Agricultural IoT Monitoring & Analytics

MicrocosmWorks can build a precision agriculture platform that unifies ground-level sensor networks, aerial imaging, and weather intelligence into a single decision-support system for farm managers. Solar-powered sensor nodes deployed across fields continuously measure soil moisture at three depths, soil temperature, electrical conductivity, and ambient conditions, transmitting readings over LoRaWAN to field gateways. Drone multispectral imagery is processed through computer vision models to generate NDVI maps, detect early signs of nutrient deficiency, and identify pest or disease hotspots before they are visible to the naked eye. The AI engine fuses all data streams into field-level prescriptions for variable-rate irrigation, targeted fertilizer application, and optimally timed spray operations, delivered to the grower's phone and directly to compatible precision equipment controllers.

The platform is delivered over 10-12 weeks across four phases. Weeks 1-2 conduct field assessment, sensor placement planning based on soil variability maps, and architecture design for the LoRaWAN mesh network with connectivity buffering for rural environments. Weeks 3-6 deploy solar-powered sensor nodes with multi-depth soil moisture probes, configure LoRaWAN gateways with local buffering, build the cloud ingestion pipeline, and establish the aerial imagery processing workflow for drone data. Weeks 7-9 train crop health and yield prediction models using historical field data, implement the variable-rate irrigation and fertigation prescription generator, and build the farmer-facing mobile and web dashboards with field-level map overlays. Weeks 10-12 validate prescriptions against agronomist review, test integration with precision equipment controllers (John Deere, Trimble, ISOBUS), and deliver the platform with grower training and seasonal operations handoff.

• Ground-to-Sky Data Fusion: MW can combine continuous soil sensor telemetry with multispectral drone imagery in a single decision engine, correlating subsurface moisture conditions with above-canopy vegetation health to produce prescriptions that neither data source could generate alone.
• Connectivity-Resilient Architecture for Rural Deployment: The LoRaWAN mesh with local gateway buffering is specifically designed for agricultural environments with intermittent connectivity, ensuring zero data loss during cellular outages that would cripple cloud-dependent platforms.
• Prescriptive Actions, Not Just Dashboards: MW can deliver zone-level irrigation schedules and variable-rate fertigation plans pushable directly to compatible precision equipment controllers, closing the gap between data insight and field action that leaves most agricultural monitoring platforms as expensive display screens.

• IoT Development — LoRaWAN sensor network design, solar-powered node engineering, and irrigation valve integration
• AI Development — Crop health image classification, yield prediction models, and pest/disease early warning algorithms
• Cloud Solutions — Geospatial data storage, imagery processing pipelines, and low-latency API infrastructure

• Predictive Maintenance for Smart Factories
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• Connected Fleet Management System