scholarly journals Classification of Hass avocado (persea americana mill) in terms of its ripening via hyperspectral images

TecnoLógicas ◽  
2019 ◽  
Vol 22 (45) ◽  
pp. 109-128 ◽  
Author(s):  
Jhon Pinto ◽  
Hoover Rueda-Chacón ◽  
Henry Arguello
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Sector ◽  
Low Cost ◽  
Color Space ◽  
Principal Component ◽  
Persea Americana ◽  
Hyperspectral Images ◽  
Non Invasive ◽  
Hass Avocado

The use of non-invasive and low-cost methodologies allows the monitoring of fruit ripening and quality control, without affecting the product under study. In particular, the Hass avocado is of high importance for the agricultural sector in Colombia because the country is strongly promoting its export, which has generated an expansion in the number of acres cultivated with this fruit. Therefore, this paper aims to study and analyze the ripening state of Hass avocados through non-invasive hyperspectral images, using principal component analysis (PCA) along with spectral vegetation indices, such as the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), photochemical reflectance index (PRI), colorimetry analysis in the CIE L*a*b* color space, and color index triangular greenness index (TGI). In particular, this work conducts a quantitative analysis of the ripening process of a population of 7 Hass avocados over 10 days. The avocados under study were classified into three categories: unripe, close-to-ripe, and ripe. The obtained results show that it is possible to characterize the ripening state of avocados through hyperspectral images using a non-invasive acquisition system. Further, it is possible to know the post-harvest ripening state of the avocado at any given day.

scholarly journals Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Yulia Ivanova ◽  
Anton Kovalev ◽  
Vlad Soukhovolsky
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Principal Component ◽  
Stem Growth ◽  
Forest Growth ◽  
Parabolic Approximation ◽  
New Approach ◽  
The Relationship ◽  
Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

scholarly journals Regrowth and ornamental traits of bermudagrass fertilized with sewage sludge

2020 ◽  
Vol 26 (3) ◽  
pp. 390-398
Author(s):  
Philippe Solano Toledo Silva ◽  
Alessandro Reinaldo Zabotto ◽  
Patrick Luan Ferreira dos Santos ◽  
Matheus Vinícius Leal do Nascimento ◽  
Armando Reis Tavares ◽  
...  
Keyword(s):  
Sewage Sludge ◽  
Soil Solution ◽  
Digital Image Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Aesthetic Aspect ◽  
Positive Correlations ◽  
The Aesthetic ◽  
Ornamental Traits

Abstract The sewage sludge is a low-cost material and sustainable alternative to substitute chemical fertilizers on ornamental lawns and gardens. Thus, the objective was to evaluate the effects of the application of sewage sludge on the regrowth and ornamental traits of DiscoveryTM bermudagrass. The experiment was carried out during the fall/winter of 2019. The turf was removed and left the soil exposed for a new grass regrowth. The treatments applied were 0, 357, 714, 1,071 and 1,428 g m-2 sewage sludge spread evenly on the lawn in a single dose. The evaluations were carried out after 120 days and the soil solution (EC and NO3 -), Normalized difference vegetation index, root length, root + rhizome + stolon + leaves volume and digital image analysis were evaluated. The results showed that the increase of sewage sludge positively influenced the turfgrass development, both in the aesthetic aspect and on bermudagrass regrowth. The soil solution can show that the sludge increased the electrical conductivity and NO3- ions; however, it did not hinder the development of the lawn, even having positive correlations between these variables and the biometric evaluations of the plant. It is concluded that the dose of 1,428 g m-2 presented the best results for the evaluated characteristics, being the recommended one for use in the fertilization of bermudagrass DiscoveryTM.

Comparative Study of Change Detection and Urban Expansion Using Multi-Date Multi-Source Data: A Case Study of Greater Khartoum

2012 ◽  
Vol 23 (2) ◽  
pp. 139-172
Author(s):  
Abdullah Salman Alsalman Abdullah Salman Alsalman
Keyword(s):  
Image Processing ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Test Area ◽  
Image Differencing ◽  
Erdas Imagine

Noting that Khartoum represents the most rapidly expanding city in the Sudan and taking into account that change detection operations are seldom , the present study has been initiated to attempt to produce work that synthesizes land use/land cover (LULC) to investigate change detection using GIS, remote sensing data and digital image processing techniques; estimate, evaluate and map changes that took place in the city from 1975 to 2003. The experiment used the techniques of visual inspection, write-function-memoryinsertion, image differencing, image transformation i.e. normalized difference vegetation index (NDVI), tasseled cap, principal component analysis (PCA), post-classification comparison and GIS. The results of all these various techniques were used by the authors to study change detection of the geographic locale of the test area. Image processing and GIS techniques were performed using Intergraph Image analyst 8.4 and GeoMedia professional version 6, ERDAS Imagine 8.7, and ArcGIS 9.2. Results obtained were discussed and analyzed in a comparative manner and a conclusion regarding the best method for change detection of the test area was derived.

scholarly journals The prototype device for non-invasive diagnosis of arteriovenous fistula condition using machine learning methods

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marcin Grochowina ◽  
Lucyna Leniowska ◽  
Agnieszka Gala-Błądzińska
Keyword(s):  
Machine Learning ◽  
Arteriovenous Fistula ◽  
Low Cost ◽  
Principal Component ◽  
Special Kind ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Signal Acquisition ◽  
Non Invasive ◽  
Prototype Device

