and time. This review presents a brief state of the art of precision viticulture technologies, focusing on monitoring tools, i.e., remote/proximal sensing, variable rate machines, robotics, DSS and the wireless sensor network.

scholarly journals Data mining and non-invasive proximal sensing for precision viticulture

2015 ◽  
Author(s):  
Maria Diago ◽  
Salvador Gutiérrez ◽  
Javier Tardaguila ◽  
Juan Fernández-Novales
Keyword(s):  
Data Mining ◽  
Proximal Sensing ◽  
Non Invasive ◽  
Precision Viticulture

scholarly journals Remote Sensing Vegetation Indices in Viticulture: A Critical Review

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 457
Author(s):  
Rigas Giovos ◽  
Dimitrios Tassopoulos ◽  
Dionissios Kalivas ◽  
Nestor Lougkos ◽  
Anastasia Priovolou
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Proximal Sensing ◽  
Spectral Bands ◽  
Sensing Platforms ◽  
Precision Viticulture ◽  
Remote Sensing Methods

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

scholarly journals The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4550
Author(s):  
Huajian Liu ◽  
Brooke Bruning ◽  
Trevor Garnett ◽  
Bettina Berger
Keyword(s):  
Hyperspectral Imaging ◽  
High Throughput ◽  
Management Practices ◽  
Leaf Tissue ◽  
Plant Phenotyping ◽  
Proximal Sensing ◽  
N Content ◽  
Hyperspectral Sensors ◽  
Wheat Leaves ◽  
Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

PRECISION VITICULTURE - AN AUSTRALIAN PERSPECTIVE

2004 ◽  
pp. 15-25 ◽  
Author(s):  
D.W. Lamb ◽  
R.G.V. Bramley ◽  
A. Hall
Keyword(s):  
Precision Viticulture

scholarly journals Novel Proximal Sensing for Monitoring Soil Organic C Stocks and Condition

2017 ◽  
Vol 51 (10) ◽  
pp. 5630-5641 ◽  
Author(s):  
Raphael A. Viscarra Rossel ◽  
Craig R. Lobsey ◽  
Chris Sharman ◽  
Paul Flick ◽  
Gordon McLachlan
Keyword(s):  
Soil Organic C ◽  
Proximal Sensing ◽  
Organic C ◽  
C Stocks

scholarly journals Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1409
Author(s):  
Nicholas Todd Anderson ◽  
Kerry Brian Walsh ◽  
Dvoralai Wulfsohn
Keyword(s):  
Machine Vision ◽  
Structural Parameters ◽  
Qualitative Assessment ◽  
Sampling Strategies ◽  
Yield Data ◽  
Proximal Sensing ◽  
Fruit Number ◽  
Fruit Maturity ◽  
Harvest Timing ◽  
Fruit Load

The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

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