Description:FIELD OF THE INVENTION
The present invention relates to precision agriculture, specifically to an advanced system for individual plant-level sunlight monitoring and management. The invention focuses on optimizing light conditions for plant growth by integrating real-time data collection, plant physiology models, and adaptive decision-making through artificial intelligence.
BACKGROUND OF THE INVENTION
Efficiently managing sunlight exposure for optimal plant growth remains a challenge in precision agriculture particularly at the single-plant level. Traditional sunlight monitoring methods are either broad and generalized or labor-intensive lacking the necessary granularity to capture key sunlight parameters like Photo-synthetically Active Radiation (PAR), light intensity, photoperiod and light quality at an individual plant level. This results in an inability to provide targeted interventions to correct deviations from ideal light conditions ultimately leading to suboptimal photosynthesis rates and plant growth.
Traditional agricultural methods for monitoring sunlight exposure rely on field-level or greenhouse-level observations, providing only basic data on light intensity. These conventional techniques fail to capture detailed sunlight parameters such as Photosynthetically Active Radiation (PAR), photoperiod, and spectral quality at the individual plant level. Furthermore, most existing systems do not offer real-time adaptation, resulting in suboptimal growth conditions. Current decision-making approaches remain manual and subjective, limiting the ability to dynamically adjust environmental factors in response to plant needs. The present invention overcomes these challenges by introducing a system capable of continuous, high-resolution monitoring and adaptive management of sunlight exposure for individual plants.
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 introduces a comprehensive and scalable system for real-time, individual plant-level sunlight monitoring and management. The system employs advanced sensors to collect granular data on critical sunlight parameters, including PAR, light intensity, photoperiod, and spectral quality. These sensors are strategically positioned at the plant canopy level, ensuring accurate and localized data collection. The captured data is then processed using plant physiology models tailored to specific species, allowing for precise analysis and optimization of light conditions to enhance plant growth.
A key feature of the invention is its ability to provide adaptive decision support. The system utilizes data-driven insights to dynamically adjust environmental factors such as shading and artificial lighting. By continuously analyzing real-time data, it ensures that plants receive optimal sunlight exposure tailored to their specific growth requirements. The system further enhances decision-making by presenting actionable recommendations to farmers through an AI-powered dashboard.
Designed for scalability and cost efficiency, the system incorporates modular hardware components and wireless communication, reducing infrastructure costs while enabling widespread deployment in various agricultural settings. Unlike traditional methods, which rely on static data and manual intervention, this invention provides an intelligent, automated solution that significantly enhances plant productivity.
Additionally, the invention’s AI-powered dashboard offers an intuitive interface that simplifies complex agricultural decision-making. It provides real-time insights, predictive analytics, and customized recommendations based on continuous monitoring, ensuring that farmers can make informed decisions with minimal manual effort.
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.
The proposed solution introduces a plant level sunlight monitoring and management system intended to revolutionize precision agriculture. It features real-time data collection using advanced sensors that capture granular data on key sunlight parameters such as PAR, light intensity, photoperiod and spectral quality directly at the plant canopy level. This data is seamlessly integrated with plant physiology models adapted to specific species permitting an accurate evaluation of light conditions against optimal growth requirements. The system integrates adaptive decision support employing a data-driven approach to dynamically adjust environmental factors such as shading and artificial lighting while affording actionable insights to farmers through an AI-powered dashboard. It is built for low-cost scalability, utilizing modular, cost-efficient hardware and wireless communication ensuring widespread applicability across diverse agricultural settings without the need for extensive infrastructure.
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.
The proposed solution introduces a plant level sunlight monitoring and management system intended to revolutionize precision agriculture. It features real-time data collection using advanced sensors that capture granular data on key sunlight parameters such as PAR, light intensity, photoperiod and spectral quality directly at the plant canopy level. This data is seamlessly integrated with plant physiology models adapted to specific species permitting an accurate evaluation of light conditions against optimal growth requirements. The system integrates adaptive decision support employing a data-driven approach to dynamically adjust environmental factors such as shading and artificial lighting while affording actionable insights to farmers through an AI-powered dashboard. It is built for low-cost scalability, utilizing modular, cost-efficient hardware and wireless communication ensuring widespread applicability across diverse agricultural settings without the need for extensive infrastructure.
The proposed system initiates a comprehensive solution for precision agriculture by focusing on individual plant-level sunlight monitoring and management. It employs advanced sensors for real-time data collection, capturing granular information on Photosynthetically Active Radiation (PAR), light intensity, photoperiod and spectral quality directly at the plant canopy level. This dataset is integrated with plant physiology models tailored to specific species enabling precise analysis of light conditions and their alignment with optimal growth parameters. The system also includes adaptive decision support leveraging data-driven insights to dynamically adjust environmental factors includes shading or artificial lighting while presenting actionable recommendations to farmers through an intuitive AI powered dashboard. The solution is designed for low-cost scalability, utilizing modular, cost-efficient hardware and wireless communication ensuring widespread applicability across diverse agricultural settings without the need for extensive infrastructure.
