Machine Learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Computers and Electronics in Agriculture
A new vision-based approach to differential spraying in precision agriculture
Computers and Electronics in Agriculture
A cognitive vision approach to early pest detection in greenhouse crops
Computers and Electronics in Agriculture
Improving weed pressure assessment using digital images from an experience-based reasoning approach
Computers and Electronics in Agriculture
Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop
Engineering Applications of Artificial Intelligence
Analysis of natural images processing for the extraction of agricultural elements
Image and Vision Computing
A computer vision approach for weeds identification through Support Vector Machines
Applied Soft Computing
Review: Sensing technologies for precision specialty crop production
Computers and Electronics in Agriculture
Original paper: Real-time image processing for crop/weed discrimination in maize fields
Computers and Electronics in Agriculture
Unsupervized data-driven partitioning of multiclass problems
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Identification of nine Iranian wheat seed varieties by textural analysis with image processing
Computers and Electronics in Agriculture
Detection and classification of areca nuts with machine vision
Computers & Mathematics with Applications
Evaluation of a new hybrid algorithm for highly imbalanced classification problems
International Journal of Hybrid Intelligent Systems
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We explore the feasibility of implementing fast and reliable computer-based systems for the automatic identification of weed seeds from color and black and white images. Seeds size, shape, color and texture characteristics are obtained by standard image-processing techniques, and their discriminating power as classification features is assessed. These investigations are performed on a database much larger than those used in previous studies, containing 10,310 images of 236 different weed species. We consider the implementation of a simple Bayesian approach (naive Bayes classifier) and (single and bagged) artificial neural network systems for seed identification. Our results indicate that the naive Bayes classifier based on an adequately selected set of classification features has an excellent performance, competitive with that of the comparatively more sophisticated neural network approach. In addition, we discuss the possibility of using only morphological and textural characteristics as classification features, which would reduce the operational complexity and hardware cost of a commercial system since they can be obtained from black and white images. We find that, under particular operational conditions, this would result in a relatively small loss in performance when compared to the implementation based on color images.