DEVELOPMENT OF RAPID LOW-COST LARS PLATFORM FOR OIL PALM PLANTATION

2015 ◽  
Vol 77 (20) ◽  
Author(s):  
Nasruddin Abu Sari ◽  
A Ahmad ◽  
MY Abu Sari ◽  
S Sahib ◽  
AW Rasib
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Precision Agriculture ◽  
Low Cost ◽  
Digital Cameras ◽  
Large Area ◽  
Oil Palm Plantation ◽  
Personal Remote Sensing ◽  
Path Distance ◽  
Low Altitude

The need to produce high temporal remote sensing imagery for supporting precision agriculture in oil palm deserves a new low-altitude remote sensing (LARS) technique. Consumer over the shelf unmanned aerial vehicles (UAV) and digital cameras have the potential to serve as Personal Remote Sensing Toolkits which are low-cost, efficient, rapid and safe. The objectives of this study were to develop and test a new technique to rapidly capturing nadir images of large area oil palm plantation (1 km2 ~ 4 km2). Using 5 different multi-rotor UAV models several imagery missions were carried out. Multi-rotors were chosen as a platform due to its vertical take-off and landing (VTOL) feature. Multi-rotor’s VTOL was crucial for imagery mission success. Post processing results showed that for an area of 1 km2, it needs 2 to 6 sorties of quad-rotor UAV with 4000x3000 pixel digital cameras flying at altitude of 120m above ground level and an average of 50m cross-path distance. The results provide a suitability assessment of low-cost digital aerial imagery acquisition system. The study has successfully developed a decent workhorse quad-rotor UAV for Rapid Aerial Photogrammetry Imagery and Delivery (RAPID) in oil palm terrain. Finally we proposed the workhorse UAV as Low-Altitude Personal Remote Sensing (LAPERS) basic founding element.

scholarly journals Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

2020 ◽  
Vol 12 (18) ◽  
pp. 3030
Author(s):  
Ram Avtar ◽  
Stanley Anak Suab ◽  
Mohd Shahrizan Syukur ◽  
Alexius Korom ◽  
Deha Agus Umarhadi ◽  
...  
Keyword(s):  
Oil Palm ◽  
Precision Agriculture ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Palm Tree ◽  
Flight Altitude ◽  
Biophysical Parameters ◽  
Oil Palm Plantation ◽  
Red Edge

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area.

scholarly journals Visual Attitude Estimation for Low-Cost Personal Remote Sensing Systems

2011 ◽  
Author(s):  
Tobias Fromm ◽  
Long Di ◽  
YangQuan Chen ◽  
Holger Voos
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
Fault Tolerant ◽  
Low Cost ◽  
Sufficient Accuracy ◽  
Sensing Systems ◽  
Personal Remote Sensing ◽  
Remote Sensing Systems ◽  
And Performance ◽  
The Moment

Remote Sensing using unmanned aerial vehicles (UAV) is gathering a lot of attention at the moment by researchers and developers, especially in terms of low-cost aircrafts which still maintain sufficient accuracy and performance. This paper introduces a low-cost approach to increase airworthiness by using a forward-looking camera to estimate the attitude of a UAV. It not only focuses on using machine learning to classify ground and sky, but also uses image processing and software engineering methods to make it fault-tolerant and really applicable on a miniature UAV. Additionally, it is able to interface with an autopilot framework to being used productively on flight missions.

Improved Architecture Designs for a Low Cost Personal Remote Sensing Platform: Flight Control and Safety

2011 ◽  
Author(s):  
Calvin Coopmans ◽  
Long Di ◽  
Austin Jensen ◽  
Aaron A. Dennis ◽  
YangQuan Chen
Keyword(s):  
Remote Sensing ◽  
Flight Control ◽  
Large Scale ◽  
Current System ◽  
Low Cost ◽  
Flight Test ◽  
Unmanned Aircraft ◽  
Personal Remote Sensing ◽  
Sensing Platform ◽  
Fail Safe

