Wearable IoT device with edge and cloud integration for real-time agricultural forecasting

Abstract

A portable device for agricultural applications, consisting of: a microcontroller-based processing unit (101); a plurality of integrated environmental sensors (102), including temperature, humidity and barometer sensors; wireless communication modules (103) supporting Bluetooth, Wi-Fi and LoRa protocols; a GPS module (104) for geolocation; a display interface (105) with touch and haptic feedback; wherein the device is configured to communicate with nearby edge computing nodes to receive localized agricultural predictions based on real-time environmental data.

Description

Technical field:

[0001] The present invention relates to agricultural technologies and, more particularly, to an IoT-enabled watch for farmers that connects to edge devices and cloud computing platforms to provide real-time hyperlocal forecasts, including weather conditions, crop disease alerts, and agronomic recommendations. Background of the invention:

[0002] Agriculture is undergoing a profound transformation, driven by the emergence of digital and precision agriculture technologies. As global demand for food increases and climate variability becomes increasingly unpredictable, farmers must make smarter, faster, and more localized decisions regarding crop health, irrigation, pest control, and harvest. At the core of modern agriculture lies the need for timely access to reliable, site-specific data that can help farmers mitigate risks, increase yields, and use resources efficiently.

[0003] Traditionally, agricultural decision-making was based on general regional weather forecasts, expert recommendations, historical practices, and manual field observations. While these methods were sufficient in the past, they lack the specificity, immediacy, and scalability required in today's agricultural ecosystems. Furthermore, conventional advisory systems often fail to consider the unique microclimatic conditions and operational realities of individual farms, especially in rural areas with limited technological infrastructure or unreliable internet connectivity.

[0004] Over the past decade, technological advances such as the Internet of Things (IoT), cloud computing, edge computing, artificial intelligence (AI), and mobile connectivity have revolutionized several sectors, including agriculture. IoT devices now enable remote monitoring of soil moisture, crop growth, and environmental parameters. Cloud platforms provide massive computing power for training AI models to predict pest outbreaks, irrigation needs, or disease risks. Meanwhile, edge computing enables local processing of sensor data, reducing latency and reliance on constant connectivity. Nevertheless, a major challenge remains the effective, real-time delivery of these technologies directly to farmers, especially in mobile, wearable, and connectivity-limited contexts.

[0005] Wearable devices have emerged as an innovative class of personal technology capable of providing hands-free access to real-time data and notifications. However, most current wearable systems are designed for consumer health and fitness applications, not for agricultural use. Few, if any, integrate the ruggedness, environmental sensing, edge communications, and cloud analytics required for reliable field operation in agriculture. Furthermore, farmers often operate in physically demanding environments with dust, humidity, and sunlight, requiring specialized hardware design and intuitive, minimalist interfaces for usability in such conditions.

[0006] There is a significant technological gap in providing wearable solutions for farmers—especially smallholder and remote farmers—that operate autonomously in the field, support edge processing, and interact with cloud platforms for continuous learning and adaptation. A wearable system that can interact with distributed IoT nodes, retrieve localized insights from edge servers, and synchronize with cloud services when connectivity allows would provide farmers with real-time, data-driven support. Such a system would help address challenges in crop disease management, risk mitigation from weather events, irrigation optimization, and resource planning, while improving sustainability and resilience.

[0007] Another limitation in the current ecosystem is the dependence of existing smart farming platforms on stable internet access. This is a significant hurdle in many parts of the world where rural connectivity is sporadic or nonexistent. A hybrid architecture—leveraging both edge computing and cloud analytics—offers a promising path to closing this gap. Edge nodes deployed in tractors, weather stations, irrigation systems, or fixed field posts can act as local hubs, performing essential computations and delivering actionable predictions to nearby portable devices, even without internet access.

[0008] Additionally, the success of digital tools in agriculture depends on user acceptance, which is strongly influenced by the system's usability, reliability, and contextual relevance. Farmers need robust devices with long battery life, low maintenance, intuitive interfaces, and systems that respect their privacy and data sovereignty.

[0009] Given the above, there is an urgent need for a wearable IoT system specifically designed for agricultural applications that can collect data from the environment and the user, communicate with local edge nodes for instant predictions, and synchronize with the cloud to enable more comprehensive updates and model refinements. Such a solution would improve farmers' situational awareness, increase productivity, reduce crop losses, and ensure data-driven decision-making even in infrastructure-poor areas.

