AUTOMATED REAL-TIME CROP PROTECTION SYSTEM

Abstract

The present invention discloses an automated system for real-time crop protection against peacock intrusions using machine learning and IoT technologies. High-resolution cameras [102] and motion sensors monitor the field, with video feeds processed by a central microcontroller [104] running a trained machine learning model to detect peacocks specifically. Upon detection, the system triggers non-harmful deterrents, such as sound emitters, lights, or sprinklers, to scare the peacocks away. Additionally, a GSM module [106] sends an alert to the landowner, keeping them informed of field activity. All detection events are logged for analysis, allowing the system to improve over time. Connected to an IoT platform, the system enables remote monitoring and control, making it adaptable, efficient, and humane. This solution provides effective crop protection while promoting wildlife safety and sustainable agricultural practices.

Specification

Description:FIELD OF THE INVENTION:
The present invention relates to an improved, automated system for crop protection, particularly for detecting and deterring peacock intrusions in real-time using machine learning and IoT devices, and more particularly for alerting landowners and activating deterrent mechanisms without harming wildlife.
BACKGROUND OF THE INVENTION:
Peacocks are known to feed on seeds, fruits, and tender crops, leading to substantial economic losses for farmers. Traditional methods of crop protection, such as scarecrows, fencing, or manual patrolling, are often ineffective, labor-intensive, and not environmentally sustainable.

OBJECTS OF THE INVENTION

One or more of the problems of the conventional prior art may be overcome by various embodiments of the system and methods of the present invention.

The principal object of the present invention is to provide an improved, automated system for detecting peacock intrusions in crop fields in real-time.

Another object of the invention is to use machine learning models to accurately identify peacocks and differentiate them from other animals to minimize false detections.

A further object of the invention is to deploy non-harmful deterrent mechanisms, such as sound emitters, lights, and sprinklers, activated only when a peacock is detected.

Yet another object is to alert landowners immediately via SMS or call whenever a peacock is detected in the crop field.

An additional object of the invention is to log all detection events and responses for analysis, helping optimize deterrent strategies over time.

Another object is to enable remote monitoring and control of the system through an IoT platform, allowing farmers to manage deterrent settings as needed.

Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.

SUMMARY OF THE INVENTION

Thus, according to the basic aspect of the present invention, there is provided an automated system designed to protect crop fields from peacock intrusions using advanced machine learning and IoT technology. The system includes multiple components that work together to detect, deter, and alert landowners to peacock activity in real time, providing a non-harmful yet effective solution for wildlife management in agricultural areas.

Another aspect of the present invention, wherein the primary hardware configuration of the system, which includes a high-resolution camera module and motion sensors installed around the crop field. These components continuously monitor the field, capturing video footage and detecting movement to provide comprehensive coverage. The real-time video feed from the cameras is sent to a central microcontroller, which houses a machine learning model specifically trained to identify peacocks. This setup allows the system to continuously analyze visual data, ensuring that any intrusion by a peacock is promptly detected.

Another aspect of the present invention, wherein the use of a trained machine learning model deployed on the microcontroller or a local edge device, which processes the incoming video feed to distinguish peacocks from other animals. The model's ability to accurately identify peacocks minimizes false detections, ensuring that the system responds only when necessary. This feature is essential for effective and precise crop protection, as it reduces unnecessary activation of deterrent mechanisms and optimizes resource usage. By relying on advanced image processing and classification techniques, the system ensures that its responses are both targeted and efficient.

Another aspect of the present invention, wherein the alerting mechanism of the system, which employs a GSM module linked to the microcontroller to notify the landowner when a peacock intrusion is detected. This module is configured to send an SMS or make a call to the landowner, ensuring immediate awareness of the situation. This real-time notification allows the landowner to take additional preventive measures if needed and provides peace of mind by keeping them informed of any activity in their fields.

Another aspect of the present invention, wherein the system's deterrent mechanism, which is connected to the microcontroller and includes various non-harmful devices such as ultrasonic sound emitters, flashing lights, and sprinklers. These deterrent devices are activated automatically upon detection of a peacock, scaring the animal away from the field without causing any harm. The use of multiple deterrent options allows the system to adapt its response based on the situation, providing an effective and humane way to protect crops. This approach ensures that wildlife is deterred safely while minimizing potential damage to agricultural produce.

Another aspect of the present invention, wherein the system's data logging feature, which records all detection events and system responses. This data is stored for future analysis, allowing landowners and developers to understand behavior patterns of peacocks and evaluate the effectiveness of deterrent strategies. By analyzing this information, the system can be continuously optimized to improve accuracy and effectiveness over time. This data-driven approach enhances the adaptability of the system, ensuring it remains effective as conditions change.

Another aspect of the present invention, wherein the system's integration with an Internet of Things (IoT) platform, enabling remote monitoring and control. This feature allows the landowner to access the system's dashboard from a smartphone or computer, providing real-time updates on field activity. Through the IoT platform, the landowner can also adjust deterrent settings, manually activate or deactivate the system, and view historical data on past detection events. This remote access capability enhances the system's usability, allowing landowners to manage their crop protection measures conveniently and effectively from any location.


BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a functional flow of operations involved in the automated system designed to protect crop fields from peacock intrusions, according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION WITH REFERENCE TO THE ACCOMPANYING FIGURES

Referring to Figure 1, in an aspect, the functional flow of processes involved in the automated system designed to protect crop fields from peacock intrusions is illustrated. The present invention provides an automated system designed to protect crop fields from peacock intrusions using advanced machine learning and Internet of Things (IoT) technologies. Peacocks are known to cause significant damage to crops, and traditional deterrent methods can often be ineffective or harmful. The invention addresses these challenges by offering a humane, precise, and efficient solution that monitors, detects, and deters peacocks without causing them harm, while keeping the landowner informed of field activity in real-time.

The system's hardware configuration includes high-resolution camera [102] modules and motion sensors strategically placed around the crop field to provide comprehensive coverage. These cameras and sensors are mounted on poles, trees, or existing farm infrastructure to maximize visibility and ensure accurate monitoring. The cameras capture continuous video footage, while the motion sensors detect any movement in the field. The real-time feed is sent to a central microcontroller that serves as the system's processing hub.

At the core of this invention is a machine learning model specifically trained to identify peacocks among various animals and environmental conditions. The model is deployed on the central microcontroller [104] or a local edge device, which processes the incoming video feed and classifies objects based on their visual features. Advanced image processing techniques allow the system to distinguish peacocks from other animals, minimizing false detections and ensuring that deterrent measures are activated only when a peacock is detected. The use of a machine learning model enhances the accuracy and adaptability of the system, allowing it to respond efficiently to real-time conditions.

When the system detects a peacock, it automatically triggers a deterrent mechanism connected to the microcontroller [104]. The system includes various non-harmful deterrent devices, such as ultrasonic sound emitters, flashing lights, and sprinklers, which are installed at key points around the field. Upon detection, the microcontroller [104] activates these deterrents to scare the peacock away from the field. The multi-faceted approach allows the system to escalate responses if needed by combining different deterrents, such as sound and lights simultaneously, to enhance effectiveness. The system's use of non-harmful deterrent methods aligns with environmental protection goals, ensuring wildlife safety while protecting crops.

In addition to activating deterrents, the system also includes an alerting feature designed to keep the landowner informed. A GSM module [106] connected to the microcontroller sends an immediate notification to the landowner's smartphone via SMS or call when a peacock is detected. The real-time alert enables the landowner to take additional preventive measures, if necessary, and provides reassurance that the system is actively monitoring and protecting the crop field.

Another key feature of this invention is its data logging capability. Each detection event and corresponding system response are recorded in a centralized database for future analysis. The data logging helps track the frequency and patterns of peacock activity, which can provide insights into their behavior and movement. By analyzing this data, the system can optimize deterrent strategies, allowing it to adapt to specific patterns over time.

The system is connected to an IoT platform that enables remote monitoring and control. Through a dedicated dashboard accessible via smartphone or computer, the landowner can view real-time updates, adjust deterrent settings, and even manually activate or deactivate the system based on changing field conditions. This remote access capability provides flexibility, allowing the landowner to manage the system from any location and adapt it to seasonal changes or specific times of day. Additionally, the system's IoT integration allows for seamless updates, ensuring that it remains up-to-date with the latest technology and improvements.

Environmental factors, such as weather conditions and time of day, are considered in the system's design to ensure optimal performance. For instance, deterrent settings can be adjusted based on day or night conditions to minimize disturbances during non-active periods. The system's adaptability ensures that it provides targeted protection, maximizing deterrent effectiveness while avoiding unnecessary activations that could reduce system lifespan or disturb other wildlife.

The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.
, Claims:1. An automated system for crop protection, comprising:
a camera module [102]; and
motion sensors positioned around a crop field to capture images and detect movement, configured to send real-time video footage to a central microcontroller [104] equipped with a machine learning model specifically trained to detect peacocks.

2. The system as claimed in Claim 1, wherein the machine learning model deployed on the central microcontroller [104] or an edge device processes the video feed to differentiate peacocks from other animals, thereby minimizing false detections.

3. The system as claimed in Claim 1, further comprising a GSM module [106] connected to the microcontroller [104], configured to send an alert via SMS or call to the landowner upon detecting a peacock intrusion.

4. The system as claimed in Claim 1, wherein the microcontroller [104] is connected to automated deterrent mechanisms, including ultrasonic sound emitters, flashing lights, or sprinklers, which are activated upon detection of a peacock to deter it from the field without causing harm.

5. The system as claimed in Claim 1, further comprising a data logging feature to record all detection events and system responses, allowing for future analysis and optimization of deterrent strategies.

6. The system as claimed in Claim 1, wherein the system is integrated with an Internet of Things (IoT) platform, enabling remote monitoring and control, allowing the farmer to adjust deterrent settings and manually activate or deactivate the system based on real-time conditions.

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