AI Crop Doctor (Computer Vision Diagnostics)
Upload a smartphone crop photo to identify plant diseases, pest damage, nutrient deficiencies, or environmental stress with confidence scoring and next-step actions.
The Problem
What difficult challenge is this experiment tackling?Misdiagnosing fungal vs bacterial leaf spots causes farmers to spend thousands of rupees on ineffective pesticides while crop damage continues to spread.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?Fine-tuning multimodal vision models on Indian crop disease imagery can detect leaf pathologies with over 90% top-3 accuracy and recommend organic/chemical remedies.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human InterventionModel inference time under 600ms on low-bandwidth mobile 4G, >92% validation accuracy on wheat and soybean datasets.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsImage metadata scrubbed of GPS latitude/longitude prior to cloud processing; zero permanent storage of personal imagery without consent.
Lessons Learned & Observations
Empirical findings from prototyping and testingInitial training dataset of 5,000+ Indian crop disease leaf images curated.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesAdd drone aerial imagery support for whole-field blight mapping.