Improving ORC methods and hotspot detection with the usage of aerial image metrology

Author(s):  
François Weisbuch ◽  
Thomas Thaler ◽  
Ute Buttgereit ◽  
Christian Stötzel ◽  
Thomas Zeuner
2017 ◽  
Author(s):  
Ao Chen ◽  
Yee Mei Foong ◽  
Thomas Thaler ◽  
Ute Buttgereit ◽  
Angeline Chung ◽  
...  

2016 ◽  
Author(s):  
Seolchong Hwang ◽  
Sungha Woo ◽  
Heeyeon Jang ◽  
Youngmo Lee ◽  
Sangpyo Kim ◽  
...  

Author(s):  
Ao Chen ◽  
Yee Mei Foong ◽  
Angeline Chung ◽  
Peter De Bisschop ◽  
Thomas Thaler ◽  
...  
Keyword(s):  

2010 ◽  
Vol 18 (14) ◽  
pp. 14467 ◽  
Author(s):  
Fernando Brizuela ◽  
Sergio Carbajo ◽  
Anne Sakdinawat ◽  
David Alessi ◽  
Dale H. Martz ◽  
...  

Author(s):  
Mohsen Karimi ◽  
Haidar Samet ◽  
Teymoor Ghanbari ◽  
Ehsan Moshksar

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.


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