[0010] The present invention aims to address this need by introducing a wearable smartwatch-style agricultural device that leverages edge computing, cloud analytics, and robust sensor integration to deliver hyperlocal, real-time, and actionable information directly to farmers' wrists. Summary of the invention:

[0011] The invention provides a novel and comprehensive system comprising a wearable watch device for agricultural applications, integrated with environmental sensors, communication modules, edge computing infrastructure, and a cloud-based analytics platform. This system enables real-time, localized predictions of agricultural conditions, including crop disease risks, irrigation requirements, and weather changes, specifically tailored to the microclimate and operational context of individual farms.

[0012] At the core of the invention is the wearable watch, designed for a farmer's wrist and ruggedized for harsh outdoor agricultural conditions. The device contains an ARM-based microcontroller optimized for low-power operation and real-time processing of sensor data. It is equipped with a comprehensive sensor suite that includes temperature, humidity, UV index, barometric pressure, accelerometer, gyroscope, and GPS modules. These sensors continuously monitor both environmental and movement data, allowing the device to gather a comprehensive view of the field context and farmers' activities.

[0013] The wearable device communicates via multiple wireless protocols, such as Bluetooth 5.0 / 5.4, Wi-Fi, and LoRaWAN, to connect to nearby edge devices such as IoT-enabled tractors, weather stations, or field posts. These edge computing nodes are capable of performing local data processing using pre-installed machine learning models. This architecture enables the wearable device to receive instant, context-aware predictions and alerts, even in areas with poor or no internet connection. For example, the wearable device can alert a farmer to potential frost threats, pest outbreaks, or soil moisture deficiencies using real-time, on-site analytics.

[0014] When internet connectivity becomes available—either directly via Wi-Fi or cellular modules, or indirectly via the edge nodes—the wearable device synchronizes with a cloud computing platform. The cloud platform hosts large-scale machine learning models, historical weather data, soil databases, and region-specific agricultural advice. It processes uploaded sensor and activity data from the wearable devices and edge nodes, retrains predictive models, and transmits updated recommendations or alerts back to the wearable devices and edge devices. This hybrid edge-cloud architecture ensures both real-time responsiveness and long-term learning adaptability.

[0015] The wearable device features an intuitive user interface, including a sunlight-readable OLED or LCD display, haptic feedback, voice alert support, and touch controls optimized for use with gloves. This ensures ease of use in dynamic, external conditions where rapid interpretation and minimal user interaction are critical.

[0016] Energy efficiency is a key innovation in the design. The wearable device integrates intelligent energy management, low-power sensors, and wireless modules, along with solar charging capabilities and optional wireless charging. This enables continuous use in the field for several days without frequent recharging, a critical requirement for rural or mobile users.

[0017] The system works through a clearly defined functional workflow: 1. Data collection: The wearable device collects real-time environmental data and user activities. 2. Edge processing: Data is transmitted to nearby edge nodes, which provide fast, site-specific weather-proof forecasts using cached models. 3. Cloud synchronization: If connectivity permits, the wearable device connects to the cloud platform to download updated forecast models and weather data and upload collected field data. 4. Insight delivery: The wearable device displays real-time alerts and actionable recommendations such as weather forecasts, disease risk levels, or watering suggestions. 5. Feedback and logging: The farmer can log field activities (e.g. spraying, irrigation) to improve future predictions and advice accuracy.

[0018] The system addresses key challenges in modern agriculture by providing: • Hyper-local precision through microclimate data and geospatial awareness • Business continuity through autonomous edge computing • Scalability for use in small farms or large commercial operations • User-centered design developed for robust use in agriculture

[0019] The invention is particularly applicable in scenarios requiring real-time decision support in remote areas, such as irrigation planning during droughts, disease monitoring in high-risk zones, or work scheduling based on weather conditions. It has benefits across different types of crops, climates, and geographies, making it a transformative Wi-Fi tool in precision agriculture and digital farming.

[0020] By fusing wearable technology with distributed intelligence through edge and cloud computing, this invention empowers farmers with the data they need, when and where they need it—ultimately improving agricultural efficiency, productivity, and resilience. Short description of the drawing Fig. : shows a block diagram of the system according to the invention. Detailed description of the invention

[0021] The present invention discloses a smart, wearable agricultural device integrated into a watch format that enables real-time, localized predictions by seamlessly connecting to edge computing infrastructures and cloud-based data analytics platforms. This invention is specifically designed to serve farmers operating in challenging rural conditions where connectivity may be limited and where rapid, local insights are critical for effective crop and resource management. It offers a unique combination of wearable electronics, IoT-based data collection, hybrid computing architectures, and user-centric interface systems that together create a transformative agricultural tool.

