Detection of citrus diseases using a fuzzy neural network
The objective is to use AI techniques to build a citrus image recognition system and to produce an integrated program that will assist plant protection professionals in determining whether the disease is infected and early detection for the purpose of taking the necessary preventive measures and red...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | article |
| Lenguaje: | AR EN |
| Publicado: |
College of Education for Pure Sciences
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/499960532ae24e25b782095ca869f819 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | The objective is to use AI techniques to build a citrus image recognition system and to produce an integrated program that will assist plant protection professionals in determining whether the disease is infected and early detection for the purpose of taking the necessary preventive measures and reducing its spread to other plants. In this research, the RBF and FRBF networks were used and applied to 830 images, to detect whether citrus fruits were healthy or ill. At first, the preprocessing of these images was done, and they were reduced to 250 x 250 pixels, and the features were extracted from them using the co-occurrence matrix method (GLCM) after setting the gray level at 8 gradients and 1 pixel distance, 21 statistical features were derived, and then these features were introduced to RBF after determine the number of input layer nodes by 21 , 20 for the hidden layer and 1 node for output layer, the centers were randomly selected from the training data and the weights were also randomly selected and trained using the Pseudo Inverse method. The RBF network was hybridized with the fuzzy logic using the FCM method, the fuzziness parameter = 2.3 was selected, and a new network called FRBF was acquired. These networks were trained and tested in training data (660 images) and testing (170 images) for citrus fruits. The detection rate was then calculated, and the results showed that the (FRBF) had a higher accuracy of 98.24% compared to RBF of 94.71%. |
|---|