- Tytuł:
- Parallelization of Concise Convolutional Neural Networks for Plant Classification
- Autorzy:
-
Sembiring, Arnes
Away, Yuwaldi
Arnia, Fitri
Muharar, Rusdha - Powiązania:
- https://bibliotekanauki.pl/articles/2202377.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polskie Towarzystwo Inżynierii Ekologicznej
- Tematy:
-
parallelisation
concise CNN
plant classification
multi-scale CNN
convolutional neural network - Opis:
- Monitoring the agricultural field is the key to preventing the spread of disease and handling it quickly. The computer-based automatic monitoring system can meet the needs of large-scale and real-time monitoring. Plant classifiers that can work quickly in computer with limited resources are needed to realize this monitoring system. This study proposes convolutional neural network (CNN) architecture as a plant classifier based on leaf imagery. This architecture was built by parallelizing two concise CNN channels with different filter sizes using the addition operation. GoogleNet, SqueezeNet and MobileNetV2 were used to compare the performance of the proposed architecture. The classification performance of all these architectures was tested using the PlantVillage dataset which consists of 38 classes and 14 plant types. The experimental results indicated that the proposed architecture with a smaller number of parameters achieved nearly the same accuracy as the comparison architectures. In addition, the proposed architecture classified images 5.12 times faster than SqueezeNet, 8.23 times faster than GoogleNet, and 9.4 times faster than MobileNetV2. These findings suggest that when implemented in the agricultural field, the proposed architecture can be a reliable and faster plant classifier with fewer resources.
- Źródło:
-
Journal of Ecological Engineering; 2023, 24, 2; 61--71
2299-8993 - Pojawia się w:
- Journal of Ecological Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł