"AI based smart agriculture monitoring and advisory system"

Abstract

The AI-based Smart Agriculture Monitoring and Advisory System is an advanced technology designed to enhance agricultural practices by integrating Internet of Things (IoT) sensors and artificial intelligence (AI) algorithms. This system collects real-time data from a network of IoT sensors deployed across agricultural fields to monitor key agronomic parameters such as soil moisture, temperature, humidity, crop health, and pest activity. The data is transmitted to a cloud-based platform where AI algorithms analyze it to generate actionable insights and provide farmers with personalized recommendations for irrigation, pest management, disease detection, and crop growth optimization. The system includes a user-friendly interface accessible via web or mobile applications, offering real-time updates, alerts, and dynamic decision support. By leveraging data-driven insights, the system helps farmers optimize resource usage, improve crop productivity, and adopt sustainable farming practices, making it an essential tool for modern, precision agriculture.

Specification

Description:Field of the invention:
[001] The field of this invention is precision agriculture, a data-driven approach to farming that leverages the Internet of Things (IoT) and artificial intelligence (AI) technologies to optimize agricultural processes. By deploying networked sensors, devices, and systems, precision agriculture allows real-time monitoring and control of critical farming operations. This approach enables farmers to make informed decisions based on real-time data about soil moisture, weather conditions, pest activity, crop health, and other vital agronomic parameters. In the context of Indian agriculture, the integration of IoT sensors and AI algorithms aims to address unique challenges such as diverse climate conditions, varied soil types, and fluctuating market demands. This invention targets the enhancement of agricultural productivity, resource efficiency, and sustainability through a comprehensive monitoring and advisory system tailored for the Indian agricultural landscape.

