SporeCam: Real-Time Airborne Pathogen Detection Intelligence

Current crop disease management remains largely reactive, relying on manual scouting and weather models that often detect infections too late, leading to irreversible yield loss. Scanit introduces a proactive, data-driven alternative through real-time pathogen pressure measurement. Early warnings and dynamic digital threat maps allow growers to act before visible symptoms appear, significantly reducing risk and improving intervention timing.

SporeCam functions as an autonomous, field-based laboratory for fungal disease detection. The system analyses up to one million airborne particles per day using dual-spectrum imaging with white and UV light to detect pathogen signatures on an hourly basis. Hidden biological markers captured in these images are processed by a cloud-based AI engine referencing a digital pathogen spore library. The AI enhances optical signature libraries so proprietary algorithms can differentiate between visually similar diseases. A single SporeCam can detect multiple pathogens across crops simultaneously and cover more acreage than traditional field IoT devices, enabling multiple small farms to benefit from a shared monitoring node. The device operates continuously with minimal farmer input, converting raw biological data into localised insights delivered through dashboards, mobile alerts, or API integrations.

Scanit demonstrated large-scale deployment through a partnership with Evergreen FS Inc. in Illinois. The monitoring network expanded from 20 million acres in 2022 to more than 40 million acres in 2023–24, tracking four major fungal diseases in corn. In 2024, Evergreen integrated Scanit's ground-truth pathogen data into its Agtrinsic disease detection platform, combining biological signals with agronomic and weather models. This enabled daily risk scoring, hotspot mapping, and actionable prompts such as scout now, hold, or treat. The system distinguished between weather-favourable conditions and confirmed pathogen presence—a critical differentiation that prevents unnecessary interventions.

Two seasons of deployment delivered measurable impact. Approximately 1,430 farmers benefited directly, with 1,085 farms onboarded and 275 active advisors. Around 1.27 million disease-risk alerts were issued, improving agronomist productivity by 18–28%. Risk-prioritised scouting triggered more than 110,000 targeted actions. Roughly 79,000 fungicide applications were avoided or deferred, generating an estimated USD 9.9 million in grower benefit. Yield protection in high-pressure environments reached approximately 2–6 bushels per acre. Multi-year Bayer studies confirmed autonomous, accurate pathogen detection correlated with in-field disease occurrence. In India, deployments in Maharashtra's grape regions through ABM Knowledgeware and expanded partnerships with DeHaat demonstrate adaptation across international markets and diverse crops.
For additional context and detailed documentation of this use case, please refer to pages 25-26 in the attached Casebook.