| Autor: |
Chowdary, M. Sivaram, Puviarasi, R. |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2587 Issue 1, p1-9, 9p |
| Abstrakt: |
The aim of this work is to calculate the accuracy in the identification of apple leaf disease using K-Nearest Neighbour (K-NN) Compared with Fuzzy logic framework. The data set contains 20 images collected from the seedbuzz website and these images are used for training and testing the predictive model in MATLAB. Statistical analysis is done using SPSS software. The sample size of two groups is calculated using the G power tool with pretest power of 0.8. The proposed system using K-NN achieved better mean accuracy of 94.770.304 and the sensitivity of 89.12 ± 0.496 followed by Fuzzy model produces 93.01 0.464 accuracy and the sensitivity of 86.99 1.047. The significance value for accuracy is 0.098 and for sensitivity 0.0613 which are obtained from statistical analysis in SPSS. The outcome of the study shows that the K-NN based model appears to be the better results in enhancing the accuracy of disease identification in apple leaves. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
