scholarly journals Comparison of selected vegetation indices and determination of suitability for yield description on agricultural field

2017 ◽  
Author(s):  
Katerina Krizova ◽  
Jitka Kumhalova
Keyword(s):  
Vegetation Indices ◽  
Agricultural Field

scholarly journals The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

2021 ◽  
Vol 13 (1) ◽  
pp. 155
Author(s):  
Dmitry I. Rukhovich ◽  
Polina V. Koroleva ◽  
Danila D. Rukhovich ◽  
Natalia V. Kalinina
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Degradation ◽  
Arable Land ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Agricultural Field ◽  
Sensing Data ◽  
Selection Of

Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

UV-Visible Spectrophotometric Determination of Lambda-Cyhalothrin Insecticide in Vegetables, Soil and Water Samples

2019 ◽  
Vol 31 (1) ◽  
pp. 1-9
Author(s):  
Deepak Kumar Sahu ◽  
Joyce Rai ◽  
Chhaya Bhatt ◽  
Manish K. Rai ◽  
Jyoti Goswami ◽  
...  
Keyword(s):  
Crop Production ◽  
Limit Of Detection ◽  
Low Cost ◽  
Molar Absorptivity ◽  
Agricultural Field ◽  
Limit Of Quantification ◽  
Yellow Colour ◽  
Modern Age ◽  
Lambda Cyhalothrin

In modern age pesticide is used widely in agriculture. Lambda-cyhalothrin (LCT) is one of the most used pesticides which are used as a insecticide to kill pest, tricks, flies etc in agricultural field and it is also used for crop production. We have developed new method to detect LCT insecticide in agriculture field and reduce its uses. In this method we found the maximum absorbance at 460 nm for yellow colour dye. We also calculated limit of detection and limit of quantification 0.001 mg kg-1 and 0.056 mg kg-1 respectively. Molar absorptivity and Sandell’s sensitivity was also calculated and obtained 1.782 ×107 mol-1 cm-1 and 9.996 ×10-6 µg cm-2 respectively. The obtained yellow colour dye obeyed Beer’s law limit range of 0.5 µg ml -1 to 16 µg ml-1 in 25 ml. This method is less time consuming, selective, simple, sensitive and low cost. Present method is successfully applied in various soil, water and vegetable samples.

Determination of sufficiency values of canopy reflectance vegetation indices for maximum growth and yield of cucumber

2017 ◽  
Vol 84 ◽  
pp. 1-15 ◽  
Author(s):  
Francisco M. Padilla ◽  
M. Teresa Peña-Fleitas ◽  
Marisa Gallardo ◽  
Rodney B. Thompson
Keyword(s):  
Vegetation Indices ◽  
Maximum Growth ◽  
Growth And Yield ◽  
Canopy Reflectance

scholarly journals Spatio-Temporal Determination of Irrigated Paddy Rice Pixels Using Evapotranspiration and Vegetation Indices: A Case Study for Ca River Basin in Vietnam

2020 ◽  
Vol 08 (12) ◽  
pp. 94-107
Author(s):  
Chung N. Luong ◽  
Lan T. Ha ◽  
Thanh C. Pham ◽  
Hung X. Dinh ◽  
Thanh T. Hoang ◽  
...  
Keyword(s):  
River Basin ◽  
Vegetation Indices ◽  
Paddy Rice ◽  
Spatio Temporal ◽  
Ca River

scholarly journals Reflectance-Based Vegetation Index Assessment of Four Plant Species Exposed to Lithium Chloride

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2750
Author(s):  
Nicole Martinez ◽  
Julia Sharp ◽  
Thomas Johnson ◽  
Wendy Kuhne ◽  
Clay Stafford ◽  
...  
Keyword(s):  
Plant Species ◽  
Lithium Chloride ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Reflectance Spectra ◽  
Treatment Level ◽  
Length Of Treatment ◽  
Contact Probe ◽  
Relative Water

