Automated Detection of Branch Dimensions in Woody Skeletons of Fruit Tree Canopies

2011 ◽  
Vol 77 (3) ◽  
pp. 229-240 ◽  
Author(s):  
Alexander Bucksch ◽  
Stefan Fleck
Keyword(s):  
Fruit Tree ◽  
Automated Detection ◽  
Tree Canopies

Short-range lidar measurement of top fruit tree canopies for pesticide applications research in the United Kingdom

1997 ◽  
Author(s):  
Peter J. Walklate ◽  
G. M. Richardson ◽  
D. E. Baker ◽  
P. A. Richards ◽  
J. V. Cross
Keyword(s):  
United Kingdom ◽  
Short Range ◽  
Fruit Tree ◽  
Lidar Measurement ◽  
The United Kingdom ◽  
Tree Canopies

A MECHANICAL TRAP FOR SAMPLING POPULATIONS OF SMALL, ACTIVE INSECTS

1977 ◽  
Vol 109 (10) ◽  
pp. 1405-1407 ◽  
Author(s):  
D.R. Menzies ◽  
E.A.C. Hagley
Keyword(s):  
Conventional Method ◽  
Sampling Method ◽  
Fruit Tree ◽  
Tree Canopy ◽  
Resident Population ◽  
Rapid Movement ◽  
Tree Canopies

It is difficult to estimate numbers of small active insects, particularly predators, in fruit-tree canopies because of the frequent and rapid movement of these species. The conventional method of sampling such populations requires tapping the branch to dislodge the insects which fall to a cloth tray from which they are recovered and counted. However, many species take to flight very rapidly and escape before identification is possible. A sampling method was required which would rapidly enclose a volume of the canopy without disturbing the resident population, thereby eliminating inaccuracies in the sample obtained. This note describes a remotely operated mechanical trap which can be placed on a limb in the tree canopy. The insects thus trapped can be immobilized, counted, and identified.

Tests of a Uranyl Oxalate Light Integrator for Use in Fruit Tree Canopies

1970 ◽  
Vol 7 (3) ◽  
pp. 639 ◽  
Author(s):  
D. H. Maggs ◽  
D. McE. Alexander
Keyword(s):  
Fruit Tree ◽  
Tree Canopies ◽  
Uranyl Oxalate

Automated detection of epithelial changes in colorectal carcinoma

2012 ◽  
Vol 50 (05) ◽  
Author(s):  
G Valcz ◽  
I Bándi ◽  
B Wichmann ◽  
A Patai ◽  
D Szabó ◽  
...  
Keyword(s):  
Colorectal Carcinoma ◽  
Automated Detection

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Matoa (Pometia Pinata Foster): Local Fruit Papua and Strategy Development

2010 ◽  
Vol 7 (1) ◽  
Author(s):  
Julius D. Nugroho
Keyword(s):  
Genetic Resources ◽  
Fruit Tree ◽  
Breeding Program ◽  
Strategy Development ◽  
Local Market ◽  
Font Size ◽  
Breeding Programs ◽  
Irian Jaya ◽  
Forest Exploitation ◽  
Program Strategy

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