Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a pest detection system and particularly to a smart pest detection system for real-time agricultural pest management.
Description of Related Art
[002] Pests pose a significant challenge to agricultural productivity worldwide, leading to substantial crop losses and economic setbacks. Traditional pest control methods, such as manual monitoring, pesticide application, and chemical interventions, have been widely used for decades. However, these approaches often suffer from inefficiencies, requiring extensive labor, time, and expertise. Additionally, manual identification methods are prone to human error, leading to misdiagnosis and delayed intervention. The increasing demand for sustainable agricultural practices necessitates the development of innovative pest management solutions that enhance accuracy while minimizing environmental impact.
[003] Recent advancements in technology have led to the adoption of digital tools for pest detection and control. Some existing solutions include mobile applications utilizing artificial intelligence (AI) for image-based classification, drone-assisted remote sensing for field monitoring, and electronic traps embedded with sensors for pest measurement. However, these solutions have notable limitations. Many require human intervention for data input and analysis, lack predictive capabilities, and operate in static conditions without adapting to environmental variations. Moreover, existing AI-based approaches primarily focus on classification rather than forecasting pest outbreaks, limiting their effectiveness in proactive pest management.
[004] A comprehensive approach to pest management necessitates real-time monitoring, predictive analysis, and automated countermeasures. The integration of AI with the Internet of Things (IoT) has shown promise in enhancing pest detection and prevention. Emerging research suggests that deep learning models can improve classification accuracy, while recurrent neural networks (RNN) can predict pest behavior based on historical patterns. Despite these advancements, the lack of a unified, real-time solution combining detection, prediction, and automated intervention remains a significant gap in the field. Addressing these challenges is critical for the development of an efficient and adaptive pest management system that can reduce reliance on chemical pesticides, optimize agricultural yields, and promote sustainable farming practices.
[005] There is thus a need for an improved and advanced a smart pest detection system for real-time agricultural pest management that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a smart pest detection system for real-time agricultural pest management. The system comprising scanning units, installed in a preset pattern across an agricultural premise, adapted to scan for pests. The system further comprising elimination units, installed in the preset pattern across the agricultural premise, adapted to store countermeasures for deactivation of the pests. The system further comprising a processor communicatively connected to the scanning units and to the elimination units. The processor is configured to receive a presence of the pests in the agricultural premise from the scanning units; interpolate a location of the pests in the agricultural premise; command a Convolutional Neural Network (CNN) to recognize the pests present in the agricultural premise; activate the corresponding elimination units for release of the stored countermeasures for deactivation of the pests in the agricultural premise; and command a Recurrent Neural Network (RNN) to analyze historical pest data and predict future pest outbreaks based on movement patterns and real-time environmental conditions.
[007] Embodiments in accordance with the present invention further provide a method for smart pest detection for real-time agricultural pest management. The method comprising steps of receiving a presence of pests in an agricultural premise from scanning units; interpolating a location of the pests in the agricultural premise; commanding a Convolutional Neural Network (CNN) to recognize the pests present in the agricultural premise; activating corresponding elimination units for release of stored countermeasures for deactivation of the pests in the agricultural premise; and commanding a Recurrent Neural Network (RNN) to analyze historical pest data and predict future pest outbreaks based on movement patterns and real-time environmental conditions.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a smart pest detection system for real-time agricultural pest management.
[009] Next, embodiments of the present application may provide a smart pest detection system that continuously monitors pest activity, hence ensuring instant identification and response, unlike traditional methods that rely on manual inspection.
[0010] Next, embodiments of the present application may provide a smart pest detection system that analyzes historical data and forecasts potential pest outbreaks, enabling farmers to take preventive action before major crop damage occurs.
[0011] Next, embodiments of the present application may provide a smart pest detection system that helps to reduce excessive pesticide use, lower costs, and minimize environmental impact.
[0012] Next, embodiments of the present application may provide a smart pest detection system that reduces dependency on internet connectivity and ensures faster decision-making.
