Wheat leaf diseases classification and severity analysis using HT-CNN and Hex-D-VCC-based boundary tracing mechanism.
Environ Monit Assess
; 195(12): 1505, 2023 Nov 21.
Article
en En
| MEDLINE
| ID: mdl-37987888
Wheat is one among the significant crops for humans. Significant fungal illnesses of wheat are brought on by multiple pathogens. Wheat output could be enhanced by the early identification of wheat leaf disease. Thus, a novel hyperparameter tanh-based convolutional neural network (HT-CNN)-based wheat leaf disease prediction is proposed with its severity level. Here, initially, the red, green, and blue (RGB) images are converted into a hue saturation value (HSV) image. Next, the small probability space filtering is applied to the V component. Afterward, the contrast of the V component has been enhanced. The obtained HSV image is converted into the RGB image. Then, by employing weighted Canberra distance-based K-means (WCD-K means), the affected and normal regions are segmented. Next, the image is binarized. Afterward, for tracing a boundary around disease-affected region, the hex directional vertex chain code (Hex-D-VCC) is applied over the binarized image, and then the features are extracted. By employing baker's map-based Harris hawks optimization (BM-HHO), the optimal features are selected. For classifying disease, the selected features are further given into the HT-CNN, and the severity level is calculated to minimize the yield loss. As per the experimental result, the proposed model shows higher accuracy and efficacy when analogized to the other methods.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Triticum
/
Monitoreo del Ambiente
Límite:
Humans
Idioma:
En
Revista:
Environ Monit Assess
Asunto de la revista:
SAUDE AMBIENTAL
Año:
2023
Tipo del documento:
Article
País de afiliación:
India
Pais de publicación:
Países Bajos