Remote sensing is a field traditionally dominated by expensive, large-scale operations. This paper presents our efforts to improve our unmanned aircraft (UA) platforms for low-cost personal remote sensing purposes. Safety concerns are first emphasized regarding the local airspace and multiple fail-safe features are shown in the current system. Then the AggieAir unmanned system architecture is briefly described including the Paparazzi UA autopilot, AggieAir JAUS implementation, AggieNav navigation unit and payload integration. Some preliminary flight test results and images acquired using an example thermal IR payload system are also shown. Finally Multi-UAV and heterogeneous platform capabilities are discussed with respect to their applications. Based on our approaches on the new architecture design, personal remote sensing on smaller-scale operations can be more beneficial and common.

scholarly journals Analysis of Forest Function Transfer to Oil Palm Plantation in Parenggean District with the Remote Sensing Approach and Geographic Information System

2019 ◽  
Vol 3 (2) ◽  
pp. 217-223
Author(s):  
Marboles Kundrat ◽  
Frederik Samuel Papilaya
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Systems Approach ◽  
Research Area ◽  
Geographic Information ◽  
Forest Conversion ◽  
Oil Palm Plantation ◽  
Forest Land Cover ◽  
Forest Function ◽  
Function Transfer

The island of Kalimantan is one of the islands that has a vast forest. Kalimantan Island is also the most important island for Indonesia, even the world. Parenggean is one of the sub-districts located in Kotawaringin Timur Regency, Central Kalimantan Province. Parenggean sub-district with an area of 493.15 km² is one of the sub-districts in East Kotawaringin Regency which has a very large oil palm plantation. This study will present data on the amount of forest land cover that has been converted. To get extensive forest conversion, this research uses the Remote Sensing and Geographic Information Systems approach. The result of research this proves there have been over the function forests became oil palm plantation in Parenggean District. The area of ​​forest that was converted into oil palm plantation in the research area is 5,143.15 hectares in 1990-2000 and 17,560.45 hectares in 2000-2010.  

Oil palm plantation mapping from high-resolution remote sensing images using deep learning

2019 ◽  
Vol 41 (5) ◽  
pp. 2022-2046 ◽  
Author(s):  
Runmin Dong ◽  
Weijia Li ◽  
Haohuan Fu ◽  
Lin Gan ◽  
Le Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Oil Palm ◽  
Remote Sensing Images ◽  
Oil Palm Plantation

scholarly journals DronAway: A Proposal on the Use of Remote Sensing Drones as Mobile Gateway for WSN in Precision Agriculture

2020 ◽  
Vol 10 (19) ◽  
pp. 6668
Author(s):  
Laura García ◽  
Lorena Parra ◽  
Jose M. Jimenez ◽  
Jaime Lloret ◽  
Pedro V. Mauri ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Low Cost ◽  
Sensor Nodes ◽  
Flying Height ◽  
World Population ◽  
Node Density ◽  
Gateway Node ◽  
Crop Monitoring ◽  
Time Required

The increase in the world population has led to new needs for food. Precision Agriculture (PA) is one of the focuses of these policies to optimize the crops and facilitate crop management using technology. Drones have been gaining popularity in PA to perform remote sensing activities such as photo and video capture as well as other activities such as fertilization or scaring animals. These drones could be used as a mobile gateway as well, benefiting from its already designed flight plan. In this paper, we evaluate the adequacy of remote sensing drones to perform gateway functionalities, providing a guide for choosing the best drone parameters for successful WiFi data transmission between sensor nodes and the gateway in PA systems for crop monitoring and management. The novelty of this paper compared with existing mobile gateway proposals is that we are going to test the performance of the drone that is acting as a remote sensing tool to carry a low-cost gateway node to gather the data from the nodes deployed on the field. Taking this in mind, simulations of different scenarios were performed to determine if the data can be transmitted correctly or not considering different flying parameters such as speed (from 1 to 20 m/s) and flying height (from 4 to 104 m) and wireless sensor network parameters such as node density (1 node each 60 m2 to 1 node each 5000 m2) and antenna coverage (25 to 200 m). We have calculated the time that each node remains with connectivity and the time required to send the data to estimate if the connection will be bad, good, or optimal. Results point out that for the maximum node density, there is only one combination that offers good connectivity (lowest velocity, the flying height of 24 m, and antenna with 25 m of coverage). For the other node densities, several combinations of flying height and antenna coverage allows good and optimal connectivity.

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