scholarly journals PEMANFAATAN INDEKS VEGETASI UNTUK ESTIMASI KANDUNGAN KALIUM PADA TANAMAN NANAS (Ananas comosus) MENGGUNAKAN UAV (Unmanned Aerial Vehicle) DI PT. GREAT GIANT PINEAPPLE, LAMPUNG

2020 ◽  
Vol 8 (1) ◽  
pp. 91-99
Author(s):  
Dita Khairunnisa ◽  
Mochtar Lutfi Rayes ◽  
Christanti Agustina
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Nutrient Status ◽  
Nutrient Content ◽  
Aerial Photographs ◽  
Ananas Comosus ◽  
P Value ◽  
Distribution Maps ◽  
Significant Difference ◽  
Aerial Vehicle

PT Great Giant Pineapple (PT. GGP) is the largest pineapple production company in Indonesia. One of the nutrients that pineapple plants really need is potassium (K). K plays a key role in carbohydrate metabolism and transport of photosynthates from source to sink. Remote sensing technology has been developed to estimate nutrient status, one of which is using an Unmanned Aerial Vehicle (UAV). This study aims to estimate the K nutrient content in pineapple plants using vegetation indexes in the form of NDVI (Normalyzed Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and OSAVI (Optimized of Soil Adjusted Vegetation Index). The research was carried out by taking aerial photographs and samples of pineapple plants in the 5 months phase before forcing up to 2 months after forcing (F-5 to F + 2), laboratory analysis, statistical analysis, and making distribution maps. The results showed that the relationship between the vegetation index value and K plant was the strongest and most significant is in 1 month before forcing phase (F-1) with the same r value for the three indices vegetation (r=0.867). The results of the regression analysis between the NDVI, SAVI and OSAVI values with K plant were 75.17%, 75.18% and 75.17%, respectively. The calculation of the K estimate using three methods yields no different values. The validation results using paired t test (t count -0.63; t table 2.31; p-value 0.544) where the K content in the measured plants and the estimation results showed no significant difference with the measurement results.

ESTIMASI KANDUNGAN FOSFOR PADA TANAMAN NANAS MENGGUNAKAN UNMANNED AERIAL VEHICLE (UAV) DI PT. GREAT GIANT PINEAPPLE, LAMPUNG

2021 ◽  
Vol 8 (2) ◽  
pp. 427-435
Author(s):  
Revaldy Andika ◽  
Retno Suntari
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nutrient Content ◽  
Aerial Photographs ◽  
Estimation Model ◽  
P Deficiency ◽  
Distribution Maps ◽  
Stunted Growth ◽  
Significant Difference ◽  
Plantation Company

PT. Great Giant Pineapple (PT. GGP) is the largest pineapple plantation company in Indonesia, with a land area of approximately ±33,000 ha and dominated by soil types in the form of Ultisols. Soil fertility at PT. GGP tends to have relatively low nutrient content, one of which is phosphorus due to Al fixation. The nutrient P in pineapple is used to stimulate root growth, accelerate the ripening of fruit and seeds. Symptoms arising from P deficiency will experience stunted growth (stunted), and the pineapple will become imperfect. This study aimed to estimate the P nutrient content in pineapple plants using vegetation indexes in the form of GNDVI (Green Normalized Difference Vegetation Index). The study was carried out by taking aerial photographs and samples of pineapple plants in the 1 months phase before forcing and 1 months after forcing (F-1 and F+1), laboratory analysis, statistical analysis, and making distribution maps. The results showed that the vegetation index could estimate the nutrient content of P using the best estimation model. This was evidenced from the results of the correlation test which shows a very strong and real relationship of 0.81-0.82 with the regression test results of 66%-67%. In addition, the results of the validation test using the paired t-test showed that the t-count was smaller than the t-table of 2.30, which means that the estimated GNDVI vegetation index and the P nutrient content of pineapple plants showed no significant difference.