Traditional methods of sunlight monitoring in agriculture are typically limited to field-level or greenhouse-level observations providing only basic data on light intensity. These methods often lack granularity making it difficult to assess sunlight parameters at the level of individual plants. Furthermore, they do not monitor critical factors such as Photosynthetically Active Radiation (PAR), photoperiod or spectral quality which are essential for optimizing plant growth. Real-time adaptation is either limited or nonexistent with most decisions relying on manual, subjective assessments rather than precise data-driven insights. The proposed system addresses these limitations by offering individual plant-level monitoring with advanced sensors that capture a comprehensive range of sunlight parameters including PAR, photoperiod and spectral quality. Integrated with plant physiology and growth models, the system provides continuous monitoring and adaptive feedback, enabling precise, AI-driven recommendations. Additionally, it is designed to be cost-efficient, modular and scalable ensuring its feasibility for diverse agricultural settings. This approach significantly enhances decision-making replacing traditional manual methods with an intelligent data-centric system.
The proposed system consists of several key components, including advanced light sensors, plant physiology models, an AI-powered decision support system, and a wireless communication network. The light sensors are capable of capturing real-time data on PAR, light intensity, photoperiod, and spectral quality. These sensors are deployed at the canopy level of individual plants, ensuring that the measurements accurately reflect the plant’s actual exposure to sunlight.
The collected sunlight data is transmitted to a central processing unit, where it is analyzed using plant physiology models. These models are tailored to specific plant species, allowing the system to determine optimal sunlight conditions based on growth requirements. The system continuously compares the real-time data with predefined optimal thresholds, identifying any deviations that could impact plant health and productivity.
When discrepancies are detected, the AI-powered decision support system evaluates possible corrective actions. These may include dynamically adjusting shading mechanisms or artificial lighting to maintain ideal sunlight exposure. The system also considers external environmental factors, such as weather conditions and seasonal variations, ensuring that the adjustments are contextually relevant.
The AI-powered dashboard acts as an interface between the system and the farmer, providing actionable recommendations in real time. The dashboard displays visual insights, historical data trends, and predictive analytics, allowing farmers to make informed decisions. Additionally, it enables remote monitoring and control, ensuring that adjustments can be made without the need for constant physical intervention.
The modular hardware design of the system allows for easy scalability. Each sensor node is designed to operate autonomously while communicating with a central processing unit via a wireless network. This decentralized approach ensures that the system can be deployed across large agricultural areas without requiring extensive infrastructure investments. The use of cost-efficient components further enhances the system’s feasibility for widespread adoption.
The system’s adaptability ensures that it can be applied in various agricultural settings, including open fields, greenhouses, and vertical farms. By providing continuous, high-resolution monitoring, it replaces traditional manual assessments with a data-driven approach that significantly improves plant growth and productivity. 
, Claims:1. A system for individual plant-level sunlight monitoring and management, comprising:
o advanced light sensors configured to capture real-time data on Photosynthetically Active Radiation (PAR), light intensity, photoperiod, and spectral quality;
o a central processing unit analyzing collected data using plant physiology models; and
o an AI-powered decision support system providing actionable recommendations.
2. The system as claimed in claim 1, wherein the light sensors are positioned at the plant canopy level to ensure accurate measurement of sunlight exposure.
3. The system as claimed in claim 1, wherein the AI-powered decision support system dynamically adjusts environmental factors, including shading and artificial lighting, based on real-time data analysis.
4. The system as claimed in claim 1, further comprising a wireless communication network enabling real-time data transmission between sensor nodes and the central processing unit.
5. The system as claimed in claim 1, wherein the plant physiology models are tailored to specific plant species, optimizing sunlight exposure based on predefined growth parameters.
6. The system as claimed in claim 1, further comprising an AI-powered dashboard that provides real-time insights, predictive analytics, and historical data trends.
7. The system as claimed in claim 1, wherein the modular design enables scalability and deployment across diverse agricultural environments.
8. The system as claimed in claim 1, wherein the system integrates weather conditions and seasonal variations into its adaptive decision-making process.
9. The system as claimed in claim 1, wherein remote monitoring and control functionalities are provided through the AI-powered dashboard.
10. The system as claimed in claim 1, wherein the decision support system minimizes manual intervention by automating adjustments to maintain optimal sunlight conditions.