Background of the invention and related prior Art:
[002] Agriculture in India is characterized by diverse climatic zones, soil types, and crop varieties, making effective farm management complex and challenging. Farmers often rely on traditional practices, which can be inefficient and prone to yield losses due to factors like unpredictable weather, water scarcity, and pest outbreaks. Despite recent advancements in technology, many farmers still lack access to timely, actionable insights that could improve crop productivity and resource use. The emergence of precision agriculture, powered by IoT and AI, presents a promising solution to these issues by enabling continuous monitoring, data-driven analysis, and proactive decision-making. This invention leverages cutting-edge technologies to address the specific needs of Indian farmers, empowering them to make informed decisions, reduce risk, and promote sustainable practices that enhance productivity and resilience in an ever-evolving agricultural landscape.
[003] A patent document CN117893346A relates to wisdom agricultural management technical field that gathers discloses an AI wisdom agricultural management system that gathers based on thing networking, include: the sensor network is used for monitoring the environmental parameters of farmlands in real time, including temperature, humidity, illumination, soil pH value, soil nutrient content, carbon dioxide concentration, wind speed and rainfall; the data acquisition module is used for collecting and storing farmland environment parameter data and comprises a wireless communication function, so that the data can be transmitted and shared in real time; and the AI algorithm module is used for analyzing farmland environment parameter data and historical data. Through the internet of things and AI technology, farmland environment, analysis data and control equipment are monitored, crop production efficiency is improved, resources are saved, quality is improved, and decision support and remote operation are provided for farmers. Beneficial effects include improved yield, reduced waste, improved quality, increased safety, decision support, and convenient operation.
[004] Another patent document CN111524024A discloses an intelligent water and fertilizer irrigation system and an analysis method based on big data, wherein the intelligent water and fertilizer irrigation system comprises a water and fertilizer irrigation data center, a water and fertilizer irrigation model uploading center, a water and fertilizer irrigation operation model analysis center, an intelligent water and fertilizer integrated irrigation module and a pipeline disinfection module; according to the intelligent agricultural water and fertilizer irrigation system, on the basis that a traditional water and fertilizer irrigation system solves the problems of fertilization and watering, further deep analysis and discussion are carried out, a big data analysis technology, an AI technology and an Internet of things technology are combined, the traditional water and fertilizer irrigation operation mode depending on a technician is upgraded and optimized, and the water and fertilizer irrigation data model is collected, analyzed, processed and applied through a big data processing technology to realize intelligent agriculture.
[005] A document CN113313469A discloses a smart agriculture management system based on a narrowband Internet of things, which comprises a monitoring subsystem, a control subsystem, a narrowband Internet of things NB-IoT base station, a cloud platform and a mobile module, wherein the monitoring subsystem is used for monitoring the narrowband Internet of things NB-IoT base station; the monitoring subsystem is used for monitoring the agricultural environment, and after the relevant numerical value of the agricultural environment exceeds a specified range, early warning is initiated and the relevant numerical value is uploaded to the cloud platform; the control subsystem is used for realizing the accurate control of water consumption, pesticide application and fertilizer application; the cloud platform is used for receiving and storing data uploaded by each module through the narrow-band Internet of things NB-IoT base station and sending a control instruction to the control module; the mobile module is used for monitoring the agricultural environment and controlling agricultural instruments by a user at the mobile end; NB-loT communication chips are integrated in all modules, and all modules are loaded with solar panels. The intelligent agricultural monitoring subsystem constructed based on the narrowband Internet of things NB-IoT technology has the advantages of wide signal coverage range, simple networking, low cost and high upper limit on the number of accessible modules.
[006] Another document CN112241951A relates to an agricultural monitoring method, system, computer equipment and storage medium based on raspberry pi and LORA, wherein the method comprises the following steps: a convolutional neural network is set up in advance at a server side and used for carrying out pest and disease damage prediction on picture data; training the convolutional neural network at a server side to obtain a corresponding parameter file; transplanting the constructed convolutional neural network and the parameter file into a raspberry group in the gateway; when the gateway receives picture data sent by an LORA node, directly performing pest and disease damage prediction in the gateway through a convolutional neural network with trained parameters; and sending the prediction result of the convolutional neural network to a server side. According to the invention, only the pest and disease damage prediction is carried out on the picture data on the gateway formed by the raspberry group, and only the prediction result is sent to the server, so that the operation pressure of the server is greatly reduced, and the agricultural monitoring efficiency is improved.
[007] A patent document CN117933913A discloses a multi-dimensional data-based intelligent agricultural data monitoring and controlling system and an intelligent terminal, which belong to the field of detection and controlling systems and the field of multi-dimensional data, and comprise a data acquisition and sensor module, a data storage and management module, a data analysis and decision support module, a remote monitoring and control module, an automatic control module, a crop disease and pest monitoring module, a market prediction and marketing module, a resource management and optimizing module, a reporting and visualization module and a security and privacy module; the sensor module is responsible for acquiring real-time data and transmitting the real-time data to the data storage and management module; real-time data are arranged, processed and analyzed in the data storage and management module; the data analysis module uses the stored data to generate a hole; the automatic control module performs remote control through the remote monitoring and control module; the data analysis and decision support module performs the actual monitoring.
[008] None of these above patents, however alone or in combination, disclose the present invention. The invention consists of certain novel features and a combination of parts hereinafter fully described, illustrated in the accompanying drawings, and particularly pointed out in the appended claims, it being understood that various changes in the details may be made without departing from the spirit, or sacrificing any of the advantages of the present invention.


Summary of the Invention:
[009] The AI-based Smart Agriculture Monitoring and Advisory System is an innovative solution designed to transform agricultural practices in India by integrating advanced artificial intelligence (AI) with a robust Internet of Things (IoT) infrastructure. This system utilizes a network of IoT sensors deployed across agricultural fields to collect real-time data on soil moisture, temperature, humidity, crop health, and pest activity. The data is transmitted to a centralized cloud platform, where AI algorithms analyze the information to generate actionable insights, such as early detection of crop diseases, optimized irrigation schedules, and pest management strategies. The system provides farmers with an intuitive interface that offers personalized recommendations, real-time updates, and alerts, helping them make informed decisions to maximize crop yields, minimize resource wastage, and promote sustainable farming practices. By empowering farmers with data-driven tools, this system aims to enhance productivity, reduce risks, and improve long-term agricultural sustainability.

