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Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh
Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh
Theses: Doctorates and Masters
Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection.
Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different …
Development Of An Optical Sensor For Real-Time Weed Detection Using Laser Based Spectroscopy, Arie Jacobus Paap
Development Of An Optical Sensor For Real-Time Weed Detection Using Laser Based Spectroscopy, Arie Jacobus Paap
Theses: Doctorates and Masters
The management of weeds in agriculture is a time consuming and expensive activity, including in Australia where the predominant strategy is blanket spraying of herbicides. This approach wastes herbicide by applying it in areas where there are no weeds. Discrimination of different plant species can be performed based on the spectral reflectance of the leaves. This thesis describes the development of a sensor for automatic spot spraying of weeds within crop rows. The sensor records the relative intensity of reflected light in three narrow wavebands using lasers as an illumination source.
A prototype weed sensor which had been previously developed …