Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption

2007 ◽  
Vol 1 (1) ◽  
pp. 013547 ◽  
Author(s):  
Kishore C. Swain
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Remote Sensing Images ◽  
Low Altitude ◽  
Nitrogen Treatment

scholarly journals A Multi-View Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images

2016 ◽  
Vol 8 (5) ◽  
pp. 381 ◽  
Author(s):  
Zhenfeng Shao ◽  
Nan Yang ◽  
Xiongwu Xiao ◽  
Lei Zhang ◽  
Zhe Peng
Keyword(s):  
Remote Sensing ◽  
Point Cloud ◽  
Remote Sensing Images ◽  
Generation Algorithm ◽  
Dense Point ◽  
Low Altitude

scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 989-992 ◽  
Author(s):  
C. Yao ◽  
Y. Zhang ◽  
Y. Zhang ◽  
H. Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Precision Agriculture ◽  
Rapid Development ◽  
Reference Value ◽  
Remote Sensing Images ◽  
Training Samples ◽  
Traditional Classification ◽  
Crop Classification

With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

scholarly journals A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China

2021 ◽  
Vol 13 (6) ◽  
pp. 1221
Author(s):  
Haidong Zhang ◽  
Lingqing Wang ◽  
Ting Tian ◽  
Jianghai Yin
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Current Application ◽  
Rapid Acquisition ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Future Work

Precision agriculture relies on the rapid acquisition and analysis of agricultural information. An emerging method of agricultural monitoring is unmanned aerial vehicle low-altitude remote sensing (UAV-LARS), which possesses significant advantages of simple construction, strong mobility, and high spatial-temporal resolution with synchronously obtained image and spatial information. UAV-LARS could provide a high degree of overlap between X and Y during key crop growth periods that is currently lacking in satellite and remote sensing data. Simultaneously, UAV-LARS overcomes the limitations such as small scope of ground platform monitoring. Overall, UAV-LARS has demonstrated great potential as a tool for monitoring agriculture at fine- and regional-scales. Here, we systematically summarize the history and current application of UAV-LARS in Chinese agriculture. Specifically, we outline the technical characteristics and sensor payload of the available types of unmanned aerial vehicles and discuss their advantages and limitations. Finally, we provide suggestions for overcoming current limitations of UAV-LARS and directions for future work.

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 UAV Low-Altitude Remote Sensing for Precision Weed Management

2017 ◽  
Vol 32 (1) ◽  
pp. 2-6 ◽  
Author(s):  
Yanbo Huang ◽  
Krishna N. Reddy ◽  
Reginald S. Fletcher ◽  
Dean Pennington
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Weed Management ◽  
Infrared Imaging ◽  
Weed Species ◽  
Crop Fields ◽  
Sensing Technology ◽  
Weed Infestation ◽  
High Resolution Images ◽  
Low Altitude

AbstractPrecision weed management, an application of precision agriculture, accounts for within-field variability of weed infestation and herbicide damage. Unmanned aerial vehicles (UAVs) provide a unique platform for remote sensing of field crops. They are more efficient and flexible than manned agricultural airplanes in acquiring high-resolution images at low altitudes and low speeds. UAVs are more universal than agricultural aircraft, because the latter are used only in specific regions. We have developed and used UAV systems for red–green–blue digital and color–infrared imaging over crop fields to identify weed species, determine crop injury from dicamba at different doses, and detect naturally grown glyphosate-resistant weeds. This article presents remote sensing technologies for weed management and focuses on development and application of UAV-based low-altitude remote sensing technology for precision weed management. In particular, this article futher discusses the potential application of UAV-based plant-sensing systems for mapping the distributions of glyphosate-resistant and glyphosate-susceptible weeds in crop fields.

OPTIMAL OPERATION POINT IN 3D DCT- BASED LOSSY COMPRESSION OF COLOR AND MULTICHANNEL REMOTE SENSING IMAGES

2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lossy Compression ◽  
Optimal Operation ◽  
Remote Sensing Images
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