[0013] Next, embodiments of the present application may provide a smart pest detection system that features high detection accuracy and adaptability to changing environmental conditions, making it more efficient over time compared to static pest detection solutions.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1 illustrates a smart pest detection system for real-time agricultural pest management, according to an embodiment of the present invention; and
[0018] FIG. 2 depicts a flowchart of a method for smart pest detection for real-time agricultural pest management, according to an embodiment of the present invention.
[0019] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0020] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0021] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0022] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0023] FIG. 1 illustrates a smart pest detection system 100 (hereinafter referred to as the system 100) for real-time agricultural pest management, according to an embodiment of the present invention. The system 100 may be adapted to detect pests. Further, upon detection, the system 100 may be adapted to deactivate and eliminate the pests. The pests controlled by the system 100 may be, but not limited to, insects, rodents, flies, and so forth. The system 100 may be installed in a polyhouse, a farmhouse, a grain silo, and so forth.
[0024] The system 100 may comprise scanning units 102, elimination units 104, a processor 108, a Convolutional Neural Network (CNN) 110, a Recurrent Neural Network (RNN) 112, and a computing unit 114.
[0025] In an embodiment of the present invention, the scanning units 102 may be installed in a preset pattern across an agricultural premise. The scanning units 102 may be adapted to scan for pests. The scanning units 102 may be, but not limited to, a camera, a sensor, a trip wire, a thermal inducer, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the scanning units 102, including known, related art, and/or later developed technologies.
[0026] In an embodiment of the present invention, the elimination units 104 may be installed in the preset pattern across the agricultural premise. The elimination units 104 may be adapted to store countermeasures 106 for deactivation of the pests. The countermeasures 106 may be, but not limited to, a chemical reagent, an insecticide, a pesticide, an inflammable-combustible gas, an ultrasound emitter, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the countermeasures 106, including known, related art, and/or later developed technologies. The elimination units 104 may be adapted to dynamically adjust the countermeasures 106 usage by targeting specific affected areas, thereby minimizing excessive pesticide application and reducing environmental impact.
[0027] In an embodiment of the present invention, the processor 108 may be connected to the scanning units 102 and to the elimination units 104. The processor 108 may be configured to receive a presence of the pests in the agricultural premise from the scanning units 102. The processor 108 may be configured to interpolate a location of the pests in the agricultural premise. The processor 108 may be configured to command the Convolutional Neural Network (CNN) 110 to recognize the pests present in the agricultural premise. The Convolutional Neural Network (CNN) 110 may be trained on a large dataset of pest images, enabling recognition of multiple pest species with high accuracy. The processor 108 may be configured to activate the corresponding elimination units 104 for release of the stored countermeasures 106 for deactivation of the pests in the agricultural premise.
[0028] In some embodiments of the present invention, before activating the elimination units 104, the processor 108 may be configured to determine that no humans are present in surroundings of the agricultural premise to prevent potential unintended exposure. The detection of human presence may be achieved through infrared sensors, motion detectors, cameras with AI-based recognition, biometric scanning, or a combination thereof. In an embodiment of the present invention, the processor 108 may further comprise a safety alert mechanism designed to notify individuals who may be sensitive to ultrasonic emissions, such as pregnant women and children.
[0029] The processor 108 may be configured to transmit an alert to the computing unit upon determining the presence of such individuals within or in proximity to the agricultural premise through the scanning units 102. The alert may be transmitted as relay notifications through various channels such as mobile applications, SMS alerts, audible alarms, or visual indicators like LED signals. The alert may also be transmitted to wearable devices or smart home assistants within a proximal range to provide real-time notifications. In cases where an individual enters the agricultural premise while the elimination units 104 may be active, the system 100 may be configured to automatically deactivate the elimination units 104 to ensure safety.
