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Articles by Soodeh Nikan
From blender to farm: Transforming controlled environment agriculture with synthetic data and SwinUNet for precision crop monitoring
4. Discussion The results of this study highlight the effectiveness of the SwinUNet architecture combined with deep domain confusion (DDC) techniques for segmenting ripe and unripe strawberries from synthetic training data. To contextualize these results, this approach can be compared with existing state-of-the-art segmentation models commonly used in agricultural applications.
Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks
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Classification Of Strawberry Diseases and Quality Using Different Machine Learning Methods
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 27 April 2024 / Approved: 10 May 2024 / Online: 10 May 2024 (16:51:08 CEST) Aghamohammadesmaeiletabforoosh, K.; Nikan, S.; Antonini, G.; Pearce, J. Classification Of Strawberry Diseases and Quality Using Different Machine Learning Methods. Preprints 2024, 2024050710. https://doi.org/10.20944/preprints202405.0710.v1 Aghamohammadesmaeiletabforoosh, K.; Nikan, S.; Antonini, G.; Pearce, J.
An Explainable Attention Zone Estimation for Level 3 Autonomous Driving
We applied GMM as the clustering method with EM optimization. An optimized convergence tolerance (tol) parameter determines the minimum change in the log-likelihood needed for the algorithm to converge. A smaller tol value leads to a more precise estimation of GMM parameters, at the cost of more iterations and computational resources. By adjusting tol, we can control the accuracy of the clustering model. Figure 11 shows the results for 13 different tol values, versus the GMM accuracy.
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