Ratnayake, Upadya, Somasiri, Nalinda 
ORCID: https://orcid.org/0000-0001-6311-2251, Ganesan, Swathi ORCID: https://orcid.org/0000-0002-6278-2090 and Pokhrel, Sangita ORCID: https://orcid.org/0009-0008-2092-7029
(2023)
Enhancing CNN Models with Data Augmentation for Accurate Fertilizer Deficiencies and Diseases Identification in Paddy Crops.
In:
International Conference on Business Innovation 2023 (ICOBI 2023).
ICOBI, pp. 575-582
Abstract
The current study focused on developing an algorithm for the detection of diseases and fertilizer deficiencies in paddy crops (i.e., good or bad paddy crops) using image detection. The dataset for this research was generated based on images obtained from the internet and real-life images of paddy fields obtained from several parts of Sri Lanka. Once data preprocessing was completed with the use of steps such as data augmentation, the CNN algorithm and TensorFlow framework in machine learning were used to make predictions on the dataset. The acquired dataset produced an average of 99% accuracy while training. This led to successful predictions through the trained model.
| Item Type: | Book Section |
|---|---|
| Status: | Published |
| School/Department: | London Campus |
| URI: | https://ray.yorksj.ac.uk/id/eprint/9644 |
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