Description:FIELD OF THE INVENTION
The present invention relates to the field of precision agriculture and smart irrigation systems. More specifically, it pertains to an adaptive and sustainable irrigation system that integrates Internet of Things (IoT) devices with feedforward ensemble learning models for real-time prediction and control of water distribution to crops.
BACKGROUND OF THE INVENTION
Modern agriculture often suffers from inefficient irrigation, leading to water waste and reduced crop yields. Traditional systems lack adaptability to real-time environmental changes. This project addresses the need for a smart irrigation solution using the Internet of Things (IoT) and feedforward machine learning models. IoT devices collect real-time data like soil moisture, temperature, and humidity. Feedforward learning models analyze this data to predict optimal irrigation schedules. The goal is to automate water delivery precisely when and where it’s needed. This cognitive ensemble system ensures efficient water use, increased productivity, and sustainability. It must be reliable, cost-effective, and suitable for rural deployment. The challenge lies in integrating sensors, data analytics, and automation effectively. The final system aims to support sustainable agriculture and long-term food security.
US10936871B2: A vehicular gesture control system includes sensors such as IoT (internet of things) sensors that can share data with other vehicles and that can communicate with the cloud to provide intelligent handling of the irrigation system.
US20220061236A1: An integrated multi-scale modeling platform is utilized to assess agricultural productivity and sustainability. The model is used to assess the environmental impacts of agricultural management from individual fields to watershed/basin to continental scales. In addition, an integrated irrigation system is developed using data and a machine-learning model that includes weather forecast and soil moisture simulation to determine an irrigation amount for farmers. Next, crop cover classification prediction can be established for an ongoing growing system using a machine learning or statistical model to predict the planted crop type in an area. Finally, a method of predicting key phenology dates of crops for individual field parcels, farms, or parts of a field parcel, in a growing season, can be established.
Agricultural irrigation often suffers from inefficiencies due to reliance on static schedules or manual intervention. Such methods cause water wastage, irregular supply, reduced crop yields, and unsustainable farming practices. Existing automated irrigation systems are limited by single-model machine learning approaches or rigid rule-based logic, which fail to adapt to changing soil, crop, and environmental conditions. They also lack scalability and resilience for deployment in rural or resource-constrained areas. The present invention overcomes these problems by offering a cognitive ensemble of IoT-enabled sensing and adaptive feedforward learning models that dynamically predict irrigation needs, ensure optimal water use, and improve crop productivity in a sustainable and scalable manner.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention discloses a cognitive smart irrigation system that synergizes IoT-based environmental sensing with feedforward learning ensemble models to deliver precise, adaptive, and sustainable irrigation control. The system uses distributed IoT sensors to measure soil moisture, temperature, humidity, and weather conditions. These data streams are transmitted to a central processing unit where ensemble feedforward models analyze the inputs and predict optimal irrigation schedules.
The predicted irrigation requirements are executed through automated pumps and valves that deliver water only when and where needed. The system integrates cloud-based storage and analytics, providing farmers with real-time dashboards for monitoring and manual overrides. Historical data is continuously used to improve model accuracy and adaptability.
The invention is designed for scalability and energy efficiency. Solar-powered IoT devices ensure reliable deployment even in off-grid areas. The system incorporates anomaly detection to identify faulty sensors or irregular irrigation patterns, thereby increasing reliability.
By fusing IoT infrastructure with predictive ensemble learning, the invention creates a cognitive irrigation ecosystem that reduces water waste, increases yields, lowers labor dependency, and ensures sustainability for long-term agricultural productivity.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This system uses IoT sensors to collect real-time data on soil moisture, temperature, humidity, and weather. This data is sent to a central unit where a feedforward machine learning model processes it. The model predicts the optimal irrigation amount and timing based on environmental and crop conditions. It sends commands to automated irrigation valves or pumps to water only where and when needed. The system is proactive, using predictions to prevent under- or overwatering. All data is uploaded to the cloud and displayed on a mobile/web dashboard for farmers. Farmers can monitor field conditions remotely and override settings if necessary. The model improves over time by learning from historical data. Solar-powered IoT devices ensure the system runs in off-grid rural areas. This reduces water waste, saves labor, and increases crop yield. It’s scalable, affordable, and supports sustainable agriculture practices.
The result is a smart, self-adjusting irrigation system tailored to real farm needs.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
This system uses IoT sensors to collect real-time data on soil moisture, temperature, humidity, and weather. This data is sent to a central unit where a feedforward machine learning model processes it. The model predicts the optimal irrigation amount and timing based on environmental and crop conditions. It sends commands to automated irrigation valves or pumps to water only where and when needed. The system is proactive, using predictions to prevent under- or overwatering. All data is uploaded to the cloud and displayed on a mobile/web dashboard for farmers. Farmers can monitor field conditions remotely and override settings if necessary. The model improves over time by learning from historical data. Solar-powered IoT devices ensure the system runs in off-grid rural areas. This reduces water waste, saves labor, and increases crop yield. It’s scalable, affordable, and supports sustainable agriculture practices. The result is a smart, self-adjusting irrigation system tailored to real farm needs.
This research introduces a cognitive ensemble framework that synergistically combines Internet of Things (IoT) infrastructure with feedforward learning models to create an adaptive and intelligent smart irrigation system. Unlike conventional systems that rely on static rules or isolated machine learning models, this approach integrates real-time sensor data with a multi-layered feedforward ensemble that learns and adapts dynamically to crop-specific, soil, and climatic variations. The cognitive layer enables continuous self-optimization by incorporating contextual awareness and predictive feedback, leading to precise irrigation scheduling, sustainable water usage, and enhanced agricultural productivity. The novelty lies in the fusion of ensemble learning with IoT-based sensing to form a context-aware, scalable, and sustainable irrigation system, setting it apart from existing single-model or rule-based solutions.
