The use of RGB cameras in defining crop development in legumes

2017 ◽  
Vol 8 (2) ◽  
pp. 224-228 ◽  
Author(s):  
I. Travlos ◽  
A. Mikroulis ◽  
E. Anastasiou ◽  
S. Fountas ◽  
D. Bilalis ◽  
...  
Keyword(s):  
Human Population ◽  
Vegetation Index ◽  
Growth Parameters ◽  
Vegetation Indices ◽  
Digital Camera ◽  
High Yield ◽  
Legume Crop ◽  
Crop Varieties ◽  
Crop Development ◽  
Crop Parameters

The human population is expected to reach 9 billion by 2050 and thus high yield crop varieties need to be developed. Remote sensing can estimate crop parameters non-destructively and quickly. The aim of this study was to compare and evaluate the use of a commercial RGB camera with an expensive canopy sensor in the crop development of two legumes. The RGB camera based vegetation index (NGRDI) was compared with the canopy sensor derived vegetation indices (NDVI and NDRE) for estimating legume crop growth parameters. The results indicated that the use of a simple digital camera RGB can in some cases replace spectral canopy sensors.

scholarly journals Evaluation of Organic Camelina Crop Under Different Tillage Systems and Fertilization Types Using Proximal Remote Sensing

2020 ◽  
Vol 77 (1) ◽  
pp. 1
Author(s):  
Foteini ANGELOPOULOU ◽  
Evangelos ANASTASIOU ◽  
Spyros FOUNTAS ◽  
Dimitrios BILALIS
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth And Yield ◽  
Tillage Systems ◽  
Minimum Tillage ◽  
Spectral Vegetation Indices ◽  
Crop Parameters

A field experiment was conducted in Southern Greece to assess Normalized Difference Vegetation Index (NDVI) and Red-Edge Normalized Difference Vegetation Index (NDRE) in estimating Camelina’s crop growth and yield parameters under different tillage systems (conventional and minimum tillage) and organic fertilization types (compost, vermicompost and untreated control). A proximal canopy sensor was used to measure the aforementioned Spectral Vegetation Indices (SVIs) at different days after sowing (DAS). Camelina presented the highest values of NDVI and NDRE under compost fertilization (0.63 and 0.22 accordingly) and minimum tillage system (0.50 and 0.18 accordingly). Additionally, the highest correlations between the measured crop parameters and NDVI, NDRE were achieved at leaf development to early flowering stage. Moreover, NDRE presented the highest correlation with seed yield (R2=0.60, p<0.05) and thus it is suggested for estimating Camelina’s productivity instead of NDVI. Finally, further research is needed for adopting the use of remote sensing technologies on predicting Camelina’s crop growth and yield.

scholarly journals Estimación de la evapotranspiración de maíz forrajero apoyada con sensores remotos y mediciones in situ.

2019 ◽  
Vol 37 (3) ◽  
pp. 279 ◽  
Author(s):  
Arturo Reyes González ◽  
David Guadalupe Reta Sánchez ◽  
Juan Isidro Sánchez Duarte ◽  
Esmeralda Ochoa Martínez ◽  
Karla Rodríguez Hernández ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation System ◽  
Vegetation Indices ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Crop Development ◽  
Subsurface Drip

Irrigated agriculture requires better estimates of crop water demand. The aim of this study was to estimate the evapotranspiration (ETc) in forage corn through vegetation indices obtained in situ and estimated with remote sensing in the Comarca Lagunera, Mexico. The research was carried out in 2011 and 2012 in four 900 m2 plots irrigated with a subsurface drip irrigation system. Normalized Difference Vegetation Index (NDVI) and crop coeff icient (Kc) during crop development were determined. The initial, maximum and f inal NDVI values were 0.13, 0.79 and 0.63 for both methods and in both cycles. The maximum Kc values were obtained 54 and 48 days after sowing (DDS) with GreenSeeker, and at 61 and 59 DDS with satellite images in 2011 and 2012, respectively. The results showed a good relationship between ETc estimated in situ and ETc estimated with remote sensing (r = 0.98) for both years. Although the variation of ETc using both methods was 1.2 mm day‑1, early in the cycle and 7.4 mm day-1 to flowering start-milky grains. Water needs of forage corn were estimated with similar precision using remote sensing and in situ measurements. Therefore, both methods can be used to improve irrigation scheduling and preserve water resources in agriculture.

scholarly journals Use of a Digital Camera to Monitor the Growth and Nitrogen Status of Cotton

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Biao Jia ◽  
Haibing He ◽  
Fuyu Ma ◽  
Ming Diao ◽  
Guiying Jiang ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Assessment Tool ◽  
Digital Images ◽  
Digital Camera ◽  
Canopy Cover ◽  
High Yield ◽  
N Content ◽  
Total N ◽  
Growth Properties ◽  
N Status

The main objective of this study was to develop a nondestructive method for monitoring cotton growth and N status using a digital camera. Digital images were taken of the cotton canopies between emergence and full bloom. The green and red values were extracted from the digital images and then used to calculate canopy cover. The values of canopy cover were closely correlated with the normalized difference vegetation index and the ratio vegetation index and were measured using a GreenSeeker handheld sensor. Models were calibrated to describe the relationship between canopy cover and three growth properties of the cotton crop (i.e., aboveground total N content, LAI, and aboveground biomass). There were close, exponential relationships between canopy cover and three growth properties. And the relationships for estimating cotton aboveground total N content were most precise, the coefficients of determination (R2) value was 0.978, and the root mean square error (RMSE) value was 1.479 g m−2. Moreover, the models were validated in three fields of high-yield cotton. The result indicated that the best relationship between canopy cover and aboveground total N content had anR2value of 0.926 and an RMSE value of 1.631 g m−2. In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring cotton growth and N status.