[0022] The central component of the system is a wearable, smartwatch-style device equipped with robust environmental protection measures to withstand field conditions such as dust, rain, and extreme temperatures. The device comprises a lightweight yet rugged case containing an embedded processor, memory units, sensors, wireless communication modules, and a display system. The processor used in the device is an ARM Cortex-M4 or M7 microcontroller with integrated digital signal processing (DSP) capabilities, which offers the ideal balance between processing power and low power consumption, allowing it to run real-time applications on incoming data without quickly depleting power. A dedicated co-processor is also employed to handle constantly running background functions such as sensor polling, time tracking, and energy optimization.

[0023] A crucial function of the wearable device is its ability to continuously monitor environmental and contextual parameters relevant to agriculture. To enable this, the device is equipped with a range of environmental sensors, including, but not limited to, temperature sensors, humidity sensors, a barometer, UV index sensors, and ambient light sensors. These sensors are calibrated for agricultural use and provide accurate microclimate measurements that reflect the specific field conditions encountered by the farmer. In addition to environmental monitoring, the wearable device includes a 3-axis accelerometer and a gyroscope to capture user activity and movement that may be correlated with specific agricultural tasks.A GPS or GNSS module is also integrated into the watch to provide accurate location data, which is essential for geotagging the collected environmental data and enabling location-specific insights.

[0024] One of the key technological innovations of this invention lies in its hybrid connectivity approach, which utilizes a mix of short-range, medium-range, and long-range wireless protocols to ensure reliable communication under various connectivity scenarios. The wearable device supports Bluetooth 5.0 or 5.4 for energy-efficient data exchange over short distances with nearby edge nodes such as field stations, tractors, or irrigation controllers. It also supports Wi-Fi (IEEE 802.11ax) for high-throughput communication, if such networks are available on the farm. For longer-range, low-power communication, the wearable device includes a LoRa or LoRaWAN module, enabling connection to remote nodes or gateways while ensuring minimal energy consumption.An NFC module provides additional features for secure authentication or configuration near handheld terminals.

[0025] The wearable device's display system is an integral part of the user interface and has been carefully designed to ensure usability in outdoor environments and under strong glare. The device features a high-resolution OLED or LCD screen with a resolution of at least 240x240 pixels, optimized for sunlight visibility. The screen is complemented by a touch-sensitive interface and haptic feedback mechanisms that allow users to interact with the system even while wearing gloves or during highly mobile field tasks. In addition to visual feedback, the device supports noise-cancelling audio output and offers voice alerts and spoken recommendations, useful when the user's visual attention is elsewhere.

[0026] The wearable device does not operate as a standalone solution; instead, it is designed to function as part of a larger, distributed computing ecosystem that includes edge computing nodes and a central cloud analytics platform. Edge computing nodes are strategically deployed in the agricultural environment. These nodes can be mounted on mobile agricultural machinery such as tractors or drones, attached to field posts, or placed alongside other IoT infrastructure such as automated irrigation systems. These edge devices have local processing capabilities that allow them to receive data from the wearable device and perform real-time calculations using pre-cached machine learning models. These models, which may have been trained in the cloud, are regularly updated and pushed to edge devices to ensure freshness and relevance.

[0027] Edge devices serve as the first layer of intelligence by processing raw sensor data and generating immediate, context-aware insights for the farmer. For example, if the wearable device records a sudden drop in temperature and the edge device correlates this with humidity and historical trends, it could predict an impending frost and trigger an alert on the wearable device. This architecture ensures that critical alerts and insights are available to the farmer even when internet access is unavailable. The reduced latency and autonomy of edge devices provide a critical advantage in time-critical scenarios where decisions such as applying frost protection or initiating irrigation must be made without delay.

[0028] The system's cloud computing component provides the deeper analytical capabilities required for more complex, long-term, or regional insights. The cloud server aggregates data uploaded by wearable and edge devices when a connection is available. This data includes time-stamped environmental measurements, location information, user activity logs, and responses to alerts or recommendations. The cloud platform uses advanced machine learning algorithms, including neural networks and ensemble models, to process this data and create predictive models for crop diseases, pest outbreaks, irrigation schedules, or optimal planting and harvest times. These models are tailored to specific geographic regions, crop types, and even individual farmer preferences.The platform can integrate with third-party meteorological APIs and agricultural databases to further improve its forecast accuracy.

[0029] Once these models are trained or updated, they are compressed and transferred to edge nodes, which cache them for offline use. The cloud also sends updated forecast data, regional advisories, and best agricultural practices to the wearable device whenever it syncs. This bidirectional data flow between cloud, edge, and wearable components ensures that the entire ecosystem remains synchronized, up-to-date, and adaptable to changing field conditions.

