GOVT. P.G. COLLEGE, DHARAMSHALA
DEPT. OF COMPUTER SCIENCE
B.TECH. (C.S.E) 6TH SEMESTER
Presented By : Ctrl+RRR
PLANT DISEASE
DETECTION
Context
800M+
People at risk
People at risk of food insecurity globally
$220B
Annual global loss
Annual global loss from crop diseases
78%
Crop losses
Crop losses caused by plant diseases
Global Food Security
Agriculture feeds over 8 billion people worldwide. Any disruption to crop health directly threatens global food supply chains, impacting food availability, prices, and nutritional security for billions.
Disease Impact
Plant diseases destroy up to 40% of food crops annually, causing severe economic and humanitarian consequences. These losses translate to billions in revenue lost and millions facing food insecurity.
Detection Gap
Most smallholder farmers lack access to plant pathologists and diagnostic laboratories. Diseases often go undetected until visible symptoms appear, by which point significant crop damage has already occurred.
AI as the Solution
Deep learning models detect disease from leaf images with expert-level accuracy, deployable on any smartphone. This technology democratizes access to expert diagnostics, enabling real-time intervention.
India-Specific Need
India's 140 million+ farming households need affordable, multilingual tools. Making Hindi-language AI interfaces critical for adoption and impact in rural communities.
Introduction
What is Plant AI?
An end-to-end AI web application that identifies plant leaf diseases from photos in seconds, combining computer vision with deep learning to provide accessible diagnostic capabilities previously available only to agricultural experts.
YOLO Detection
YOLOv8 object detection for precise leaf localization, automatically cropping the region of interest before classification to improve accuracy in cluttered backgrounds.
Dual Ensemble
MobileNetV2 contributes 55% weight while YOLOv8-cls contributes 45%, combining their strengths for robust classification across diverse conditions.
3 Input Modes
Upload photos from gallery, capture new images with camera, or use live webcam for real-time detection with bounding-box overlay.
Bilingual
English and Hindi UI with correctly-shaped Devanagari script in PDF reports, ensuring accessibility for India's diverse farming communities.
PDF Reports
Auto-generated per-image diagnostic reports with disease identification, treatment recommendations, and prevention guidance in both languages.
Live Detection
Real-time bounding-box overlay showing detected leaf regions and instant classification results as you point the camera at plants.
Supports 15 disease classes across Tomato, Potato, and Pepper crops — the most economically important vegetable crops in India.
Literature Review
Previous research established the foundation for AI-based plant disease detection, but each approach had limitations our system addresses through ensemble architecture and bilingual deployment.
Reference
Journal
Findings
Gap / Contribution
Mohanty et al. (2016)
Frontiers in Plant Science
MobileNet on PlantVillage: 99.35% accuracy in lab conditions; real-world accuracy much lower due to background noise
No leaf localization; background clutter degrades performance
Ferentinos (2018)
Comp. & Electronics in Agri.
Deep CNNs (AlexNet, VGG, GoogleNet) for disease detection; GoogleNet achieved 99.53% on lab images
High compute cost; unsuitable for mobile/edge deployment
Ramcharan et al. (2019)
Frontiers in Plant Science
MobileNetV2 smartphone app for cassava disease detection in Africa; proved mobile-first AI is feasible
Single crop; no ensemble or bounding-box detection pipeline
Jiang et al. (2020)
IEEE Access
YOLOv3 for real-time multi-class plant disease detection; 89.2% mAP at 10 FPS on embedded hardware
Classification accuracy limited without a dedicated classifier
Our System
YOLO detection + MobileNetV2 + YOLOv8-cls ensemble with bilingual UI and WeasyPrint PDF reports.
Problem Statement
How can resource-constrained farmers accurately identify plant diseases and receive actionable treatment guidance in real time — without expert agronomists, expensive labs, or language barriers?
1
Late Detection
Diseases identified visually only after 40–60% crop damage has occurred — far too late for effective intervention. By the time farmers notice visible symptoms, significant yield loss is already inevitable, and treatment options become limited and costly.