Detailed Description of the Invention with Accompanying Drawings:
[010] For the purpose of facilitating an understanding of the invention, there is illustrated in the accompanying drawing a preferred embodiment thereof, from an inspection of which, when considered in connection with the following description, the invention, its preparation, and many of its advantages should be readily understood and appreciated.
[011] The principal object of the invention is to develop AI based smart agriculture monitoring and advisory system. The AI-based smart agriculture monitoring and advisory system is a comprehensive solution designed to address the diverse challenges faced by farmers in India. This system combines IoT sensors, AI algorithms, and user-friendly interfaces to deliver real-time monitoring, predictive analytics, and personalized advisory services. Below is an in-depth technical breakdown of the system's key components and functionalities:


A. IoT Sensors Network:
1. Sensor Deployment and Types:
The system deploys a network of IoT sensors across agricultural fields to collect a wide range of agronomic data. These sensors are strategically placed to cover various zones of the field for accurate, comprehensive monitoring.
Soil Moisture Sensors: These sensors are installed at multiple depths in the soil to provide accurate moisture readings, ensuring optimal irrigation scheduling and water conservation.
Temperature and Humidity Sensors: These sensors measure ambient temperature and humidity levels, which significantly impact crop growth and disease spread.
Rainfall Gauges: These sensors capture real-time rainfall data, helping predict irrigation needs and assess water availability.
Leaf Wetness and Crop Health Sensors: Specialized sensors use image recognition capabilities to assess crop health by monitoring parameters such as leaf color, texture, and morphology. They can also detect early signs of pest infestations and diseases by analyzing visual indicators.
2. Power Supply:
The sensors are powered by energy-efficient solutions, such as solar panels or rechargeable batteries, ensuring continuous operation even in remote areas with limited access to conventional power sources.
B. Data Transmission and Storage:
1. Data Transmission:
The collected data is wirelessly transmitted to a cloud-based platform via low-power, wide-area network (LPWAN) protocols such as LoRaWAN or NB-IoT. These protocols ensure long-range, low-energy communication, ideal for rural agricultural environments.
2. Data Security:
The cloud platform employs strong encryption protocols to ensure the integrity and security of the transmitted data, preventing unauthorized access or tampering.
3. Cloud Storage and Processing:
The cloud infrastructure is scalable to handle large volumes of data generated from multiple fields over time. Data processing mechanisms are in place to process incoming data in real-time, enabling rapid analysis and timely decision-making.

C. Artificial Intelligence Algorithms:
1. Core AI Engine:
The AI engine forms the core of the system, analyzing the collected data to generate actionable insights. The system uses a combination of machine learning and deep learning algorithms tailored to agricultural applications.
Machine Learning Models: These include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble methods, which are trained on historical agronomic data, such as crop growth patterns, soil characteristics, weather conditions, and pest outbreaks.
Deep Learning for Image Recognition: Advanced deep learning techniques are used to process images from crop health sensors, detecting diseases, pests, and nutrient deficiencies automatically.
Anomaly Detection: The system includes anomaly detection algorithms that flag deviations from normal conditions, such as unexpected changes in soil moisture levels or temperature, and alert farmers to potential issues.
2. Continuous Data Analysis:
As new data flows in, the AI algorithms constantly monitor and analyze the data in real-time. This allows the system to detect patterns, identify emerging issues, and generate insights that help optimize farming practices.
D. Farmer Advisory Interface:
1. User Interface:
The system provides farmers with a web or mobile-based interface that displays real-time updates on farm conditions, including soil moisture levels, temperature, humidity, rainfall, and pest activity.
Personalized Dashboards: Each farmer has access to a customized dashboard that offers insights into crop health, growth status, and actionable recommendations based on the analysis of the data from their specific farm.
Interactive Visualization Tools: These tools allow farmers to explore historical data, view trends, compare field conditions, and analyze the impact of past decisions on current farm status.