[0030] The processor 108 may further be configured to command the Recurrent Neural Network (RNN) 112 to analyze historical pest data and predict future pest outbreaks based on movement patterns and real-time environmental conditions. The Recurrent Neural Network (RNN) 112 may further compile the real-time environmental conditions such as, but not limited to, a temperature, a humidity, a crop type, a past pest occurrences, and so forth, for predicting the future pest outbreaks.
[0031] The processor 108 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, Edge AI device, a Jetson Nano, a Raspberry Pi, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processor 108, including known, related art, and/or later developed technologies.
[0032] In an embodiment of the present invention, the computing unit may be adapted to receive the predicted future pest outbreaks. In an embodiment of the present invention, the computing unit 114 may be adapted for remote monitoring of the scanning units 102 and the elimination units 104. The computing unit 114 may further be adapted for a control interface (not shown) of the elimination units 104. The computing unit 114 may be, but not limited to, a personal computer, a desktop, a server, a laptop, a tablet, a mobile phone, a notebook, a netbook, a smartphone, a wearable device, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computing device, including known, related art, and/or later developed technologies.
[0033] In an exemplary embodiment of the present invention, the system 100 may be implemented in a hibiscus plantation for efficient pest detection and elimination. Hibiscus plants are highly susceptible to various pests, including aphids, whiteflies, spider mites, and mealybugs, which can significantly impact plant health and yield. The implementation of the system 100 in a hibiscus plantation ensures real-time pest monitoring and targeted pest control, thereby reducing dependency on excessive pesticide use and promoting sustainable agricultural practices. The system 100 may also be applied to other pest-prone crops such as tomatoes, cotton, citrus fruits, and rice. These crops often face infestations from pests like bollworms in cotton, fruit flies in citrus orchards, stem borers in rice, and tomato hornworms in tomato plantations. Implementing the system 100 in these agricultural settings ensures early pest detection, precise countermeasure deployment, and reduced environmental impact.
[0034] The scanning units 102 may be strategically positioned throughout the hibiscus plantation and other crop fields to continuously monitor pest activity. These scanning units 102 may include high-resolution cameras and multispectral sensors capable of capturing detailed images and infrared signatures of pests infesting the crops. Additionally, AI-integrated image processing allows for accurate identification of pest species affecting hibiscus crops, tomatoes, cotton, citrus fruits, and rice, thereby enabling precise pest control measures.
[0035] Upon detection of pests, the elimination units 104 may be activated to deploy targeted countermeasures 106. For instance, in the case of aphid infestations in hibiscus, a controlled release of biological agents such as ladybugs or neem-based insecticides may be initiated. Similarly, for cotton bollworms, pheromone traps and targeted insecticide sprays may be used. The dynamic adjustment of countermeasure deployment ensures minimal environmental impact while effectively mitigating pest threats. The processor 108 may analyze pest population trends and environmental conditions such as humidity and temperature to anticipate potential outbreaks in the hibiscus plantation and other crops. By leveraging the predictive capabilities of the Recurrent Neural Network (RNN) 112, the system 100 can provide farmers with timely insights and preemptive recommendations for pest management. Furthermore, remote monitoring through the computing unit 114 allows farmers to oversee pest control operations from any location for continuous protection of various crops.
[0036] In an embodiment of the present invention, the system 100 may also incorporate an adaptive irrigation mechanism synchronized with pest detection data. Since excessive moisture levels can attract pests like fungus gnats and mealybugs in hibiscus, as well as rice pests like stem borers, the system 100 may regulate irrigation schedules based on real-time environmental data to optimize plant health and reduce pest susceptibility. By integrating the system 100 within hibiscus plantations and other pest-prone crops, farmers benefit from a technologically advanced, data-driven approach to pest management. The real-time detection, targeted elimination, and predictive analytics offered by the system 100 enhance the overall productivity and sustainability of various agricultural practices while minimizing chemical pesticide usage and environmental impact.
[0037] It should be understood that the embodiments described above are merely exemplary implementations of the present invention. Any references to specific applications are for illustrative purposes only and should not be construed as limiting the scope of the present invention.
