NDLI: Image Processing Performance Assessment Using Crop Weed Competition Models

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Mapping Potential Crop Management Zones within Fields: Use of Yield-map Series and Patterns of Soil Physical Properties Identified by Electromagnetic Induction Sensing

Image Processing Performance Assessment Using Crop Weed Competition Models

Processing of Yield Map Data

Modelling Wild-Oat Density in Terms of Soil Factors: A Machine Learning Approach

Precision Agriculture : Volume 6, Issue 1, February 2005

Precision Agriculture : Volume 5

Precision Agriculture : Volume 4

Precision Agriculture : Volume 3

Precision Agriculture : Volume 2

Precision Agriculture : Volume 1

Image Processing Performance Assessment Using Crop Weed Competition Models

Content Provider	Springer Nature Link
Author	Onyango, Christine Marchant, John Grundy, Andrea Phelps, Kath Reader, Richard
Copyright Year	2005
Abstract	Precision treatment of both crops and weeds requires the accurate identification of both types of plant. However both identification and treatment methods are subject to error and it is important to understand how misclassification errors affect crop yield. This paper describes the use of a conductance growth model to quantify the effect of misclassification errors caused by an image analysis system.Colour, morphology and knowledge about planting patterns have been combined, in an image analysis algorithm, to distinguish crop plants from weeds. As the crop growth stage advances, the algorithm is forced to trade improved crop recognition for reduced weed classification. Depending on the chosen method of weed removal, misclassification may result in inadvertent damage to the crop or even complete removal of crop plants and subsequent loss of yield. However incomplete removal of weeds might result in competition and subsequent yield reduction. The plant competition model allows prediction of final crop yield after weed or crop removal. The competition model also allows the investigation of the impact on yield of misclassification in the presence of both aggressive and benign weed types. The competition model and the image analysis algorithm have been linked successfully to investigate a range of misclassification scenarios in scenes containing cabbage plants.
Starting Page	183
Ending Page	192
Page Count	10
File Format	PDF
ISSN	13852256
Journal	Precision Agriculture
Volume Number	6
Issue Number	2
e-ISSN	15731618
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	image analysis crop/weed classification competition models Meteorology/Climatology Remote Sensing/Photogrammetry Agriculture Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Agricultural and Biological Sciences

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