Lagrange multipliers and optimality
SIAM Review
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Principles of Digital Image Processing: Core Algorithms
Principles of Digital Image Processing: Core Algorithms
A computer vision approach for weeds identification through Support Vector Machines
Applied Soft Computing
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Aquatic weed automatic classification using machine learning techniques
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
A hybrid WA-CPSO-LSSVR model for dissolved oxygen content prediction in crab culture
Engineering Applications of Artificial Intelligence
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Site-specific weed management can reduce the amount of herbicides used in comparison to classical broadcast applications. The ability to apply herbicides on weed patches within the field requires automation. This study focuses on the automatic detection of different species with imaging sensors. Image processing algorithms determine shape features for the plants in the images. With these shape descriptions classification algorithms can be trained to identify the weed and crop species. Since weeds differ in their economic loss due to their yield effect and are controlled by different herbicides, it is necessary to correctly distinguish between the species. Image series of different measurements with plant samples at different growth stages were analysed. For the classification a sequential classification approach was chosen, involving three different support vector machine (SVM) models. In a first step groups of similar plant species were successfully identified (monocotyledons, dicotyledons and barley). Distinctions within the class of dicotyledons proved to be particularly difficult. For that purpose species in this group were subject to a second and third classification step. For each of these steps different features were found to be most important. Feature weighting was done with the RELIEF-F algorithm and SVM-Weighting. The focus was on the early identification of the two most harmful species Cirsium arvense and Galium aparine, with optimal accuracy than using a non-sequential classification approach. An overall classification accuracy of 97.7% was achieved in the first step. For the two subsequent classifiers accuracy rates of 80% and more were obtained for C. arvense and G. aparine.