[0030] Power management is another critical area addressed by this invention. The wearable device contains a 300 to 500 mAh lithium-ion battery optimized to provide a battery life of 5 to 7 days under normal operating conditions. An intelligent power management algorithm modulates the sensing frequency, screen brightness, and wireless activity based on the context and urgency of the data. For example, during extreme weather conditions or periods of disease risk, the sensing and communication intervals can be increased to provide more frequent updates. The device also supports solar charging via an integrated photovoltaic cell embedded in the armband or case, enabling continuous operation during extended field deployments. Wireless charging functionality is also supported for convenient maintenance.

[0031] The system's software architecture enables modular and scalable deployment. The firmware on the wearable device includes a real-time operating system (RTOS) that manages task scheduling, power management, data storage, and secure communication. The edge computing nodes run lightweight analytics frameworks capable of running TensorFlow Lite or equivalent models for on-device inference. The cloud platform operates on a scalable microservices architecture that supports thousands of users simultaneously, maintaining personalized services through secure user profiles.

[0032] The invention further envisions a complete functional workflow that begins with continuous data collection by the wearable device. This data is cached locally and transmitted to the edge device based on a priority queue—critical alerts such as pest risk are processed immediately, while background monitoring data is stored and uploaded later. The edge node processes the incoming data using cached models and delivers an actionable insight. This is displayed to the user on the wearable device via visual, audible, or haptic alerts. The farmer can acknowledge the alert, dismiss it, or log a follow-up action (e.g., turning on an irrigation system), which is in turn recorded and transmitted upstream to improve the model's learning loop.The entire feedback mechanism is a closed loop that supports both autonomous and supported decision making by the farmer.

[0033] In practical application, this system serves a variety of use cases. A farmer can use the portable device to receive early warnings about fungal infections that may occur under certain moisture conditions, or to monitor soil moisture levels and receive irrigation advice tailored to specific crop growth stages. It can also provide hourly weather forecasts tailored to the farm's exact location, rather than relying on broad regional forecasts. Furthermore, in livestock farming, it can track movement and environmental stressors that could affect animal health. For high-value crops such as vineyards or greenhouses, the system's detailed environmental awareness enables precise UV monitoring and microclimate control.

[0034] Security and data protection are integral to the design. All data transmitted between the wearable device, the edge nodes, and the cloud servers is encrypted using AES or equivalent standards. Authentication occurs at both the device and server levels, and data sovereignty protocols ensure that farmers retain control over how their data is used, shared, or stored, in compliance with international data protection regulations such as the GDPR.

[0035] In conclusion, the invention represents a fully integrated, wearable IoT ecosystem for agriculture that bridges the digital divide in agriculture by providing intelligent, localized, and context-aware decision support. By combining environmental sensing, hybrid edge-cloud computing, robust communications, an intuitive user interface, and adaptive energy management, this invention addresses critical limitations in modern agriculture and empowers farmers to make timely, informed decisions with minimal manual effort and maximum autonomy. It has the potential to transform the way agriculture is conducted in both technologically advanced and resource-constrained environments by providing equal levels of scalability, sustainability, and resilience. Description of the numbers in the attached drawing 100 systems 101 Microcontroller-based processing unit 102 Variety of integrated environmental sensors 103 Wireless communication modules 104 GPS module 105 Display interface

Claims

[1] A portable device for agricultural applications, consisting of: a microcontroller-based processing unit (101); a variety of integrated environmental sensors (102), including temperature, humidity and barometer sensors; wireless communication modules (103) that support Bluetooth, Wi-Fi and LoRa protocols; a GPS module (104) for geolocation; a display interface (105) with touch and haptic feedback; the device is configured to communicate with nearby edge computing nodes to receive localized agricultural predictions based on real-time environmental data. [2] Device according to claim 1, further comprising an energy management system including a rechargeable lithium-ion battery, a solar charger and intelligent frequency modulation for extended field operations. [3] Device according to claim 1, wherein the edge computing nodes are deployed on agricultural equipment or field stations and are configured to run machine learning models from a cloud platform to generate local predictions. [4] Device according to claim 1, which additionally includes synchronization functions with a cloud computing platform that enable the uploading of environmental data and the downloading of updated forecast models and regional advice. [5] Device according to claim 1, wherein the environmental sensors are configured to provide microclimate-specific inputs for predicting crop diseases, irrigation planning and pest outbreaks. [6] System for real-time analysis in agriculture comprising the portable device according to claim 1 and a distributed network of edge computing nodes, wherein this system processes sensor data locally and generates actionable recommendations that are displayed on the portable interface. [7] System according to claim 6, wherein the portable device continues to receive real-time insights from edge nodes during network interruptions and updates its predictive models from the cloud platform when the internet connection is restored.

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