scholarly journals The creation of vegetation indices for the needs of precision agriculture by means of MathCad

2020 ◽  
Vol 11 (2) ◽  
pp. 50-58
Author(s):  
N. A. Pasichnyk ◽  
◽  
V. P. Lysenko ◽  
O. O. Opryshko ◽  
V. O. Miroshnyk ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Vegetation Indices ◽  
The Creation

scholarly journals QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 952
Author(s):  
Lia Duarte ◽  
Ana Cláudia Teodoro ◽  
Joaquim J. Sousa ◽  
Luís Pádua
Keyword(s):  
Open Source ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Data Interpretation ◽  
Vegetation Monitoring ◽  
Distribution Maps ◽  
Monitoring Process ◽  
Multi Temporal ◽  
The Creation ◽  
The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

scholarly journals Evaluation of In-Season Management Zones from High-Resolution Soil and Plant Sensors

Agronomy ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1124
Author(s):  
Martina Corti ◽  
Pietro Marino Gallina ◽  
Daniele Cavalli ◽  
Bianca Ortuani ◽  
Giovanni Cabassi ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Environmental Sustainability ◽  
Cropping Systems ◽  
Stem Elongation ◽  
Vegetation Indices ◽  
Data Sources ◽  
Management Zones ◽  
Operational Conditions ◽  
Wheat Field ◽  
Red Edge

The adoption of precision agriculture has the potential to increase the environmental sustainability of cropping systems as well as farmers’ income. Farmers in transition to precision agriculture need low-input and effective protocols to delineate homogenous management zones to optimize their actions without past knowledge e.g., yield maps. Different approaches have been developed so far, based on the analysis of the within-field variability in crop and soil properties, but procedures were rarely suited for operational conditions. We identified here a low-inputs protocol to map management zones from soil electrical conductivity and/or crop vegetation indices, using a winter wheat field in northern Italy as a pilot case. The reliability of the alternative data sources was evaluated at three crop development stages using a yield map as reference. Red-edge and NIR (NDRE) bands were the most reliable data sources for management zones identification, with 62%, 68%, and 74% of correct classifications at early tillering, stem elongation, and late booting, respectively. Our work identifies a minimum dataset for accurate management zones’ definition and highlights that in-season monitoring based on the red-edge band was able to reliably identify management zones already at early tillering, despite minor differences in crop growth.

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.

scholarly journals Mapeamento Temático da Cobertura Vegetal na Microrregião do Sertão do São Francisco Alagoano, Utilizando Imagens TM LANDSAT 5 (Thematic Mapping of Plant Cover in Microregion Sertão of the San Francisco Alagoas, Images Using TM LANDSAT 5)

2013 ◽  
Vol 5 (5) ◽  
pp. 1121
Author(s):  
Heliofábio Barros Gomes ◽  
Rosiberto S. da S. Junior ◽  
Frederico Tejo De Paci ◽  
Danilo K. C. De Lima ◽  
Pedro H. P. De Castro ◽  
...  
Keyword(s):  
San Francisco ◽  
Precision Agriculture ◽  
Meteorological Data ◽  
Spectral Response ◽  
Plant Cover ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Area Index ◽  
Thematic Mapping ◽  
Density And Biomass

 Atualmente o uso de técnicas de gerenciamento de fazendas utilizando ferramentas de agricultura de precisão vem se tornando cada vez mais comum. Uma dessas ferramentas é a obtenção de informações da resposta espectral dos alvos, cujas aplicações exigem a consideração de vários fatores como a textura do solo e o tipo de vegetação em estudo, pois os mesmos podem influenciar na interpretação dos dados. Os índices de vegetação têm sido muito utilizados no monitoramento de áreas vegetadas, na determinação e estimativa do índice de área foliar, biomassa e da radiação fotossinteticamente ativa. Os índices foram calculados através de etapas do Algoritmo SEBAL (Balanço de Energia da Superfície do Solo), mediante dados de imagens do TM – LANDSAT 5. Os resultados mostraram que ocorreu uma variação na cobertura vegetal da região em estudo, no sentido de alteração negativa da densidade e biomassa. A variação da densidade foi mais acentuada em 2008 do que em 2006 conforme resultados apresentados nos índices estudados. Os resultados obtidos demonstraram que o algoritmo SEBAL teve bom desempenho em escala regional na estimativa dos Índices de Vegetação, com potenciais para serem aplicados em áreas onde a disponibilidade de dados meteorológicos são limitantes.Palavras-chave: NDVI, SAVI, Sensoriamento Remoto. Thematic Mapping of Plant Cover in Microregion Sertão of the San Francisco Alagoas, Images Using TM LANDSAT 5 ABSTRACTCurrently the use of farm management techniques using tools of precision agriculture is becoming increasingly common. One such tool is to obtain information from the spectral response of the targets whose applications require consideration of several factors such as soil texture and type of vegetation in the study, as they may influence the interpretation of data. The vegetation indices have been used in the monitoring of vegetated areas, the determination and estimation of leaf area index, biomass and PAR. Rates were calculated using the algorithm steps of the SEBAL (Surface Energy Balance of Soil) upon image data from TM – LANDSAT 5. The results showed that there was a change in the vegetation of the study area in order to change negative density and biomass. The variation of density was larger in 2008 than in 2006 according to results presented in the indices studied. The results showed that the algorithm SEBAL performed well on a regional scale in the estimation of vegetation indices, with potential for application in areas where the availability of meteorological data are limited.Keywords: NDVI, SAVI, REMOTE SENSING. 

scholarly journals Automatic Estimation of Crop Disease Severity Levels Based on Vegetation Index Normalization

