scholarly journals Within-field variability estimation based on variogram analysis of satellite data for precision agriculture

2020 ◽  
Vol 17 (2) ◽  
pp. 114-122
Author(s):  
V.P. Yakushev ◽  
◽  
V.M. Bure ◽  
O.A. Mitrofanova ◽  
E.P. Mitrofanov ◽  
...  
Keyword(s):  
Satellite Data ◽  
Precision Agriculture ◽  
Variogram Analysis ◽  
Field Variability

scholarly journals Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy conditions: Part 2 - image restoration with HICO satellite data in overcast conditions

2021 ◽  
Author(s):  
Joanna Joiner ◽  
Zachary Fasnacht ◽  
Bo-Cai Gao ◽  
Wenhan Qin
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Land Surface ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Space Station ◽  
Water Quality Assessment ◽  
Low Contrast

Satellite-based visible and near-infrared imaging of the Earth's surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using multi-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion ofthe panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment as well as disturbance, change, hazard, and disaster detection.

Introduction to Precision Agriculture

2021 ◽  
pp. 1-22
Author(s):  
Akalpita Tendulkar
Keyword(s):  
Precision Agriculture ◽  
Fertilizer Application ◽  
Farm Management ◽  
Global Positioning ◽  
Soil Variation ◽  
On Farm ◽  
Field Variability ◽  
Global Population ◽  
The Internet Of Things ◽  
Yield Monitoring

The global population is increasing at a tremendous speed; thus, the demand for safe and secure food to meet this population is in demand. Therefore, traditional farming methods are insufficient to meet this demand; thus, the next revolution in agriculture is required, which is Precision Agriculture (PA), the Fourth Agriculture Revolution. PA is a technology where the concept of farm management is based on observation, measuring, and responding to inter- and intra-field variability in crops. The technologies used for performing precision agriculture are mapping, global positioning system (GPS), yield monitoring and mapping, grid soil sampling application, variable-rate fertilizer application, remote sensing, geographic information systems (GIS), quantifying on farm variability, soil variation, variability of soil water content, time and space scales, robots, drones, satellite imagery, the internet of things, smartphone, and machine learning. Hence, the current chapter will be emphasizing the overview, concepts, history, world interest, benefits, disadvantages, and precision farming needs.

Field variability and vulnerability index to identify regional precision agriculture opportunity

2017 ◽  
Vol 19 (4) ◽  
pp. 589-605 ◽  
Author(s):  
Christopher W. Bobryk ◽  
Matt A. Yost ◽  
Newell R. Kitchen
Keyword(s):  
Precision Agriculture ◽  
Vulnerability Index ◽  
Field Variability

scholarly journals The accuracy of farm machinery for precision agriculture: a case for fertilizer application

1997 ◽  
Vol 45 (1) ◽  
pp. 199-215 ◽  
Author(s):  
D. Goense
Keyword(s):  
Precision Agriculture ◽  
Fertilizer Application ◽  
Positioning Accuracy ◽  
Site Specific ◽  
Positioning Systems ◽  
Application Rates ◽  
Small Influence ◽  
Variance Calculation ◽  
Field Variability ◽  
Different Levels

Work quality, capacity and reliability are important criteria for design and evaluation of farm equipment. With the introduction of precision agriculture, the ability to adapt to spatially variable soil and crop conditions, becomes an additional aspect. A calculation method was developed to find the precision of site specific fertilizer application. The variance between the required rate, RR, and the applied rate, AR, was used as a measure for precision. The theory of geo-statistics was used for variance calculation. Spreading patterns were evaluated for different levels of field variability, positioning accuracy and resolution of the required application rates. The shape of spreading patterns had small influence. The effect of the accuracy of positioning systems was dependent on the resolution of the required application rates and of the working width of independently controlled sections of the spreaders.

scholarly journals PRECISION VITICULTURE FROM MULTITEMPORAL, MULTISPECTRAL VERY HIGH RESOLUTION SATELLITE DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 919-925
Author(s):  
Z. Kandylakis ◽  
K. Karantzalos
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Quality Parameters ◽  
Total Phenolic ◽  
Canopy Reflectance ◽  
Northern Greece ◽  
In Situ Data ◽  
Very High

