Development of an AI System for Plant Disease Detection
Phytophthora on tomatoes under favorable weather destroys 80% of the crop in 10 days. The problem is not that the disease is incurable — the problem is that by the time an agronomist visually notices symptoms, the infection has already spread to 15–20% of the leaf surface. We develop computer vision systems that detect infection at 3–5% leaf area, providing a 3–4 day window for treatment. Our years of experience and accuracy guarantee of at least 90% allow agricultural holdings to reduce crop losses by 30–50%. The system pays for itself in less than one season due to reduced crop losses. Savings on plant protection products can reach 40%. We have already completed 15+ projects in this field. Typical project cost ranges from $10,000 to $50,000 depending on scope and complexity.
How to Improve Plant Disease Detection Accuracy?
The main technical challenge is inter-class similarity of symptoms. Iron deficiency chlorosis and early stage powdery mildew on cucumber leaves look virtually identical — both produce light spots with blurred edges. A model trained only on spot color systematically confuses them.
Why Multi-Feature Analysis Outperforms RGB Classification?
We use multi-feature analysis including:
- Textural features of the spot (LBP, Haralick features via
skimage.feature) - Lesion geometry: shape, perimeter-to-area ratio
- Spread pattern across the leaf (marginal vs central vs diffuse)
- Vegetation stage as context (young leaves give different patterns)
A dual-stream architecture works well: one stream processes an RGB patch via EfficientNet, the other processes computed texture maps via a lightweight MobileNetV3. Output embeddings are concatenated before the final classification head. On the PlantVillage dataset plus our own annotations across 14 crops, such architecture yields top-1 accuracy 0.93 vs 0.87 for single-stream EfficientNet-B4 — a 1.07x improvement.
What to Choose: Detection or Segmentation?
For field conditions, detection is needed, not just patch classification. YOLOv8 or RT-DETR return a bounding box with disease class and confidence in real time — critical for a mobile app that agronomists use directly in the field.
From practice: YOLOv8m on a dataset of 12,000 annotated leaf images (8 diseases, 4 crops) after fine-tuning showed mAP50 = 0.81. Problematic classes — early blight and alternaria (IoU < 0.5 on 23% predictions) due to overlapping lesions. Solution: instance segmentation instead of bbox — Mask R-CNN or YOLOv8-seg allows separating overlapping foci.
| Model | mAP50 | mAP50-95 | FPS (mobile GPU) |
|---|---|---|---|
| YOLOv8n | 0.73 | 0.51 | 42 |
| YOLOv8m | 0.81 | 0.58 | 18 |
| RT-DETR-L | 0.84 | 0.62 | 12 |
| YOLOv8m-seg | 0.79 | 0.57 | 14 |
YOLOv8n INT8 works 3.5x faster than RT-DETR-L with comparable accuracy (0.73 vs 0.84, but on a mobile device speed is more important).
Deployment in Field Conditions
Two scenarios with different requirements:
Mobile app for agronomist. Model — ONNX + ONNX Runtime Mobile on Android/iOS. YOLOv8n INT8 runs on Snapdragon 8 Gen 2 with a latency of 45–70 ms, which is acceptable for handheld shooting. The app works offline, results sync when connected.
Drone system. NVIDIA Jetson Orin NX (16 GB) + TensorRT engine. Real-time inference at 4K 30 fps, detection on every 5th frame, coordinates of infected points written to GeoJSON.
Performance comparison on different platforms:
| Platform | Model | Latency (ms) | Memory (MB) |
|---|---|---|---|
| Snapdragon 8 Gen 2 | YOLOv8n INT8 | 45–70 | 4.2 |
| Jetson Orin NX | YOLOv8m FP16 | 12–18 | 28 |
Data: Labeling and Dataset Expansion
Public labeled datasets on plant diseases are sufficient to start (PlantVillage — 87,000 images, 38 disease classes). However, for specific crops and regions, fine-tuning on own data is needed — diseases look different in different climatic zones and on different varieties.
Data Requirements for Model Training
To train a model, you need a representative set of images from your fields. We recommend at least 500 labeled images per disease class. We speed up labeling via active learning: after initial training, the model selects the most "uncertain" examples (entropy sampling), and the agronomist labels only those. In practice, this reduces manual labeling effort by 40–60%.
What's Included in the Project
Our turnkey solution includes:
- Data audit and disease list definition
- Field data collection and annotation (if needed)
- Model development and training (transfer learning)
- Field validation and iterative improvement
- Deployment in mobile app or on edge device (Jetson, etc.)
- Performance drift monitoring, support, and retraining
- API documentation and operational guides
- Training for agronomists on system use
How Development Works: Step-by-Step Plan
- Data audit and disease list definition
- Field data collection and labeling (if necessary)
- Model development and training (transfer learning)
- Field validation, iterations
- Deployment in mobile app or on edge device
- Drift monitoring, support and retraining
- API and operation documentation
- Training agronomists to use the system
We deliver turnkey projects: from data collection to deployment. We will evaluate your project within 2 days. Contact us for a consultation. Order a preliminary audit of your project.
Typical labeling errors
- Ambiguous lesion boundaries
- Labeling at different vegetation stages
- Incorrect identification of similar diseases
Timeline and Cost
Detection system for one crop and 5–8 diseases: 6–10 weeks with labeled data available. Multi-crop platform with mobile app and server component: 3–4 months. Cost is calculated individually; typical range $10,000–$50,000. Our company has been on the market for 5 years and has completed 15+ projects in machine vision for the agricultural sector.