2020 ◽  
Vol 12 (12) ◽  
pp. 1930 ◽  
Author(s):  
Hengqian Zhao ◽  
Chenghai Yang ◽  
Wei Guo ◽  
Lifu Zhang ◽  
Dongyan Zhang
Keyword(s):  
Disease Severity ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Hyperspectral Imagery ◽  
Vegetation Indices ◽  
The United States ◽  
Ground Survey ◽  
Severity Grading ◽  
Crop Disease ◽  
Survey Results

The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.

scholarly journals Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 618 ◽  
Author(s):  
Samuel C. Hassler ◽  
Fulya Baysal-Gurel
Keyword(s):  
Precision Agriculture ◽  
Weed Management ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Point Clouds ◽  
Unmanned Aircraft ◽  
Ease Of Use ◽  
Biomass Estimation ◽  
Trade Offs ◽  
Legal Constraints

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

scholarly journals Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape

2018 ◽  
Vol 10 (9) ◽  
pp. 1484 ◽  
Author(s):  
Liang Wan ◽  
Yijian Li ◽  
Haiyan Cen ◽  
Jiangpeng Zhu ◽  
Wenxin Yin ◽  
...  
Keyword(s):  
Image Classification ◽  
Oilseed Rape ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Multispectral Images ◽  
Flower Number ◽  
Coverage Area ◽  
Reference Images ◽  
Remote Estimation

Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference. Images of the rape field were first classified using K-means method based on Commission Internationale de l’Éclairage (CIE) L*a*b* space, and the result showed that classified flower coverage area (FCA) possessed a high correlation with the flower number (r2 = 0.89). The relationships between ten commonly used vegetation indices (VIs) extracted from UAV-based RGB and multispectral images and the flower number were investigated, and the VIs of Normalized Green Red Difference Index (NGRDI), Red Green Ratio Index (RGRI) and Modified Green Red Vegetation Index (MGRVI) exhibited the highest correlation to the flower number with the absolute correlation coefficient (r) of 0.91. Random forest (RF) model was developed to predict the flower number, and a good performance was achieved with all UAV variables (r2 = 0.93 and RMSEP = 16.18), while the optimal subset regression (OSR) model was further proposed to simplify the RF model, and a better result with r2 = 0.95 and RMSEP = 14.13 was obtained with the variable combination of RGRI, normalized difference spectral index (NDSI (944, 758)) and FCA. Our findings suggest that combining VIs and image classification from UAV-based RGB and multispectral images possesses the potential of estimating flower number in oilseed rape.

scholarly journals Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices

2021 ◽  
Vol 13 (11) ◽  
pp. 2036
Author(s):  
Elio Romano ◽  
Simone Bergonzoli ◽  
Ivano Pecorella ◽  
Carlo Bisaglia ◽  
Pasquale De Vita
Keyword(s):  
Durum Wheat ◽  
Precision Agriculture ◽  
Survey Methods ◽  
Vegetation Indices ◽  
Wheat Yield ◽  
Spectral Indices ◽  
Entire Study ◽  
Growing Seasons ◽  
Use Of Time ◽  
Occurrence Matrix

One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing seasons, was proposed. Texture analysis performed using the Gray-Level Co-Occurrence Matrix (GLCM) was used to integrate and correct the sum of the vegetation indices maps (NDVI and MCARI2) and define the homogenous productivity zones on ten durum wheat fields in southern Italy. The homogenous zones identified through the method that integrates the GLCM indices with the spectral indices studied showed a greater accuracy (0.18–0.22 Mg ha−1 for ∑NDVIs + GLCM and 0.05–0.49 Mg ha−1 for ∑MCARI2s + GLCM) with respect to the methods that considered only the sum of the indices. Best results were also obtained with respect to the homogeneous zones derived by using yield maps of the previous year or vegetation indices acquired in a single day. Therefore, the survey methods based on the data collected over the entire study period provided the best results in terms of estimated yield; the addition of clustering analysis performed with the GLCM method allowed to further improve the accuracy of the estimate and better define homogeneous productivity zones of durum wheat fields.

scholarly journals Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Ali Ahmad ◽  
Javier Ordoñez ◽  
Pedro Cartujo ◽  
Vanesa Martos
Keyword(s):  
Precision Agriculture ◽  
Weed Management ◽  
Disease Surveillance ◽  
Soil Analysis ◽  
Vegetation Indices ◽  
Tree Plantation ◽  
Crop Monitoring ◽  
Agriculture Sector ◽  
Research Activities ◽  
Remotely Piloted Aircraft

The current COVID-19 global pandemic has amplified the pressure on the agriculture sector, inciting the need for sustainable agriculture more than ever. Thus, in this review, a sustainable perspective of the use of remotely piloted aircraft (RPA) or drone technology in the agriculture sector is discussed. Similarly, the types of cameras (multispectral, thermal, and visible), sensors, software, and platforms frequently deployed for ensuring precision agriculture for crop monitoring, disease detection, or even yield estimation are briefly discoursed. In this regard, vegetation indices (VIs) embrace an imperative prominence as they provide vital information for crop monitoring and decision-making, thus a summary of most commonly used VIs is also furnished and serves as a guide while planning to collect specific crop data. Furthermore, the establishment of significant applications of RPAs in livestock, forestry, crop monitoring, disease surveillance, irrigation, soil analysis, fertilization, crop harvest, weed management, mechanical pollination, crop insurance and tree plantation are cited in the light of currently available literature in this domain. RPA technology efficiency, cost and limitations are also considered based on the previous studies that may help to devise policies, technology adoption, investment, and research activities in this sphere.

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