This study considers whether a relationship exists between response to lithium (Li) exposure and select vegetation indices (VI) determined from reflectance spectra in each of four plant species: Arabidopsis thaliana, Helianthus annuus (sunflower), Brassica napus (rape), and Zea mays (corn). Reflectance spectra were collected every week for three weeks using an ASD FieldSpec Pro spectroradiometer with both a contact probe (CP) and a field of view probe (FOV) for plants treated twice weekly in a laboratory setting with 0 mM (control) or 15 mM of lithium chloride (LiCl) solution. Plants were harvested each week after spectra collection for determination of relevant physical endpoints such as relative water content and chlorophyll content. Mixed effects analyses were conducted on selected endpoints and vegetation indices (VI) to determine the significance of the effects of treatment level and length of treatment as well as to determine which VI would be appropriate predictors of treatment-dependent endpoints. Of the species considered, A. thaliana exhibited the most significant effects and corresponding shifts in reflectance spectra. Depending on the species and endpoint, the most relevant VIs in this study were NDVI, PSND, YI, R1676/R1933, R750/R550, and R950/R750.

The Determination of the Effect of A New Grass Killer on Application Equipment★

CORROSION ◽  
1956 ◽  
Vol 12 (2) ◽  
pp. 35-39
Author(s):  
J. A. KELLY ◽  
W. J. FALKENSTEIN ◽  
J. P. CARR
Keyword(s):  
Sodium Salt ◽  
Laboratory Tests ◽  
Production Model ◽  
Agricultural Field ◽  
Standard Construction ◽  
Corrosive Effect ◽  
Preliminary Study ◽  
Chemical Corporation ◽  
Standard Production

Abstract Laboratory tests have been conducted to determine the possible corrosive effect of aqueous solutions of Dalapon sodium salt (sodium 2,2-dichloropropionate), a new grass killer, on the metals present in typical agricultural field sprayers. From preliminary study, it was concluded that solutions of the chemical could be used in agricultural field sprayers of standard construction with little effect on the materials of construction. In order to corroborate these findings, a standard production model agricultural field sprayer was obtained from the John Bean Division, Food Machinery and Chemical Corporation, for test purposes. The sprayer was operated for a period of four weeks using standard concentrations of Dalapon sodium salt in water and observations were made. From these data and from observation of the disassembled sprayer after testing, it is concluded that the chemical exhibited slight but not significant effect on the materials of construction.

scholarly journals Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Qian Sun ◽  
Lin Sun ◽  
Meiyan Shu ◽  
Xiaohe Gu ◽  
Guijun Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Confusion Matrix ◽  
Accurate Determination ◽  
Vegetation Indices ◽  
Texture Features ◽  
Multispectral Image ◽  
Visual Interpretation ◽  
Aerial Vehicle

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

Comparison among normalized vegetation indices for the determination of LAI

2003 ◽  
Author(s):  
Luca Bernasconi ◽  
Ivan Pippi ◽  
Sabrina Raddi
Keyword(s):  
Vegetation Indices

scholarly journals Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

2019 ◽  
Vol 11 (16) ◽  
pp. 1920 ◽  
Author(s):  
Rei Sonobe
Keyword(s):  
Vegetation Indices ◽  
Support Vector ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Multilayer Feedforward Neural Network ◽  
Crop Types ◽  
Crop Classification ◽  
Sar Data ◽  
Using Data ◽  
Learning Machine

The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 ± 1.8% were achieved using SVM.

Determination of differences in crop injury from aerial application of glyphosate using vegetation indices

2011 ◽  
Vol 77 (2) ◽  
pp. 204-213 ◽  
Author(s):  
B.V. Ortiz ◽  
S.J. Thomson ◽  
Y. Huang ◽  
K.N. Reddy ◽  
W. Ding
Keyword(s):  
Vegetation Indices ◽  
Aerial Application ◽  
Crop Injury
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