Comparison of Unmanned Aerial Vehicle Technology versus Standard Practice of Scene Assessment by Paramedic Students of a Mass-Gathering Event

2021 ◽  
pp. 1-6
Author(s):  
Trevor Jain ◽  
Aaron Sibley ◽  
Henrik Stryhn ◽  
Adam Lund ◽  
Ives Hubloue
Keyword(s):  
Data Collection ◽  
Unmanned Aerial Vehicle ◽  
P Value ◽  
Mass Gathering ◽  
Data Collection Tool ◽  
Secondary Importance ◽  
Standard Practice ◽  
Mass Gatherings ◽  
Significant Difference ◽  
Aerial Vehicle

Abstract Introduction: The proliferation of unmanned aerial vehicle (UAV) technology has the potential to change the situational awareness of medical incident commanders’ (ICs’) scene assessment of mass gatherings. Mass gatherings occur frequently and the potential for injury at these events is considered higher than the general population. These events have generated mass-casualty incidents (MCIs) in the past. The aim of this study was to compare UAV technology to standard practice (SP) in scene assessment using paramedic students during a mass-gathering event (MGE). Methods: This study was conducted in two phases. Phase One consisted of validation of the videos and accompanying data collection tool. Phase One was completed by 11 experienced paramedics from a provincial Emergency Medical Services (EMS) service. Phase Two was a randomized comparison with 47 paramedic students from the Holland College Paramedicine Program (Charlottetown, Prince Edward Island, Canada) of the two scene assessment systems. For Phase Two, the paramedic students were randomized into a UAV or a SP group. The data collection tool consisted of two board categories: primary importance with 20 variables and secondary importance with 25 variables. After a brief narrative, participants were either shown UAV footage or the ground footage depending on their study group. After completion of the videos, study participants completed the data collection tool. Results: The Phase One validation showed good consensus in answers to most questions (average 79%; range 55%-100%). For Phase Two, a Fisher’s exact test was used to compare each variable from the UAV and SP groups using a P value of .05. Phase Two demonstrated a significant difference between the SP and UAV groups in four of 20 primary variables. Additionally, significant differences were found for seven out of 25 secondary variables. Conclusion: This study demonstrated the accurate, safe, and feasible use of a UAV as a tool for scene assessment by paramedic students at an MGE. No observed statistical difference was noted in a majority of both primary and secondary variables using a UAV for scene assessment versus SP.

scholarly journals Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4442
Author(s):  
Zijie Niu ◽  
Juntao Deng ◽  
Xu Zhang ◽  
Jun Zhang ◽  
Shijia Pan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Distribution Maps ◽  
Time Operation ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Segmentation Image

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

scholarly journals Efficacy of Bio-inoculants Arbuscular Mycorrhizal Fungi and Phosphorus on Micronutrient Status of Leaves and Soil in Litchi (Litchi chinensis Sonn.) Layers in Nursery Condition

2020 ◽  
pp. 122-129
Author(s):  
Priyanka Kumari ◽  
R. R. Singh ◽  
Ruby Rani ◽  
Mahendra Singh ◽  
Uday Kumar
Keyword(s):  
Mycorrhizal Fungi ◽  
Copper Content ◽  
Soil Nutrient ◽  
Arbuscular Mycorrhizal ◽  
Nutrient Status ◽  
Nutrient Content ◽  
Time Duration ◽  
Litchi Chinensis ◽  
Significant Difference ◽  
Phosphorus Application

