203 lines
4.7 KiB
Markdown
203 lines
4.7 KiB
Markdown
# ✅ Update Summary - 4 Classes dengan Kelas Healthy
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## 📝 Yang Sudah Dilakukan
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### 1️⃣ Backend Updates
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#### rice_inference.py
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```python
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# Sebelum: 3 kelas
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CLASS_NAMES = ["Bacterialblight", "Brownspot", "Leafsmut"]
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# Sesudah: 4 kelas + Healthy
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CLASS_NAMES = ["Bacterialblight", "Brownspot", "Healthy", "Leafsmut"]
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```
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#### ClassificationController.php
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Added **Healthy disease info** dengan details:
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```php
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'Healthy' => [
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'name' => 'Daun Sehat',
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'severity' => 'Tidak Ada',
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'symptoms' => ['Tidak ada bercak atau lesi...'],
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'treatment' => ['Lanjutkan pemeliharaan rutin...']
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]
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```
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### 2️⃣ Mobile App Updates
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#### result_screen.dart
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- ✅ Conditional display: "Penyakit Terdeteksi" vs "Status Daun"
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- ✅ Warna dinamis: Merah untuk sakit, **Hijau untuk sehat**
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- ✅ Severity color handling untuk "Tidak Ada"
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---
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## 🎯 Langkah Selanjutnya (Untuk Anda)
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### STEP 1: Siapkan Dataset Healthy
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```
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rice leaf diseases dataset/
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├── Healthy/ ← PENTING: Folder ini harus ada
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│ ├── healthy_1.jpg
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│ ├── healthy_2.jpg
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│ ├── ... (minimal 100-200 foto)
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├── Bacterialblight/
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├── Brownspot/
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└── Leafsmut/
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```
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**Tips Dataset Healthy:**
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- Foto daun padi yang **benar-benar sehat** (no spot, no disease)
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- Beragam: sudut berbeda, pencahayaan berbeda
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- Clear resolution (224x224 min)
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- No tools/hands visible
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### STEP 2: Retrain Model
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**Open Jupyter:**
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```bash
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cd "d:\PROJECT TA\rice leaf diseases dataset"
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jupyter notebook rice_leaf_cnn_classification.ipynb
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```
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**Update notebook cell:**
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```python
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# Cell: Define Classes
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classes = ['Bacterialblight', 'Brownspot', 'Healthy', 'Leafsmut'] # ← Add Healthy
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IMG_SIZE = 224
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```
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**Jalankan cells berturut-turut:**
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1. Setup & Imports
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2. Define Classes (update ke 4 classes)
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3. Load Dataset
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4. Data Augmentation
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5. Model Building
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6. **Training** (tunggu selesai)
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7. Evaluation
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8. Save Model
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### STEP 3: Validasi Model
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```bash
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# Test Python script directly
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cd web_TA
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# Create test JSON
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$base64_image = "..." # base64 dari foto
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$json = @{image=$base64_image} | ConvertTo-Json
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# Test inference
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$json | python scripts/rice_inference.py classify --model-dir "../rice leaf diseases dataset"
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# Output harus include Healthy di predictions
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```
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### STEP 4: Test Mobile App
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1. **Foto Daun Sehat**
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- Upload → Harus detect "Daun Sehat" (green)
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- Severity: "Tidak Ada" (green badge)
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2. **Foto Daun Sakit**
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- Upload → Detect penyakit (red)
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- Severity: Sesuai tingkat (orange/red)
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3. **Multiple Photo**
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- No errors "gagal menghubungi model"
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- All requests succeed
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---
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## 📊 File yang Sudah Diupdate
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```
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✅ web_TA/scripts/rice_inference.py
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└─ Updated CLASS_NAMES (3→4 classes)
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✅ web_TA/app/Http/Controllers/ClassificationController.php
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└─ Added Healthy disease info in getDiseaseInfo()
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✅ mobile_TA/padi_app/lib/screen/result_screen.dart
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└─ Conditional UI untuk Healthy
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└─ Dynamic color based on predicted_class
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└─ Updated _getSeverityColor() handling
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📄 New: MODEL_RETRAINING_GUIDE.md
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└─ Complete training instructions
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```
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---
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## ✨ Fitur Baru
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### Mobile App - Healthy Class Support
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```dart
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// Sebelum: Always "Penyakit Terdeteksi:" with red color
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// Sesudah:
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if (predicted_class == 'Healthy') {
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Text("Status Daun:"), // Not "Penyakit"
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Text(disease_name, style: green) // Green, not red
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} else {
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Text("Penyakit Terdeteksi:"),
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Text(disease_name, style: red) // Red for disease
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}
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```
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### Backend - Complete Health Info
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```json
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{
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"predicted_class": "Healthy",
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"disease_info": {
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"name": "Daun Sehat",
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"severity": "Tidak Ada", // Not "High"/"Medium"
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"symptoms": ["Tidak ada bercak..."],
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"treatment": ["Lanjutkan pemeliharaan..."]
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}
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}
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```
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---
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## ⏱️ Estimated Time
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| Task | Duration |
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|------|----------|
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| Prepare Healthy dataset | 1-2 hours |
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| Data cleaning (remove duplicates) | 10 mins |
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| Model training (50 epochs) | 30 mins - 2 hours |
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| Model validation | 5 mins |
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| Mobile app testing | 15 mins |
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| **Total** | **2-4 hours** |
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---
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## 🚀 After Completing These Steps
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System akan siap dengan:
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- ✅ 4-class model (Bacterialblight, Brownspot, **Healthy**, Leafsmut)
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- ✅ Accurate healthy leaf detection
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- ✅ Beautiful UI untuk healthy status
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- ✅ No more intermittent errors (sudah fixed sebelumnya)
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- ✅ Production-ready classification
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---
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## 📖 Complete Training Guide
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**Full detailed instructions:** [MODEL_RETRAINING_GUIDE.md](MODEL_RETRAINING_GUIDE.md)
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Panduan lengkap includes:
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- Dataset preparation
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- Training steps detail
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- Validation commands
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- Troubleshooting
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- Performance tips
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---
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**Status**: ✅ Backend Updated | ⏳ Waiting for Model Retraining | ⏳ Mobile Testing
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*Ready to proceed with training?* Let me know jika ada pertanyaan!
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