Abstract Pattern recognition and automatic decision support methods provide significant advantages in the area of health protection. The aim of this work is to develop a low-cost tool for monitoring arteriovenous fistula (AVF) with the use of phono-angiography method. This article presents a developed and diagnostic device that implements classification algorithms to identify 38 patients with end stage renal disease, chronically hemodialysed using an AVF, at risk of vascular access stenosis. We report on the design, fabrication, and preliminary testing of a prototype device for non-invasive diagnosis which is very important for hemodialysed patients. The system includes three sub-modules: AVF signal acquisition, information processing and classification and a unit for presenting results. This is a non-invasive and inexpensive procedure for evaluating the sound pattern of bruit produced by AVF. With a special kind of head which has a greater sensitivity than conventional stethoscope, a sound signal from fistula was recorded. The proces of signal acquisition was performed by a dedicated software, written specifically for the purpose of our study. From the obtained phono-angiogram, 23 features were isolated for vectors used in a decision-making algorithm, including 6 features based on the waveform of time domain, and 17 features based on the frequency spectrum. Final definition of the feature vector composition was obtained by using several selection methods: the feature-class correlation, forward search, Principal Component Analysis and Joined-Pairs method. The supervised machine learning technique was then applied to develop the best classification model.

scholarly journals Chlorophyll Fluorescence Kinetics May Be Useful to Identify Early Drought and Irrigation Effects on Photosynthetic Apparatus in Field-Grown Wheat

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1275
Author(s):  
Lenka Botyanszka ◽  
Marek Zivcak ◽  
Erik Chovancek ◽  
Oksana Sytar ◽  
Viliam Barek ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Water Deficit ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Photosynthetic Apparatus ◽  
Optical Methods ◽  
Accurate Information ◽  
Ojip Transient ◽  
Fluorescence Kinetics ◽  
Non Invasive

To assess the reliability and sensitivity of non-invasive optical methods to detect the early effects of water deficit in the field, we analyzed the time-series of non-invasive measurements obtained in a dry season in a representative collection of wheat genotypes grown in small-plot field trials, in non-irrigated and irrigated variants. Despite a progressive water deficit and significant yield loss, the measurements indicated very minor changes in chlorophyll content or canopy cover. This corresponded well to the insignificant differences in spectral reflectance normalized difference vegetation index (NDVI) values. On the other hand, we identified the significant and rapid response of fast fluorescence kinetics data following the onset of irrigation. Analysis of parameters showed the main effects of drought were associated with changes in the amplitude of the I–P phase of the OJIP transient, indicating changes at the level of photosystem I and beyond. Statistical analyses identified the integrative parameter performance index PItot as the most sensitive parameter, which well-reflects the differences in responses of the genotypes to water deficit. Our results suggest that focusing on photosynthetic functions detected by the rapid chlorophyll fluorescence records can provide more accurate information on the drought stress level, compared to the structural data obtained by absorbance or reflectance measurements.

Evaluation of a Low-cost Camera for Agricultural Applications

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Abdon Francisco Aureliano Netto ◽  
Rodrigo Nogueira Martins ◽  
Guilherme Silverio Aquino De Souza ◽  
Fernando Ferreira Lima Dos Santos ◽  
Jorge Tadeu Fim Rosas
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Paspalum Notatum ◽  
Crop Canopy ◽  
N Application ◽  
Satisfactory Accuracy ◽  
Agricultural Applications

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

scholarly journals A New Approach for Land Degradation and Desertification Assessment Using Geospatial Techniques

2017 ◽  
Author(s):  
Masoud Masoudi ◽  
Parviz Jokar ◽  
Biswajeet Pradhan
Keyword(s):  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Evaluation Method ◽  
Low Cost ◽  
United Nations Environment Programme ◽  
Geospatial Techniques ◽  
New Approach ◽  
Khuzestan Province ◽  
Current State

Abstract. Land degradation reduces production of biomass and vegetation cover in every land uses. The lack of specific data related to degradation is a severe limitation for its monitoring. Assessment of current state of land degradation or desertification is very difficult because this phenomena includes several complex processes. For that reason, there is no common agreement has been achieved among the scientific community for its assessment. This study was carried out as an attempt to develop a new approach for land degradation assessment based on its current state by modifying of FAO1/UNEP2 index and normalized difference vegetation index (NDVI) index in Khuzestan province, placed in the southwestern part of Iran. The proposed evaluation method is easy to understand the degree of destruction due to low cost and save time. Results showed that based on percent of hazard classes in current condition of land degradation, the most widespread and minimum area of hazard classes are moderate (38.6 %) and no hazard (0.65 %) classes, respectively. While results in the desert area of study area showed that severe class is much widespread than other hazard classes, showing environmentally bad situation in the study area. Statistical results indicated that degradation is highest in desert and then rangeland compared to dry cultivation and forest. Also statistical test showed average of degradation amount in the arid region is higher than other climates. It is hoped that this attempt using geospatial techniques will be found applicable for other regions of the world and better planning and management of lands, too. 1 Food and Agriculture Organization 2 United Nations Environment Programme

scholarly journals Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt

Plant Disease ◽  
2012 ◽  
Vol 96 (11) ◽  
pp. 1683-1689 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Reza Ehsani ◽  
Sharon A. Inch ◽  
Randy C. Ploetz
Keyword(s):  
Discriminant Analysis ◽  
Vegetation Index ◽  
Near Infrared ◽  
Principal Component ◽  
Persea Americana ◽  
Near Infrared Reflectance ◽  
Laurel Wilt ◽  
Linear Discriminant ◽  
Raffaelea Lauricola ◽  
Non Destructive

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

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