2. Alerts and Notifications:
The system sends real-time notifications and alerts to farmers regarding critical events, such as impending weather extremes, pest infestations, or soil moisture imbalances, enabling immediate action to address potential threats.
Push Notifications: Farmers receive alerts directly to their mobile devices, providing information on how to manage emerging risks and optimize farm operations.
E. Decision Support System:
1. Proactive Recommendations:
The decision support module uses insights from AI algorithms to provide proactive recommendations tailored to the farmer's specific needs. These include advice on irrigation scheduling, nutrient management, pest control, and harvesting.
Dynamic Adjustment: The system's recommendations are not static but are updated continuously based on real-time data inputs, ensuring that farmers receive the most relevant and timely advice as conditions evolves.
2. Automated Decision-Making:
The system's AI engine can automate certain decision-making processes, such as triggering irrigation systems based on real-time moisture readings, adjusting fertilizer applications, or activating pest control measures when necessary.
3. External Data Integration:
The system can integrate with external data sources, such as government agricultural advisories, market trends, and weather forecasts, further enhancing the decision-making capabilities and providing farmers with a holistic view of factors that may impact their operations.
F. Sustainability and Scalability:
1. Sustainable Practices:
By optimizing resource usage such as water, fertilizers, and pesticides, the system promotes sustainable agricultural practices, reducing waste and minimizing the environmental impact of farming.
Water Conservation: The real-time monitoring of soil moisture ensures that irrigation is only carried out when necessary, helping to conserve water, especially in drought-prone regions.


2. Scalability:
The system is designed to scale, catering to farms of varying sizes, ranging from smallholder farms to large commercial agricultural operations. The modular nature of the IoT sensors and the cloud-based infrastructure allows it to be easily deployed in diverse agricultural environments.
[012] In essence, the AI-based Smart Agriculture Monitoring and Advisory System represents a sophisticated integration of IoT, AI, and cloud technologies designed to optimize agricultural practices in India. It offers real-time monitoring, predictive insights, and automated decision-making tools that empower farmers to make data-driven, informed decisions. Through continuous analysis and adaptive recommendations, the system improves productivity, mitigates risks, and promotes sustainability in farming operations. The ability to tailor recommendations to individual farmers' needs makes the system a valuable tool for enhancing crop yields, reducing input costs, and ensuring long-term agricultural resilience.
Figure 1. AI based smart agriculture monitoring and advisory system according to the embodiment of the present invention.
[013] Without further elaboration, the foregoing will so fully illustrate my invention, that others may, by applying current of future knowledge, readily adapt the same for use under various conditions of service. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention.

Advantages over the prior art
[014] AI based smart agriculture monitoring and advisory system proposed by the present invention has the following advantages over the prior art:
1. Enhanced Crop Productivity: By providing real-time insights into soil conditions, weather patterns, and crop health, the system helps farmers optimize irrigation, fertilization, and pest management. This leads to improved crop yields and efficient resource use.
2. Water Conservation: With precise monitoring of soil moisture levels, the system enables farmers to implement targeted irrigation practices. This reduces water wastage, ensures efficient water use, and supports sustainable farming, particularly in water-scarce regions.
3. Early Detection of Pests and Diseases: The AI-driven image recognition and anomaly detection algorithms enable early identification of crop diseases, pest infestations, and nutrient deficiencies. This allows farmers to take proactive measures, preventing widespread damage and reducing the need for harmful pesticides.
4. Cost Reduction: By automating key aspects of farm management, such as irrigation and pest control, the system reduces labor costs and minimizes the overuse of inputs like water, fertilizers, and pesticides, leading to significant cost savings.
5. Optimized Resource Management: The system's recommendations help farmers make data-driven decisions regarding resource allocation, ensuring optimal use of water, fertilizers, and labor. This minimizes wastage and promotes more efficient farming practices.
6. Sustainability and Environmental Protection: The system promotes sustainable farming by encouraging practices that minimize environmental harm, such as reducing chemical pesticide use and optimizing water and fertilizer consumption. This contributes to the long-term health of the soil and surrounding ecosystems.
7. Real-time Decision-Making: With continuous data collection and analysis, the system provides real-time updates and alerts, allowing farmers to make immediate adjustments to their operations. This is especially valuable for responding quickly to changing environmental conditions, such as weather extremes or pest outbreaks.
8. Personalized Recommendations: The system tailors advice based on the specific needs of each farm, taking into account crop types, local weather conditions, and soil characteristics. This personalized approach helps farmers adopt the most suitable agricultural practices for their unique situation.
9. Risk Mitigation: The system helps farmers predict and mitigate various risks, such as crop diseases, weather fluctuations, and pest infestations, reducing the likelihood of crop failure and economic loss.
10. Improved Farmer Empowerment and Decision Support: By offering a user-friendly interface with detailed insights and decision support, the system empowers farmers to make informed choices. This increases their confidence and capability in managing their farms efficiently, leading to better outcomes.
11. Scalability and Adaptability: The system is scalable, allowing it to be used by farms of varying sizes-from smallholder farmers to large commercial farms. Its adaptability to different types of crops and environmental conditions makes it a versatile tool for a wide range of agricultural practices.
12. Integration with External Data Sources: The system can integrate with external data, such as weather forecasts, government agricultural advisories, and market trends, further enhancing the farmer's decision-making by providing a holistic view of both environmental and market conditions.
13. Data-Driven Insights for Long-Term Growth: The continuous collection and analysis of data provide valuable insights into long-term farming trends, enabling farmers to improve their practices over time, adapt to changing conditions, and increase overall farm resilience.
14. Reduced Dependency on Traditional Farming Practices: The AI-based system reduces the reliance on outdated, less efficient farming methods, transitioning farmers towards modern, technology-driven approaches that improve productivity and sustainability.
[015] In the preceding specification, the invention has been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense. Therefore, the aim in the appended claims is to cover all such changes and modifications as fall within the true spirit and scope of the invention. The matter set forth in the foregoing description and accompanying drawings is offered by way of illustration only and not as a limitation. The actual scope of the invention is intended to be defined in the following claims when viewed in their proper perspective based on the prior art.
