2
Expert Scarcity
Only 1 plant pathologist per 500+ farms in developing regions. Remote farmers have zero diagnostic access, forcing them to rely on guesswork or travel long distances to agricultural extension offices, delaying critical treatment decisions.
3
Language Barrier
Most AI diagnostic tools are English-only interfaces. 650 million+ Indian farmers primarily speak regional languages and cannot effectively use English-only systems, creating a significant adoption barrier despite technical capability.
4
No Actionable Output
Existing apps classify disease but don't produce structured, shareable treatment and prevention reports for field use. Farmers need clear, step-by-step guidance in their language, not just a disease name.
Our Solution
Real-time AI detection + bilingual guidance + auto PDF reports — accessible via any browser on smartphones, tablets, or computers without installation.
Methodology — System Pipeline
Our three-phase training strategy and dual-ensemble architecture maximize accuracy while maintaining efficient inference time suitable for web deployment.
📷 User Input (Upload / Camera / Live)
Images accepted in three formats: batch upload from file system, single capture via device camera, or continuous live detection from webcam feed
🔍 YOLOv8 Detector (yolov8n.pt)
YOLOv8 nano architecture detects leaf regions with bounding boxes, filtering out background clutter
◆ Leaf / Plant Found?
Validation check: if no leaf detected, HSV green blob fallback attempts to identify plant material
✂️ Crop Leaf Region (+ margin padding)
Extract detected leaf region with margin padding for classifier input
MobileNetV2 + YOLOv8-cls
Two parallel classifiers process cropped leaf image independently
⚖️ Ensemble Voting (55% MB + 45% YC)
Weighted average combines predictions for final diagnosis
📊 Result + PDF Report
Generate bilingual diagnostic report with treatment guidance
🏋️ Training Pipeline (3 Phases)
01
Data
PlantVillage Dataset — 15 classes with 54,305 images total
02
Preprocessing
Resize, augment, normalize images; split 80/20 train/validation
03
Model Training
Train MobileNetV2 and YOLOv8-cls separately, then combine
Result Analysis
~93%
MobileNetV2
Validation Accuracy
~91%
YOLOv8-cls
Top-1 Accuracy
~2 s
Per-image
Inference Time
15
Disease Classes
Detected
📋 Key Metrics
~92.4%
Precision
Correct positive predictions
~91.8%
Recall
Proportion of actual positives identified
~92.1%
F1-Score
Harmonic mean of precision and recall
0.18
Train Loss
Final training loss value
0.23
Val Loss
Final validation loss value
~0.5
Inference FPS
Frames per second processing
Ensemble boosts accuracy ~2% over single model — The combination of MobileNetV2 and YOLOv8-cls with weighted voting improves accuracy by approximately 2 percentage points compared to using either model alone, demonstrating the value of ensemble architecture.
Conclusion
✅ Key Achievements
YOLO + MobileNetV2 ensemble achieves ~93% validation accuracy on 15 disease classes
Bounding-box leaf localization improves classifier precision in cluttered backgrounds
Bilingual UI (English + Hindi) with correctly-shaped Devanagari PDF reports
Three input modes: batch upload, camera snapshot, and real-time live detection
Auto-generated WeasyPrint PDF with diagnosis, treatment, and prevention per image
3-phase training strategy maximizes accuracy with efficient resource use
🚀 Future Enhancements
📱 Mobile App
Flutter/React Native offline field app for farmers without consistent internet connectivity
🌾 More Crops
Expand to Rice, Wheat, Maize, Sugarcane — India's major staple crops
🗺 GPS Heatmapping
Map disease spread by region for alerts and agricultural planning
☁️ Edge Deployment
Raspberry Pi + TFLite for remote farms without cloud infrastructure
🌍 More Languages
Tamil, Telugu, Marathi, Bengali support for pan-India reach
📊 Severity Scoring
Estimate disease severity percentage per image for treatment intensity
🤝 Community Portal
Crowd-sourced data to retrain models with real-world farmer-submitted images
Thank You