Folgen
Ramesh Kestur
Ramesh Kestur
Adjunct Faculty and Senior Research Fellow, IIITB
Bestätigte E-Mail-Adresse bei iiitb.ac.in
Titel
Zitiert von
Zitiert von
Jahr
Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV
J Senthilnath, A Dokania, M Kandukuri, KN Ramesh, G Anand, SN Omkar
Biosystems engineering 146, 16-32, 2016
1722016
MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard
R Kestur, A Meduri, O Narasipura
Engineering Applications of Artificial Intelligence 77, 59-69, 2019
1662019
Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods
J Senthilnath, M Kandukuri, A Dokania, KN Ramesh
Computers and Electronics in Agriculture 140, 8-24, 2017
1652017
Detection of the power lines in UAV remote sensed images using spectral-spatial methods
R Bhola, NH Krishna, KN Ramesh, J Senthilnath, G Anand
Journal of environmental management 206, 1233-1242, 2018
1062018
UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle
MM Ramesh Kestur, Shariq Farooq,Rameen Abdal, Emad Mehraj,Omkar Narasipura
Journal of applied remote sensing 12 (1), 2018
912018
Tree crown detection, delineation and counting in uav remote sensed images: A neural network based spectral–spatial method
R Kestur, A Angural, B Bashir, SN Omkar, G Anand, MB Meenavathi
Journal of the Indian Society of Remote Sensing 46, 991-1004, 2018
482018
Detection of Rows in Agricultural Crop Images Acquired by Remote Sensing from a UAV
KN Ramesh, N Chandrika, SN Omkar, M M B, V Rekha
International Journal of Image, Graphics and Signal Processing(IJIGSP) 8 (11 …, 2016
292016
Automatic detection of powerlines in UAV remote sensed images
KN Ramesh, AS Murthy, J Senthilnath, SN Omkar
2015 International Conference on Condition Assessment Techniques in …, 2015
172015
Mango Tree Net--A fully convolutional network for semantic segmentation and individual crown detection of mango trees
VA Gurumurthy, R Kestur, O Narasipura
arXiv preprint arXiv:1907.06915, 2019
142019
IoT based Data Sensing System for AutoGrow, an Autonomous greenhouse System for Precision Agriculture
P Patil, R Kestur, M Rao, C Aswath
2023 IEEE Applied Sensing Conference (APSCON), 1-3, 2023
132023
Unmanned aerial system technologies for pesticide spraying
R Kestur, SN Omkar, S Subhash
Innovative Pest Management Approaches for the 21st Century: Harnessing …, 2020
132020
SIFT-FANN: An efficient framework for spatio-spectral fusion of satellite images
KK Rai, A Rai, K Dhar, J Senthilnath, SN Omkar, KN Ramesh
Journal of the Indian Society of Remote Sensing 45, 55-65, 2017
92017
Performance Analysis Of Set Partitioning In Hierarchical Trees (Spiht) Algorithm For A Family Of Wavelets Used In Color Image Compression
AS Murthy, KN Ramesh, YA Mamatha
ICTACT Journal On Image And Video Processing 5 (02), 2014
42014
MangoGAN: a general adversarial network-based deep learning architecture for mango tree crown detection
R Kestur, A Kulkarni, R Bhaskar, P Sreenivasa, D Dhanya Sri, ...
Journal of Applied Remote Sensing 16 (1), 014527-014527, 2022
22022
Vegetation mapping of a tomato crop using multilayer perceptron (MLP) neural network in images acquired by remote sensing from a UAV
R Kestur, MB Meenavathi
International Journal of Computer Applications 182 (13), 13-17, 2018
22018
Mango Tree Net--A fully convolutional network for semantic segmentation and individual crown detection of mango trees
V Agaradahalli Gurumurthy, R Kestur, O Narasipura
arXiv e-prints, arXiv: 1907.06915, 2019
12019
Road extraction in RGB images acquired by Low Altitude Remote Sensing from an Unmanned Aerial Vehicle, A Neural Network Based Approach
AN Ramesh Kestur, Yogitha, VM Ravi, O SN, MB Meenavath
ACRS2017: 38th Asian conference on remote sensing, 2018
2018
BANANA-SEG, A FULLY CONVOLUTIONAL DEEP NEURAL NETWORK FOR BANANA TREE CROWN MAPPING IN AERIAL IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV)
R Kestur, MB Meenavathi
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–18