Pest Detection From Field Images With AI Treatment Guidance

Added Jun 2025 3 design docs

Crop pests can erase a season's profit in weeks, and by the time damage is visible from the road, treatment options have narrowed and costs have multiplied. Smallholders and agronomists alike need early detection that works from ordinary field photos combined with the sensor data farms already collect. The intern builds an IoT-enabled detection platform in Python. Field images are analyzed with OpenCV to isolate leaves and lesions, and generative AI identifies likely pests, produces treatment recommendations in plain language, and drafts scouting reports for agricultural stakeholders. Real-time IoT sensor data covering humidity, temperature, and leaf wetness enriches the analysis, since outbreak risk depends as much on conditions as on sightings. MongoDB stores images, detections, and sensor streams, powering outbreak tracking maps and visual analytics that show how infestations spread across fields over time, with secure user management and exportable reports completing the product. The intern learns to fuse computer vision, sensor telemetry, and generative AI into one decision-support pipeline, and to present uncertain model output in a way a farmer can act on responsibly.

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