BAHTIAR, ARIEF RAIS and Pranowo, . and Santoso, Albertus Joko and Juhariah, Jujuk (2020) Deep Learning Detected Nutrient Deficiency in Chili Plant. In: The 8th International Conference on Information Technology iCoiCT 2020. Telkom University, Bandung, Indonesia.
|
Text (Arief Rais Bahtiar; Pranowo Pranowo, Albertus Joko Santoso and Jujuk Juhariah)
30. Deep Learning Detected Nutrient Deficiency in Chili Plant.pdf Download (2MB) | Preview |
Abstract
Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%.
| Item Type: | Book Section |
|---|---|
| Subjects: | Teknik Informatika > Soft Computing |
| Divisions: | Fakultas Teknologi Industri > Teknik Informatika |
| Depositing User: | Editor 3 uajy |
| Date Deposited: | 01 Apr 2022 13:07 |
| Last Modified: | 01 Apr 2022 13:07 |
| URI: | http://e-journal.uajy.ac.id/id/eprint/26652 |
Actions (login required)
 |
View Item |