Litchi (Litchi chinensis Sonn.) originated from South China, it is sub-tropical evergreen fruit crops, especially grown on the marginal climate of tropics and subtropics. It is delicious juicy fruit of India having excellent nutritional quality, pleasant flavoured, good amount of antioxidant and vitamins C, vitamin B-complex and phytonutrients flavonoids. It has a great potential to earn foreign exchange in the national and international market through export. Arbuscular mycorrhizal (AM) infection is a common association between plant roots and microorganisms. It is responsible for increasing plant nutrient uptake and also increases in macro and micronutrients in leaf. Therefore, the present work has been analyzed macro and micro nutrients from soil and leaf, after 60, 90 and 120 days after inoculation of two bio-inoculants with phosphorus (SSP) including nine treatments with three replications. After 120 days of inoculation both the species of mycorrhizal combination with phosphorus application were very effective. Highest Copper content is (10.99 ppm), Zinc (33.17 ppm), Iron (121.47 ppm) and Manganese (15.33 ppm) was recorded in case T5 (G. mosseae 10 g + Phosphorus 50 mg kg-1 of soil) which is gradually increases. The soil nutrient content gradually decreased with time duration but no- significant difference was found among treatments after 120 days inoculation. After 120 days potting result was found that the Copper content is (1.70 ppm), Zinc (3.07 ppm), Iron (7.80 ppm) and Manganese (4.00 ppm) was recorded in case T5 (G. mosseae 10 g + Phosphorus 50 mg kg-1 of soil).this research was undertaken to find out whether Arbuscular mycorrhizal (AM) infection and phosphorus affect the micro-nutrient status of soil and leaves in nursery stage.

Illumination Correction of Multi-Time RGB Images Obtained with an Unmanned Aerial Vehicle

2021 ◽  
pp. 50-58
Author(s):  
Michael Yu. Kataev ◽  
Maria M. Dadonova ◽  
Dmitry S. Efremenko
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Digital Camera ◽  
Quantitative Result ◽  
Efficient Solutions ◽  
Acquisition Time ◽  
Time Interval ◽  
Agricultural Field ◽  
Aerial Vehicle ◽  
Rgb Images

The goal of this research was to study and optimize multi-temporal RGB images obtained by a UAV (unmanned aerial vehicle). A digital camera onboard the UAV allows obtaining data with a high temporal and spatial resolution of ground objects. In the case considered by us, the object of study is agricultural fields, for which, based on numerous images covering the agricultural field, image mosaics (orthomosaics) are constructed. The acquisition time for each orthomosaic takes at least several hours, which imposes a change in the illuminance of each image, when considered separately. Orthomosaics obtained in different periods of the year (several months) will also differ from each other in terms of illuminance. For a comparative analysis of different parts of the field (orthomosaic), obtained in the same time interval or comparison of areas for different periods of time, their alignment by illumination is required. Currently, the majority of alignment approaches rely rather on colour (RGB) methods, which cannot guarantee finding efficient solutions, especially when it is necessary to obtain a quantitative result. In the paper, a new method is proposed that takes into account the change in illuminance during the acquisition of each image. The general formulation of the problem of light correction of RGB images in terms of assessing the colour vegetation index Greenness is considered. The results of processing real measurements are presented.

Analysis of the spectrum and vegetation index of rice under different nitrogen levels based on unmanned aerial vehicle remote sensing

2018 ◽  
Vol 11 (5) ◽  
pp. 832-840
Author(s):  
裴信彪 PEI Xin-biao ◽  
吴和龙 WU He-long ◽  
马 萍 MA Ping ◽  
严永峰 YAN Yong-feng ◽  
彭 程 PENG Cheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Nitrogen Levels ◽  
Aerial Vehicle

scholarly journals Inherent Reflectance Variability of Vegetation

Agriculture ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 246 ◽  
Author(s):  
Baabak Mamaghani ◽  
M. Grady Saunders ◽  
Carl Salvaggio
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Time Of Day ◽  
Unmanned Aircraft ◽  
P Value ◽  
Ground Sample ◽  
Significant Difference

With the inception of small unmanned aircraft systems (sUAS), remotely sensed images have been captured much closer to the ground, which has meant better resolution and smaller ground sample distances (GSDs). This has provided the precision agriculture community with the ability to analyze individual plants, and in certain cases, individual leaves on those plants. This has also allowed for a dramatic increase in data acquisition for agricultural analysis. Because satellite and manned aircraft remote sensing data collections had larger GSDs, self-shadowing was not seen as an issue for agricultural remote sensing. However, sUAS are able to image these shadows which can cause issues in data analysis. This paper investigates the inherent reflectance variability of vegetation by analyzing six Coneflower plants, as a surrogate for other cash crops, across different variables. These plants were measured under different forecasts (cloudy and sunny), at different times (08:00 a.m., 09:00 a.m., 10:00 a.m., 11:00 a.m. and 12:00 p.m.), and at different GSDs (2, 4 and 8 cm) using a field portable spectroradiometer (ASD Field Spec). In addition, a leafclip spectrometer was utilized to measure individual leaves on each plant in a controlled lab environment. These spectra were analyzed to determine if there was any significant difference in the health of the various plants measured. Finally, a MicaSense RedEdge-3 multispectral camera was utilized to capture images of the plants every hour to analyze the variability produced by a sensor designed for agricultural remote sensing. The RedEdge-3 was held stationary at 1.5 m above the plants while collecting all images, which produced a GSD of 0.1 cm/pixel. To produce 2, 4, and 8 cm GSD, the MicaSense RedEdge-3 would need to be at an altitude of 30.5 m, 61 m and 122 m respectively. This study did not take background effects into consideration for either the ASD or MicaSense. Results showed that GSD produced a statistically significant difference (p < 0.001) in Normalized Difference Vegetation Index (NDVI, a commonly used metric to determine vegetation health), R 2 values demonstrated a low correlation between time of day and NDVI, and a one-way ANOVA test showed no statistically significant difference in the NDVI computed from the leafclip probe (p-value of 0.018). Ultimately, it was determined that the best condition for measuring vegetation reflectance was on cloudy days near noon. Sunny days produced self-shadowing on the plants which increased the variability of the measured reflectance values (higher standard deviations in all five RedEdge-3 channels), and the shadowing of the plants decreased as time approached noon. This high reflectance variability in the coneflower plants made it difficult to accurately measure the NDVI.

scholarly journals Nitrogen, phosphorus and potassium status of rice co-planted with a novel phytoremediator, Marsilea minuta L.

2018 ◽  
Vol 43 (2) ◽  
pp. 211-218
Author(s):  
U Hassi ◽  
MT Hossain ◽  
SMI Huq
Keyword(s):  
Oryza Sativa L ◽  
Negative Relationship ◽  
Nutrient Content ◽  
Control Treatment ◽  
P Value ◽  
Aquatic Fern ◽  
Significant Negative Relationship ◽  
Significant Difference ◽  
Phosphorus And Potassium ◽  
Nitrogen Phosphorus

A pot experiment was carried out to assess the effects of arsenic and aquatic fern (Marsilea minuta L.), when applied as a phytoremediator, on the nutrient content (nitrogen, phosphorus, and potassium) of rice. Two sets of pot experiments were conducted in the net house on rice (Oryza sativa L.) together with aquatic fern (M. minuta) and on aquatic fern (M. minuta) alone where soils were treated with 1 mg/L As-solution at 80% arsenite and 20% arsenate. No significant difference was found in the nitrogen, phosphorus, and potassium concentrations of rice, in the absence of arsenic, whether grown in the presence of M. minuta or not. The uptake of total nitrogen, phosphorus, and potassium was found to be 36%, 23%, and 22% more, respectively in rice plants treated with M. minuta and arsenic over the control treatment, although the results were statistically insignificant. However, a significant negative relationship was found between arsenic and root nitrogen (P-value of 0.0017) when grown together with arsenic and M. minuta. A significant positive relationship was found between arsenic and shoot phosphorus (P-value of 0.0025) as well as arsenic and shoot and root potassium (P-values were 0.0045 and 0.0115, respectively). The results indicate that Marsilea minuta might be used as a phytoremediator of As together with rice plants.Bangladesh J. Agril. Res. 43(2): 211-218, June 2018

scholarly journals Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicle ◽  
Migratory Bird ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Detection Methods ◽  
Single Shot ◽  
Convolutional Network ◽  
Aerial Vehicle ◽  
Bird Detection

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.

scholarly journals New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (24) ◽  
pp. 4170
Author(s):  
Pengfei Chen ◽  
Fangyong Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Estimation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Green Band ◽  
Validation Data ◽  
Texture Parameters ◽  
Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

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