Strawberry Sickness Identification Through Leaf Images Using Convolutional Neural Network
ALDI RAMDANI
Informasi Umum
Kode
21.04.3227
Klasifikasi
006.37 - Computer Vision
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Image Processing - Computer Vision
Dilihat
334 kali
Informasi Lainnya
Abstraksi
Strawberry is a plant with high economic value and promising business prospects. A common problem in strawberry cultivation is that the seeds quickly get a disease. Some diseases like spot leaf, blight leaf, and scorch leaf can be detected from the leaf. Identifying strawberry diseases from its leaf can prevent damage to the fruit. We proposed a CNN Model to identifying strawberry diseases from its leaf. CNN is one of deep learning approaches that has been used in many previous studies to identifying fruit diseases. There are four different strawberry leaf types, healthy, scorch leaf, spot leaf, and leaf blight, in the proposed technique. Using ResNet-50 architecture for the model with 3600 images, the model achieves a prediction accuracy of 100% for spot leaf, 99% for blight leaf, 99% for scorch leaf, 100% for a healthy leaf. The proposed model provides a simple, reliable technique for identifying strawberry diseases.
Koleksi & Sirkulasi
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Pengarang
| Nama | ALDI RAMDANI |
| Jenis | Perorangan |
| Penyunting | Suyanto, Erwin Budi Setiawan |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2021 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