In order to exploit efficiently very high resolution satellite multispectral data for precision agriculture applications, validated methodologies should be established which link the observed reflectance spectra with certain crop/plant/fruit biophysical and biochemical quality parameters. To this end, based on concurrent satellite and field campaigns during the veraison period, satellite and in-situ data were collected, along with several grape samples, at specific locations during the harvesting period. These data were collected for a period of three years in two viticultural areas in Northern Greece. After the required data pre-processing, canopy reflectance observations, through the combination of several vegetation indices were correlated with the quantitative results from the grape/must analysis of grape sampling. Results appear quite promising, indicating that certain key quality parameters (like brix levels, total phenolic content, brix to total acidity, anthocyanin levels) which describe the oenological potential, phenolic composition and chromatic characteristics can be efficiently estimated from the satellite data.

Conservative Precision Agriculture: an assessment of technical feasibility and energy efficiency within the LIFE+ AGRICARE project

2017 ◽  
Vol 8 (2) ◽  
pp. 439-443 ◽  
Author(s):  
Donato Cillis ◽  
Andrea Pezzuolo ◽  
Francesco Marinello ◽  
Bruno Basso ◽  
Nicola Colonna ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Precision Agriculture ◽  
Environmental Sustainability ◽  
Conservation Tillage ◽  
Conservation Agriculture ◽  
Conventional Tillage ◽  
Precision Farming ◽  
Technical Feasibility ◽  
Temporal Field ◽  
Field Variability

The integration of conservation agriculture with the benefits of precision farming represents an innovative feature aimed to achieve better economic and environmental sustainability. The synergy between these principles was assessed through a technical feasibility and energy efficiency to define the best approach depending on different agricultural systems, spatial and temporal field variability. The study compares three conservation tillage techniques supported by precision farming with conventional tillage in a specific crop rotation: wheat, rapeseed, corn and soybean. The preliminary results show a positive response of precision farming in all the conservation tillage systems, increasing yields until 22%. The energy efficiency achieves highest level in those techniques supported by precision farming, gaining peak of 9% compared to conventional tillage.

scholarly journals EVALUATION OF RGB-BASED VEGETATION INDICES FROM UAV IMAGERY TO ESTIMATE FORAGE YIELD IN GRASSLAND

2018 ◽  
Vol XLII-3 ◽  
pp. 1215-1219 ◽  
Author(s):  
U. Lussem ◽  
A. Bolten ◽  
M. L. Gnyp ◽  
J. Jasper ◽  
G. Bareth
Keyword(s):  
Precision Agriculture ◽  
Vegetation Indices ◽  
Fertilizer Application ◽  
Forage Yield ◽  
Accurate Information ◽  
Fertilizer Treatment ◽  
Management Decisions ◽  
Fresh Matter ◽  
Negative Impacts ◽  
Field Variability

Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R<sup>2</sup> values of 0.62.

WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time

2021 ◽  
pp. 243-254
Author(s):  
Balakrishna K.
Keyword(s):  
Wireless Sensor Networks ◽  
Precision Agriculture ◽  
Wireless Sensor ◽  
The Internet ◽  
Communication Model ◽  
Climatic Parameters ◽  
Statistical Software ◽  
Irrigation Optimization ◽  
Field Variability ◽  
The Internet Of Things

The use of wireless sensor networks, the internet of things, and advanced technologies lead to new direction of research in the agriculture domain called prescriptive agriculture. Prescriptive agriculture is the enforcement of precision agriculture, which is observing, measuring, and responding to inter and intra field variability of farm field. In this chapter, the advent of wireless sensor network, APSim, and communication model spurred a new direction in the farming domain at optimizing irrigation. Sensors are programmed to collect the datasets of climatic parameters such as relative humidity and temperature, where the datasets were forwarded to the server through a GSM module. Datasets collected were analyzed through statistical software for grown crops by considering inter and intra farm field conditions. Finally, information on irrigation is decimated through an algorithm designed by way2SMS and WebHost server.

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