PRECISION_AGRICULTURE
CropIntelligencePlatform
GenAI-augmented disease detection with MobilePlantViT. Mobile-edge deployment for real-time field diagnostics and advisory systems.
Edge AI
Mobile-First
0.69M Params
GenAI Advisory
Offline-Capable
ACCURACY
94.7%
FIELDS_MONITORED
1247
DISEASE_DETECTIONS
8934
AGRICULTURAL_LOSSES
Significant losses due to detection failures
1
Pest Infestations
Crop loss 15–40%
2
Plant Diseases
Late detection = spread
3
Nutrient Deficiencies
Yield reduction 20–30%
4
Suboptimal Inputs
Water/fertilizer waste
EXISTING_TOOL_GAPS
[1]
Early & accurate detection across large fields
[2]
Optimal interventions tuned to local conditions
[3]
Scale across heterogeneous crops & geographies
[4]
Integrate with farmer workflows (mobile offline)
[5]
Explainability, trust & ongoing adaptation
[6]
Incorporate textual agronomic knowledge
Crop Intelligence Platform
Hybrid system using ML/CV/GenAI agents + mobile/web UI + advisory engine + operational pipeline tailored to agriculture
ViT
MobilePlantViT
Mobile-Friendly Hybrid Vision Transformer (2025)
0.69M
Parameters
90–98%
Accuracy
<200ms
Mobile Inference
✓
Lightweight hybrid ViT for mobile/edge devices
✓
Generalized across diverse crop disease datasets
✓
State-of-the-art mobile-grade accuracy
✓
Real-time field diagnostics capability
✓
Hyperspectral imagery support available
✓
2025 research validated architecture
arXiv:2503.16628
2025
08
Platform Modules
End-to-End Architecture
Edge / Mobile Capture
01
RGB, NIR, multispectral
Preprocessing Pipeline
02
Standardize, background removal, tiles
ML / CV Core
03
MobilePlantViT + multimodal fusion
GenAI Advisory Agent
04
Domain-tuned LLM + agronomy KB
Business Logic
05
Crop rules, ROI, supply chain
Mobile App
06
Offline-first React Native
Web Dashboard
07
Next.js + field maps
Feedback Loop
08
Retraining + A/B testing
ML_COMPUTER_VISION_CORE
Vision & ML Pipeline
Mobile Hybrid ViT/CNN
Inspired by MobilePlantViT for classification
Segmentation Head
Localize disease spots on leaf images
Multimodal Fusion
Vision + sensor data via Transformer
Uncertainty Estimation
Monte Carlo dropout, ensembles for ambiguous cases
Few-Shot Adaptation
Meta-learning for new disease classes
Generative Augmentation
GAN/diffusion to expand rare class images
Contrastive Pretraining
Self-supervised learning on unlabeled field images
Mobile-friendly • Edge-optimized • Domain-adapted • Uncertainty-aware
GenAI Advisory Engine
Domain-Tuned LLM + Agronomy KB
1
Generate treatment plan constrained by chemical rules & regulation
2
Answer farmer queries with pictorial explanations + disease summaries
3
Field Q&A via hybrid retrieval + generation from agronomy KB
4
Smart prompts (e.g., "Given soil moisture 45% and forecast rain, postpone fungicide")
SAFETY_GUARDRAILS
!
Do not suggest banned chemicals
!
Human review for critical suggestions
!
Dosage verification against safety limits
!
Weather-aware timing recommendations
Case Blueprints
Three deployment patterns
Smallholder Rice Disease Monitor
Mobile-First
A
MODEL
MobilePlantViT on-device
DISEASES
Rice blast, sheath blight
FEATURES
3 key capabilities
Offline capture
Extension worker dashboard
Network sync when available
Commercial Farm Crop Health
Large-Scale
B
MODEL
Drone + satellite imagery
DISEASES
Multi-spectral RGB fusion
FEATURES
3 key capabilities
Segmentation maps
Zone-specific interventions
ROI simulation per zone
Plant Protection Marketplace
Advisory + Commerce
C
MODEL
Diagnosis → Treatment → Supply
DISEASES
All major crop diseases
FEATURES
3 key capabilities
Input supplier proposals
Marketplace delivery
Logistics coordination
Challenges & Mitigations
1
CHALLENGE
Domain shift / geography
WHY
Crop appearance changes by region, lighting, variety
SOLUTION
Region-specific adaptation, few-shot fine-tuning, domain adaptation
2
CHALLENGE
Scarcity of labeled data
WHY
Many diseases occur rarely
SOLUTION
Generative image augmentation, transfer learning, meta-learning
3
CHALLENGE
Mobile resource constraints
WHY
Low compute, limited memory
SOLUTION
Ultra-light ViT (MobilePlantViT 0.69M params)
4
CHALLENGE
Explainability and trust
WHY
Farmers will not trust black-box outputs
SOLUTION
Visual overlays (masks), saliency heatmaps, textual reasoning from GenAI
5
CHALLENGE
Edge-vs-cloud tradeoffs
WHY
Network connectivity may be poor
SOLUTION
Hybrid mode, offline caching, prioritized upload of uncertain cases
6
CHALLENGE
Regulatory / chemical risks
WHY
Wrong advisory may harm crop or health
SOLUTION
Hard-coded constraints, human override paths, validation vs KB
MLOps & Validation
Performance Metrics
| METRIC | TARGET | COMMENT |
|---|---|---|
| Classification accuracy | ≥ 90–98% | Per-crop benchmarking |
| Segmentation IoU | ≥ 0.80–0.90 | Lesion area quantification |
| Advisory acceptance | ≥ 80% | User surveys |
| Mobile latency | ≤ 200 ms | Classification time |
| Drift stability | Stable monthly | Confidence distribution |
| Crop loss reduction | 10–30% | Pilot vs control |
VALIDATION_APPROACH
1
Pilot field trials in multiple geographies
2
Split-plot control groups
3
Mock field variation tests (lighting, occlusion, wind-blur)
4
User trust surveys & error logging
5
Shadow runs before production rollout
Deploy mobile-grade crop intelligence
MobilePlantViT-powered disease detection with GenAI advisory. Offline-capable mobile apps and real-time field diagnostics for precision agriculture at scale.
TECH_STACK
MobilePlantViT
0.69M params
GenAI Advisory
Domain-tuned LLM
Mobile Apps
Offline-first
Web Dashboard
Next.js + maps
RESEARCH_VALIDATED
arXiv:2503.16628 (2025)
ICPR 2024 Winner
MobilePlantViT