The invention utilizes an IoT-based sensing infrastructure that includes soil moisture sensors, temperature and humidity sensors, and weather monitoring devices placed across agricultural fields. These sensors continuously capture real-time data, which is wirelessly transmitted to a central gateway.
The gateway aggregates sensor data and forwards it to a computational unit equipped with a feedforward ensemble learning engine. The engine consists of multiple feedforward neural network models combined in an ensemble architecture to improve prediction accuracy. By averaging or weighting predictions across the ensemble, the system achieves higher robustness against variability in environmental data.
The predictive engine estimates irrigation requirements by analyzing soil moisture levels, evapotranspiration rates, crop growth stage, and weather forecasts. It computes both the quantity and timing of irrigation events, ensuring water is applied precisely where needed.
An actuator subsystem consisting of automated valves and pumps is integrated with the system. Based on the predictive outputs, the actuator subsystem executes irrigation events across different field zones. This zonal irrigation capability enables targeted watering for heterogeneous soil and crop conditions.
To enhance sustainability, the system is designed to operate with solar-powered IoT nodes. Low-power communication protocols ensure minimal energy consumption, making the system suitable for rural and resource-constrained regions.
The invention further incorporates a cloud-based data management platform. Sensor readings, irrigation records, and prediction logs are stored securely in the cloud, where they can be accessed by farmers through web and mobile dashboards. This provides remote monitoring capabilities and allows users to intervene manually if desired.
The system integrates anomaly detection algorithms to monitor sensor health and irrigation patterns. If a sensor reports inconsistent data or if irrigation deviates from expected norms, alerts are generated for timely maintenance or corrective action.
The feedforward ensemble models are trained using historical data collected over multiple growing seasons. This allows the system to learn from past irrigation cycles, crop responses, and environmental fluctuations, thereby improving accuracy over time.
The ensemble architecture supports contextual adaptability by dynamically adjusting irrigation predictions for different crop types, soil textures, and climatic zones. This versatility makes the system applicable across a wide range of agricultural contexts.
The invention is inherently scalable. Multiple IoT nodes can be deployed across large farms, with their data integrated into a unified processing unit. The modular design allows gradual expansion of the system without requiring extensive reconfiguration.
Unlike traditional irrigation systems, this invention does not rely on fixed thresholds or static schedules. Instead, it applies continuous predictive analytics that adapt to changing real-time conditions.
The system contributes significantly to sustainable agriculture by reducing overwatering and conserving water resources. It also increases crop yield through precise irrigation scheduling, reduces farmer workload, and lowers operational costs.
By integrating cognitive ensemble learning with IoT sensing, the invention provides an intelligent, autonomous irrigation solution that ensures both agricultural productivity and environmental sustainability.
BEST METHOD OF WORKING
The best method of working the invention involves deploying IoT sensor nodes across a field to monitor soil and environmental conditions. The sensors transmit data to a solar-powered central gateway connected to the predictive feedforward ensemble model. The model computes optimal irrigation schedules and transmits commands to automated pumps and valves for precise water delivery. Data is stored in a cloud-based platform accessible via a farmer’s mobile application or dashboard. Historical data continuously retrains the ensemble models, ensuring adaptive learning. The system is maintained through anomaly detection alerts, enabling reliability in long-term use.
, Claims:1. A system for smart irrigation, comprising:
a plurality of IoT sensors configured to measure soil moisture, temperature, humidity, and weather conditions;
a central processing unit including a feedforward ensemble learning engine configured to receive and analyze sensor data;
a prediction module configured to estimate optimal irrigation schedules based on environmental and crop conditions;
an actuator subsystem comprising automated pumps and valves configured to deliver water according to the prediction module outputs;
a cloud-based storage and monitoring platform configured to store data and provide a user interface for farmers; and
a power subsystem comprising solar-powered units for sustaining IoT sensors and communication devices.
2. The system as claimed in claim 1, wherein the IoT sensors are distributed across zones of an agricultural field for localized monitoring and irrigation control.
3. The system as claimed in claim 1, wherein the feedforward ensemble learning engine comprises multiple neural network models combined to provide robust predictive outputs.
4. The system as claimed in claim 1, wherein the prediction module incorporates weather forecasts, crop growth stages, and soil texture for irrigation decision-making.
5. The system as claimed in claim 1, wherein the actuator subsystem is configured to enable zonal irrigation control for heterogeneous field conditions.
6. The system as claimed in claim 1, wherein the cloud-based platform provides remote monitoring, manual override, and irrigation history visualization.
7. The system as claimed in claim 1, wherein the power subsystem includes low-power communication protocols to optimize energy efficiency.
8. A method for smart irrigation, comprising:
collecting real-time soil moisture, temperature, humidity, and weather data using IoT sensors;
transmitting the data to a central processing unit;
analyzing the data using a feedforward ensemble learning engine;
predicting optimal irrigation schedules;
activating pumps and valves to irrigate based on the predicted schedules; and
storing data and providing remote access through a cloud-based monitoring platform.
9. The method as claimed in claim 8, wherein the ensemble learning engine is continuously retrained using historical environmental and crop data.
10. The method as claimed in claim 8, wherein anomaly detection is employed to identify faulty sensors and irregular irrigation patterns.