Automations
This pillar addresses controlled-environment agriculture workflows that automate climate, lighting, nutrient delivery, harvesting schedules, and energy use across greenhouse and vertical farm operations. The content should emphasize closed-loop environmental control, robotics integration, and production optimization architectures for high-density farming systems.
This foundational page outlines a custom, end-to-end orchestration architecture for controlled-environment agriculture, connecting climate, lighting, irrigation, and robotics into a single, closed-loop system. It explains how to build a multi-agent workflow that reduces manual oversight by 70%, improves yield consistency, and integrates with existing SCADA and farm management platforms. The focus is on the technical blueprint for a unified control plane that enables true autonomy across disparate greenhouse and vertical farm subsystems.
This page details a custom workflow that fuses sensor data, external weather forecasts, and crop-stage models to autonomously adjust HVAC, dehumidification, and CO₂ enrichment. It targets the operational bottleneck of manual climate balancing, delivering energy savings and yield protection through predictive setpoint optimization. The architecture covers agentic orchestration of PLCs, approval gates for major system overrides, and integration with BMS for enterprise-scale greenhouses.
This page explains a custom automation workflow that schedules and adjusts LED lighting spectra in real-time based on crop stage, energy pricing, and canopy density. It eliminates the manual labor of static light recipes, improving energy efficiency and plant morphology. The implementation covers multi-agent coordination of lighting controllers, DLI (Daily Light Integral) tracking, and integration with demand-response programs for utility cost reduction.
This page outlines a sensor-driven automation workflow that triggers irrigation events based on substrate moisture, VPD, and plant water use models, while dynamically blending nutrient solutions. It addresses the waste and inconsistency of manual watering, reducing water and fertilizer use by up to 40%. The architecture details integration with soil moisture sensors, dosing pumps, and recirculating system controls, including exception handling for clogged emitters.
This page details a custom multi-agent workflow that continuously monitors and corrects pH and electrical conductivity in nutrient reservoirs, preventing crop stress and lockout. It automates a high-frequency manual task, ensuring solution stability and reducing crop loss. The solution covers real-time sensor ingestion, automated acid/base dosing logic, and alerting for trends that require human investigation, integrated with common fertigation controllers.
This page explains a computer-vision and agent-based workflow that automates pest and disease detection via scouting drones or fixed cameras, then triggers targeted biocontrol releases or treatment protocols. It reduces manual scouting labor and improves response time, limiting outbreak spread. The architecture covers image analysis pipelines, beneficial insect release system integration, and work order generation for human crews, with audit trails for organic certification.
This page details a custom workflow that uses 3D and hyperspectral imaging to autonomously track daily growth, estimate leaf area index, and flag early signs of nutrient deficiency or disease. It replaces manual plant measurements, providing continuous, data-driven insights for crop steering. Implementation covers fixed or mobile imaging rigs, computer vision model orchestration, and integration of phenotypic data into crop management platforms for actionable alerts.
This page outlines an automation workflow that combines imaging, weight sensors, and historical data to predict the perfect harvest date for each plant or zone, then coordinates robotic harvesters and conveyor systems. It maximizes yield quality and minimizes waste from premature or late harvesting. The architecture involves multi-agent task planning for robotic fleets, integration with warehouse management systems, and yield reconciliation with ERP platforms.
This page explains a custom environmental control workflow that optimizes temperature, humidity, and light for seed germination trays and automatically ramps down conditions for seedling hardening. It standardizes a labor-intensive, experience-dependent process to improve propagation success rates. The build covers integration with chamber controllers, scheduling based on seed variety, and alerts for transplant readiness, linking to transplanting robot systems.
This page details an orchestration workflow that dynamically assigns tasks—like transporting harvested trays, delivering substrate, or scouting—to a fleet of AGVs within a vertical farm. It solves the coordination bottleneck of manual material handling, increasing throughput. The architecture covers spatial AI for path planning, collision avoidance, integration with WMS for task generation, and predictive scheduling for robot charging and maintenance.
This page outlines a custom workflow that automates energy curtain deployment and HVAC setpoints based on internal light levels, external weather, and real-time electricity pricing. It targets significant energy cost reduction (20-30%) through passive solar gain and demand shaving. The implementation involves multi-agent decision logic, integration with utility APIs for price signals, and control over curtain motors and climate computers.
This page explains a supply-chain automation workflow that triggers harvests based on incoming customer orders, optimizing for freshness and reducing cold storage inventory. It eliminates the guesswork and waste of forecast-based harvesting. The architecture covers integration between e-commerce/ERP platforms and harvest scheduling systems, multi-agent logic for order batching and labor assignment, and automated packing instruction generation.
This page details a custom planning workflow that automates crop rotation schedules, bench/tower assignments, and staggered planting to maximize space utilization and continuous harvest in shared facilities. It replaces complex, error-prone spreadsheet planning. The solution involves digital twin simulation, agent-based optimization against constraints (light, height, harvest date), and integration with seeding and transplanting systems to execute the plan.