, Claims:We claim:
1. An AI-based Smart Agriculture Monitoring and Advisory System, comprising:
- A network of IoT sensors deployed across agricultural fields to monitor agronomic parameters including soil moisture, temperature, humidity, crop health, and pest activity;
- A cloud-based platform to receive, store, and process data transmitted wirelessly from the IoT sensors using secure communication protocols;
- An AI algorithm configured to analyze the collected data and provide actionable insights regarding crop health, irrigation, pest management, and disease detection;
- A user interface accessible via web or mobile application to deliver real-time updates, personalized recommendations, and alerts to farmers based on AI-driven analysis.
2. The system of claim 1, wherein the IoT sensors include soil moisture sensors, temperature and humidity sensors, rainfall gauges, leaf wetness sensors, and crop health monitoring devices, placed at multiple depths in the soil to optimize irrigation and detect pest infestations.
3. The system of claim 1, wherein the AI algorithm utilizes machine learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to process agronomic data, detect anomalies, and generate predictive insights to enhance farming operations.
4. The system of claim 1, wherein the AI algorithm employs deep learning techniques for automated image recognition to detect crop diseases, pests, and nutrient deficiencies based on visual indicators such as leaf color, texture, and morphology.
5. The system of claim 1, further comprising an anomaly detection module that identifies deviations from expected agronomic parameters, such as abnormal soil moisture levels or temperature, and generates real-time alerts to notify farmers of potential risks.
6. The system of claim 1, wherein the cloud-based platform uses scalable data storage and real-time data processing mechanisms to accommodate large volumes of agricultural data and provide timely insights for decision-making.
7. The system of claim 1, wherein the user interface includes a personalized dashboard displaying farm-specific insights, including crop health status, weather forecasts, irrigation schedules, and actionable recommendations based on AI analysis and historical data trends.
8. The system of claim 1, further comprising a decision support module that provides proactive, dynamically adjusted recommendations for irrigation, pest control, fertilization, and harvesting, based on continuous analysis of real-time data inputs.
9. The system of claim 1, wherein the system integrates external data sources, such as weather forecasts, government agricultural advisories, and market trends, to enhance decision-making and provide farmers with a comprehensive understanding of factors affecting farm operations.
10. A method for monitoring and advising on agricultural operations using the AI-based Smart Agriculture Monitoring and Advisory System, comprising:
- Deploying IoT sensors to monitor agronomic parameters across agricultural fields;
- Transmitting the sensor data wirelessly to a cloud-based platform for processing and analysis;
- Analyzing the data using AI algorithms to detect anomalies, predict trends, and provide insights into crop health, irrigation needs, pest control, and disease management;
- Providing farmers with real-time updates, personalized alerts, and actionable recommendations through a user interface accessible via web or mobile application.

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