Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis

@article{Golzarian2011ClassificationOI,
  title={Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis},
  author={Mahmood Reza Golzarian and Ross A Frick},
  journal={Plant Methods},
  year={2011},
  volume={7},
  pages={28 - 28}
}
Wheat is one of the most important crops in Australia, and the identification of young plants is an important step towards developing an automated system for monitoring crop establishment and also for differentiating crop from weeds. In this paper, a framework to differentiate early narrow-leaf wheat from two common weeds from their digital images is developed. A combination of colour, texture and shape features is used. These features are reduced to three descriptors using Principal Component… 
Evaluation of bag-of-features (BoF) technique for weed management in sugarcane production
TLDR
This study clearly shows that the Bag-of-Features approach can provide important subsidies for the formulation of strategies for weed management, especially in sugarcane, for which the timing of weed control is crucial.
Detecting creeping thistle in sugar beet fields using vegetation indices
Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features
TLDR
It can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.
A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification
TLDR
This study reviews several image processing methods in the feature extraction of leaves and discusses certain machine learning classifiers for an analysis of different species of leaves.
Machine Vision Based Classification of Rice (Oryza Sativa L.) Cultivars Using Morphological, Chromatic and Textural Features of Seed Images
TLDR
It is indicated that morphological, chromatic as well as textural features play a vital role in identification of new varieties and distinguishing them to classify into similar groups.
An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video
The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes.
DEVELOPMENT OF A MACHINE VISION SYSTEM FOR WEED DETECTION DURING BOTH OF OFF-SEASON AND IN-SEASON IN BROADACRE NO-TILLAGE CROPPING LANDS
TLDR
Results show that the proposed weed detection methods are more suitable for the weed detection in the no-till age background than the existing methods and could be used as a powerful tool for the Weed control.
Finding local leaf vein patterns for legume characterization and classification
TLDR
This paper describes a procedure which can be useful to discover representative leaf vein patterns for each species or variety under analysis, and considers three legumes, namely red bean, white bean and soybean.
...
...

References

SHOWING 1-10 OF 65 REFERENCES
Colour and shape analysis techniques for weed detection in cereal fields
PA—Precision Agriculture: Computer-Vision-based Weed Identification under Field Conditions using Controlled Lighting
TLDR
Compared to other studies, the plant identification system presented is an improvement, especially considering that the experiments were carried out under field conditions and showed that colour features can help to increase the classification accuracy.
CLASSIFICATION OF WEED SPECIES USING COLOR TEXTURE FEATURES AND DISCRIMINANT ANALYSIS
TLDR
Between species discriminant analysis showed that the CCM texture statistics procedure was able to classify between five weed species and soil with an accuracy of 93% using hue and saturation statistics, only.
CLASSIFICATION OF BROADLEAF AND GRASS WEEDS USING GABOR WAVELETS AND AN ARTIFICIAL NEURAL NETWORK
A texture–based weed classification method was developed. The method consisted of a low–level Gabor wavelets–based feature extraction algorithm and a high–level neural network–based pattern
Color indices for weed identification under various soil, residue, and lighting conditions
Color slide images of weeds among various soils and residues were digitized and analyzed for red, green, and blue (RGB) color content. Red, green, and blue chromatic coordinates (rgb) of plants were
Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley
TLDR
A system for site-specific weed control in sugarbeet, maize, winter wheat, and winter barley is presented, including online weed detection using digital image analysis, computer-based decision making and global positioning systems (GPS)-controlled patch spraying.
...
...