University of Twente Student Theses
Enhancing Early Disease Diagnosis: Analysis of a Cassava Plant Dataset
Yuceturk, Salih Eren (2023) Enhancing Early Disease Diagnosis: Analysis of a Cassava Plant Dataset.
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| Abstract: | Smart agriculture applications have become a common way of letting farmers conduct basic, but timely intervention on their crops. This can save entire regions from crop failure. Areas dependent on subsistence farming, and high in population density are under pronounced risk of unexpected food shortages. This application is still new and more data is required to expand our early reactive capabilities against crop disease. Certain diseases can become fatal for a plant even before symptoms are visible, and early diagnosis can help mitigate crop loss. In this paper, we aim to analyse a dataset of cassava plants. This study contributes to the field of early plant disease diagnosis by improving our understanding of the utility of spectral data in classifying crop diseases. We propose two goals - to investigate and analyze the dataset, and to build, and evaluate supervised models to classify the crop diseases. This study will consider research questions related to disease growth, the usefulness of spectral readings for early diagnosis, and the impact of different classifiers on disease classification. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 48 agricultural science, 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/96094 |
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