This page outlines a sophisticated climate automation workflow that uses infrared sensors and energy balance models to predict and prevent leaf temperature stress, autonomously adjusting misting, fogging, and air circulation. It addresses a key physiological bottleneck for quality and growth. The architecture covers sensor fusion, model-in-the-loop control algorithms, and integration with fine-mist and HVAC systems for precise microclimate control.
This page explains a custom workflow that monitors stock solution levels, calculates depletion based on fertigation events, and automatically triggers the mixing of new batches from raw fertilizer salts. It eliminates manual, error-prone stock tank management. The build covers integration with scale systems, dosing equipment, and inventory management, with validation checks and alerts for off-spec solutions before they enter the main system.
This page details an IPM automation workflow that schedules and executes the release of predatory mites or wasps based on pest scouting data, then monitors their establishment using camera traps. It optimizes biocontrol efficacy and reduces chemical pesticide use. The architecture involves agentic scheduling tied to pest thresholds, integration with automated release dispensers, and computer vision for post-release verification.
This page outlines a computer vision workflow that automatically inspects, grades, and sorts harvested produce on packing lines based on size, color, and defect criteria. It replaces manual grading, increasing throughput and consistency. The implementation covers high-speed imaging, real-time classification models, integration with sorting machinery (air jets, robotic arms), and data logging for lot traceability and customer reporting.
This page explains a custom automation workflow that coordinates robotic systems to fill trays with substrate, precisely sow seeds, and apply vermiculite or coverings, all based on digital crop plans. It standardizes propagation and reduces labor at a critical bottleneck. The architecture involves multi-agent task planning, integration with seed inventory systems, vision-based quality checks, and conveyor synchronization.
This page details an energy optimization workflow that automates the dispatch of on-site CHP units and the charging/discharging of thermal energy storage based on greenhouse heat demand and electricity export prices. It maximizes the economic return on capital-intensive energy assets. The build involves forecasting agents, integration with CHP controller APIs, and dynamic setpoint control for the greenhouse climate system to utilize stored heat.
This page outlines a supply chain workflow that uses shelf-life models and real-time cold chain data to predict spoilage risk for harvested batches, then automatically triggers markdowns or priority routing to minimize waste. It directly protects margin by optimizing sell-through. The architecture integrates with ERP/WMS, uses IoT temperature data, and includes agentic logic for pricing adjustments and sales channel recommendations.
This page explains a custom lighting workflow that automates the application of specific UV-B light spectra at calculated doses to stimulate plant defense mechanisms, reducing susceptibility to pathogens. It automates a precise, timing-sensitive protocol that is impractical to manage manually. The implementation covers spectral control of UV-capable LEDs, scheduling based on crop stage and disease pressure models, and safety interlocks for human presence.
This page details a substrate health automation workflow that monitors dissolved oxygen in irrigation water or substrate and triggers aeration cycles (via venturi injectors or pulse irrigation) to prevent root hypoxia. It addresses a hidden yield limiter in dense growing systems. The architecture involves DO sensor integration, control logic for pumps and solenoids, and coordination with the main irrigation schedule to avoid over-watering.
This page outlines a custom workflow that analyzes telemetry from boilers, chillers, pumps, and filters to predict failures, schedule maintenance, and order parts automatically. It reduces unplanned downtime that can devastate crop cycles. The implementation covers IoT data ingestion, anomaly detection models, integration with CMMS for work order generation, and parts inventory systems for proactive reordering.
This page explains a custom workflow that automatically tests and blends different water sources (municipal, rainwater, RO reject) to meet target quality parameters (pH, alkalinity) before entering the fertigation system. It ensures input water consistency and reduces treatment costs. The architecture involves inline sensor arrays, control valves, and dosing logic, with alerts for source water contamination or system deviations.
This page details a workflow that automates the setup, environmental differentiation, data collection, and statistical analysis of side-by-side crop trials for new varieties or practices. It accelerates R&D cycles by removing manual data handling. The build covers creating digital twins for trial zones, automated data aggregation from sensors and imagers, and report generation for comparing yield, quality, and resource use metrics.
This page outlines a food safety automation workflow that, upon a quality flag or external alert, instantly traces the affected product lot back through harvest, growing zone, and input batches, then isolates related inventory. It turns a multi-day manual investigation into a minutes-long automated process, limiting liability. The architecture leverages blockchain or centralized ledger integration across sowing, growing, and harvest systems to create an immutable audit trail.
This page explains a sustainability reporting workflow that automatically aggregates energy, water, fertilizer, and packaging data from operational systems to calculate and report the carbon footprint for each crop batch. It automates a complex manual reporting task for ESG disclosures and customer requests. The implementation involves data connectors to utility meters, ERP, and fertigation systems, with calculation engines and framework mapping for standards like GHG Protocol.
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One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
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