Author Identifier

Saman Akbarzadeh

Date of Award


Document Type



Edith Cowan University

Degree Name

Doctor of Philosophy


School of Science

First Supervisor

Professor Kamal Alameh

Second Supervisor

Dr Selam Ahderom


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 wavelengths. The results of this work show that the discrimination performance of the Gaussian kernel SVM algorithm, with either raw reflected intensities or NDVI values being used as inputs, provides better discrimination accuracy than the conventional discrete NDVI-based aggregation algorithm.

Then, we investigate a fast statistical method for CNN parameter optimization, which can be applied in many CNN applications and provides more explainable results. This study specifically applies Taguchi based experimental designs for network optimization in a basic network, a simplified inception network and a simplified Resnet network, and conducts a comparison analysis to assess their respective performance and then to select the hyper parameters and networks that facilitate faster training and provide better accuracy. Results show that, for all investigated CNN architectures, there is a measurable improvement in accuracy in comparison with un-optimized CNNs, and that the Inception network yields the highest improvement (~ 6%) in accuracy compared to simple CNN (~ 5%) and Resnet CNN counterparts (~ 2%).

Aimed at achieving weed-crop classification in real-time at high speeds, while maintaining high accuracy, the algorithms are uploaded on both a small embedded NVIDIA Jetson TX1 board for real-time precision agricultural applications, and a larger high throughput GeForce GTX 1080Ti board for aerial crop analysis applications. Experimental results show that for a simplified CNN algorithm implemented on a Jetson TX1 board, an improvement in detection speed of thirty times (60 km/hr) can be achieved by using spectral reflectance data rather than imaging data. Furthermore, with an Inception algorithm implemented on a GeForce GTX 1080Ti board for aerial weed detection, an improvement in detection speed of 11 times (~2300 km/hr) can be achieved, while maintaining an adequate detection accuracy above 80%. These high speeds are attained by reducing the data size, choosing spectral components with high information contents at lower resolution, pre-processing efficiently, optimizing the deep learning networks through the use of simplified faster networks for feature detection and classification, and optimizing computational power with available power and embedded resources, to identify the best fit hardware platforms.