- Tytuł:
- Plant disease detection using ensembled CNN framework
- Autorzy:
-
Mondal, Subhash
Banerjee, Suharta
Mukherjee, Subinoy
Sengupta, Diganta - Powiązania:
- https://bibliotekanauki.pl/articles/27312905.pdf
- Data publikacji:
- 2022
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
convolutional neural network
disease detection
ResNet-50
VGG-19
InceptionV3 - Opis:
- Agriculture exhibits the prime driving force for the growth of agro-based economies globally. In agriculture, detecting and preventing crops from the attacks of pests is a primary concern in today’s world. The early detection of plant disease becomes necessary in order to avoid the degradation of the yield of crop production. In this paper, we propose an ensemble-based convolutional neural network (CNN) architecture that detects plant disease from the images of a plant’s leaves. The proposed architecture considers CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). This approach helped us build a generalized model for disease detection with an accuracy of 97.9% under test conditions.
- Źródło:
-
Computer Science; 2022, 23 (3); 321--333
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł