scholarly journals Multispectral aerial image processing system for precision agriculture

2018 ◽  
Vol 16 (47) ◽  
Author(s):  
Samy Kharuf-Gutierrez ◽  
Rubén Orozco-Morales ◽  
Osmany de la C. Aday Díaz ◽  
Emma Pineda Ruiz
Keyword(s):  
Image Processing ◽  
Precision Agriculture ◽  
Processing System ◽  
Aerial Images ◽  
Aerial Image ◽  
Image Processing System ◽  
Vegetation Indexes ◽  
Hydric Stress ◽  
Processing Software ◽  
Number Of Stems

Cuban agriculture has the growing need to increase its productivity. To achieve this, precision agriculture can play a fundamental role. It is necessary to develop an image processing system able to process all the crops information and calculate vegetation indexes in a satisfactory way. This will entail in accurate measurements of the nitrogen lack, the hydric stress, and the vegetal strength, among other aspects, seeking to improve the accuracy in the care of these aspects. This document reports the results of an investigation pointed to develop a procedure for capturing and processing multispectral aerial images obtained from Unmanned Aerial Vehicles [UAV]. This procedure searched to measure the vegetation indexes of sugarcane crops that may be correlated with the level of vegetal strength, the number of stems, and the foliar mass per lot. We used a USENSE-X8 UAV together with a Sequoia multispectral sensor and the QGIS processing software. The procedure was experimentally validated.

High-speed aerial image processing system based on DSP

2001 ◽  
Author(s):  
Haiju Lei ◽  
Dehua Li ◽  
Hanping Hu ◽  
Zhaonan Guo
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Processing System ◽  
Aerial Image ◽  
Image Processing System

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

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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