scholarly journals EXTRACTION OF DEBILITATED TREES ALONG THE ROAD BY BLOCKED NDVI

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 209-214
Author(s):  
M. Tokunaga
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Digital Camera ◽  
Infrared Camera ◽  
Geometric Correction ◽  
Geometric Accuracy ◽  
The Road ◽  
Index Changes ◽  
Changes Over Time

Abstract. A method for extracting degraded trees was developed by using a near-infrared camera modified from a digital camera to photograph roadside trees. Traditionally, remote sensing has utilized vegetation index using near-infrared and red bands as a method to extract degraded trees. However, it was not possible to assess the health of roadside trees sufficiently because the observation from above only observed the canopy of the roadside trees. Observations from the ground can cover the shortcomings because they observe the sides as well as the canopy of the tree. However, ground-based observations are strongly influenced by sunlight, which needs to be compensated for. Also, since the target is trees on the side of the road, it is desirable to take a video of the trees from above the vehicle. The basic idea of this study is simple: a tree where the vegetation index is lower than other trees is considered a cautionary tree, and a tree where the vegetation index changes over time or month is lower than other trees is extracted as a degraded tree. In order to compare videos shot at different times, frame matching of videos and geometric correction between frames were performed. To account for geometric accuracy, pixels were grouped together as blocks, and changes in vegetation indices from block to block were analyzed. In order to improve the accuracy of the analysis, non-vegetation areas were removed from the images. As a result, blocks of debilitated trees were extracted from the trees along the road.

scholarly journals CIE L*a*b* Color Space Based Vegetation Indices Derived from Unmanned Aerial Vehicle Captured Images for Chlorophyll and Nitrogen Content Estimation of Tea (Camellia sinensis L. Kuntze) Leaves

2019 ◽  
Vol 4 (1) ◽  
pp. 46
Author(s):  
Wahono Wahono ◽  
Didik Indradewa ◽  
Bambang Hendro Sunarminto ◽  
Eko Haryono ◽  
Djoko Prajitno
Keyword(s):  
Visible Light ◽  
Nitrogen Content ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Color Space ◽  
Digital Camera ◽  
Leaf Nitrogen Content ◽  
Light Spectrum ◽  
Simple Index ◽  
Tea Leaf

A lot of digital image techniques to assess crop agronomic character have been developed.  Most of those techniques are based on non-visible light equiped cameras, such as infared wavelengths. This research was aimed to examine the use of commercial digital camera with sensor range in visible light spectrum using CIE L*a*b* color space to estimate chlorophyll and nitrogen content of tea leaf.  Data was collected from an experiment of nitrogen dossage levels on 3 years after prunning tea crops.  The result shows that Lb* Difference Simple Index (LI), a*b* Difference Simple Index (AI), and  a* Vegetation Index (VIA) can be used to estimate tea leaf chlorophyll and nitrogen content.  The relationship between VIA and tea leaf nitrogen content was defined on linear equation y = 1.8382x2 - 0.3099x + 3.0658 with determinant coefficient R² = 0.71.

scholarly journals Evaluation of vegetation indices and plant growth regulator use on the rooting of azalea cuttings

2020 ◽  
Vol 38 (2) ◽  
pp. 153-159 ◽  
Author(s):  
Ping-Cheng Hou ◽  
Kuan-Hung Lin ◽  
Yen-Jung Huang ◽  
Chun-Wei Wu ◽  
Yu-Sen Chang
Keyword(s):  
Plant Growth ◽  
Root Growth ◽  
Vegetation Index ◽  
Growth Parameters ◽  
Vegetation Indices ◽  
Dry Weight ◽  
Naphthaleneacetic Acid ◽  
Stem Cuttings ◽  
Root Dry Weight ◽  
Rooting Media

ABSTRACT The objective of our study was to develop a protocol enabling the use of vegetation indices to evaluate the rooting of Azalea (Rhododendron pulchrum cv. Sweet) cuttings. Six root growth parameters were recorded after exposing those cuttings to rooting media for 47 days. Among plants with different soil-plant analysis development (SPAD) and normalized difference vegetation index (NDVI) values, those with higher values exhibited significantly higher number of roots, root length, and root dry weight, suggesting that reflectance indices were useful in measuring the root growth parameters of the cuttings. Another aim of this work was to study the effects of plant growth regulators (PGRs) on rooting for cutting propagation. Azalea cuttings were soaked in the treatments with indole-3-butyric acid (IBA) at 2,000 mg L-1 or combined with 1-naphthaleneacetic acid (NAA; 2,000 mg L-1), salicylic acid (SA; 10-4 M), and thiamine (TA; 800 mg L-1). The same observations as SPAD and NDVI on six different rooting parameters were recorded and analyzed after cutting’s exposure to rooting media for 99 days for the auxin test, and 62 days for the SA and TA tests. Compared to NAA alone, IBA enhanced root growth and development as determined by increases in all parameters, and therefore it was used thereafter. Successful results for the number of roots and root dry weight were achieved using azalea cuttings with a combination of IBA and SA. In addition, the mix of IBA and TA resulted in higher number of roots and length of root. These combined treatments are recommended for establishing stem cuttings to produce nursery plants of azalea.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

2021 ◽  
Vol 11 (5) ◽  
pp. 2282
Author(s):  
Masudulla Khan ◽  
Azhar U. Khan ◽  
Mohd Abul Hasan ◽  
Krishna Kumar Yadav ◽  
Marina M. C. Pinto ◽  
...  
Keyword(s):  
Plant Growth ◽  
Crop Yield ◽  
Biotic Stress ◽  
Crop Production ◽  
Plant Disease ◽  
Yield Loss ◽  
Growth Parameters ◽  
Plant Diseases ◽  
High Yield ◽  
Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

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