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DC Field | Value | Language |
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dc.contributor.author | Raman, Sundaresan | - |
dc.date.accessioned | 2023-01-05T04:23:06Z | - |
dc.date.available | 2023-01-05T04:23:06Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.uri | https://dl.acm.org/doi/fullHtml/10.1145/3493700.3493719 | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8312 | - |
dc.description.abstract | Foliar diseases account for upto 40% to the loss of annual crop yield worldwide. This necessitates early detection of these diseases in order to prevent spread and reduce crop damage. The PlantVillage Dataset is the largest open-access database comprising 38 classes of healthy and diseased leaves. However this dataset contains images of leaves taken in a controlled environment which severely restricts the portability of models trained on this dataset to the real world. Motivated by the need to detect a variety of leaf diseases captured under diverse conditions and backgrounds, as is the case presently where many farmers do not have access to lab infrastructure or high-end cameras, we choose the PlantDoc dataset for our experiments. This dataset contains images comprising a subset of 27 classes of the PlantVillage dataset taken under different backgrounds and of varying resolutions. In this paper, we first present a new set of baselines for foliar disease classification using images taken in the field highlighting the inadequacy of current benchmarks. Secondly, we propose a Stacked Ensemble for Multiple Foliar Disease classification (SEMFD-Net), an ensemble model created by stacking a subset of our baseline models and a simple feed-forward neural network as our meta-learner which significantly outperforms the baselines. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ACM Digital Library | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Ensemble Models | en_US |
dc.subject | Image classification | en_US |
dc.subject | Plant Diseases | en_US |
dc.title | SEMFD-Net : A Stacked Ensemble for Multiple Foliar Disease Classification | en_US |
dc.type | Article | en_US |
Appears in Collections: | Department of Computer Science and Information Systems |
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