[0038] FIG. 2 depicts a flowchart of a method 200 for the smart pest detection for the real-time agricultural pest management, according to an embodiment of the present invention.
[0039] At step 202, the system 100 may receive the presence of the pests in the agricultural premise from the scanning units 102.
[0040] At step 204, the system 100 may interpolate the location of the pests in the agricultural premise.
[0041] At step 206, the system 100 may command the Convolutional Neural Network (CNN) 110 to recognize the pests present in the agricultural premise.
[0042] At step 208, the system 100 may activate the corresponding elimination units 104 for release of the stored countermeasures 106 for deactivation of the pests in the agricultural premise.
[0043] At step 210, the system 100 may command the Recurrent Neural Network (RNN) 112 to analyze the historical pest data and predict the future pest outbreaks based on the movement patterns and the real-time environmental conditions.
[0044] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0045] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A smart pest detection system (100) for real-time agricultural pest management, the system (100) comprising:
scanning units (102), installed in a preset pattern across an agricultural premise, adapted to scan for pests;
elimination units (104), installed in the preset pattern across the agricultural premise, adapted to store countermeasures (106) for deactivation of the pests; and
a processor (108) communicatively connected to the scanning units (102) and to the elimination units (104), characterized in that the processor (108) is configured to:
receive a presence of the pests in the agricultural premise from the scanning units (102);
interpolate a location of the pests in the agricultural premise;
command a Convolutional Neural Network (CNN) (110) to recognize the pests present in the agricultural premise;
activate the corresponding elimination units (104) for release of the stored countermeasures (106) for deactivation of the pests in the agricultural premise; and
command a Recurrent Neural Network (RNN) (112) to analyze historical pest data and predict future pest outbreaks based on movement patterns and real-time environmental conditions.
2. The system (100) as claimed in claim 1, wherein the scanning units (102) is selected from a camera, a sensor, a trip wire, a thermal inducer, or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the countermeasures (106) stored in the elimination units (104) are selected from a chemical reagent, an insecticide, a pesticide, an inflammable-combustible gas, an ultrasound emitter, or a combination thereof.
4. The system (100) as claimed in claim 1, wherein the elimination units (104) dynamically adjusts the countermeasures (106) usage by targeting specific affected areas, thereby minimizing excessive pesticide application and reducing environmental impact.
5. The system (100) as claimed in claim 1, wherein the processor (108) is selected from an Edge AI device, a Jetson Nano, a Raspberry Pi, or a combination thereof.
6. The system (100) as claimed in claim 1, wherein the Convolutional Neural Network (CNN) (110) is trained on a large dataset of pest images, enabling recognition of multiple pest species with high accuracy.
7. The system (100) as claimed in claim 1, wherein the Recurrent Neural Network (RNN) (112) compiles the real-time environmental conditions selected from a temperature, a humidity, a crop type, a past pest occurrences, or a combination thereof, for predicting the future pest outbreaks.
8. The system (100) as claimed in claim 1, comprising a computing unit adapted to receive the predicted future pest outbreaks.
9. A method (200) for smart pest detection for real-time agricultural pest management, the method (200) is characterized by steps of:
receiving a presence of pests in an agricultural premise from scanning units (102);
interpolating a location of the pests in the agricultural premise;
commanding a Convolutional Neural Network (CNN) (110) to recognize the pests present in the agricultural premise;
activating corresponding elimination units (104) for release of stored countermeasures (106) for deactivation of the pests in the agricultural premise; and
commanding a Recurrent Neural Network (RNN) (112) to analyze historical pest data and predict future pest outbreaks based on movement patterns and real-time environmental conditions.
10. The method (200) as claimed in claim 9, wherein the Recurrent Neural Network (RNN) (112) compiles the real-time environmental conditions selected from a temperature, a humidity, a crop type, a past pest occurrences, or a combination thereof, for predicting the future pest outbreaks.
Date: March 07, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant