The Motion-Activated Picture Capture, Animal Detection, and Alert System

Abstract

The Motion-Activated Picture Capture, Animal Detection, and Alert System is an advanced wildlife monitoring project employing motion sensors and high- resolution cameras. When motion is detected, the system captures images and utilizes sophisticated algorithms for accurate animal detection and classification. Trained on diverse datasets, the machine learning algorithms continuously improve detection accuracy, adapting to various environmental conditions. The system includes a real-time alert mechanism, notifying authorities or conservationists when rare or endangered species are identified, enabling swift response to potential threats or conservation opportunities. Beyond conservation, the project has applications in research, education, and ecotourism, offering valuable data for biodiversity studies, educational engagement, and immersive wildlife experiences for visitors. This comprehensive solution leverages cutting-edge technologies to contribute significantly to wildlife protection and ecosystem preservation.

Introduction

I. INTRODUCTION

The Motion-Activated Picture Capture, Animal Detection, and Alert System is an innovative project that integrates various technologies to provide a sophisticated solution for wildlife monitoring. The system utilizes motion sensing, image capture facilitated by an ESP32 model, a buzzer for alerts, and advanced artificial intelligence (AI) technologies. The system is designed to activate in response to motion, ensuring that image capture and animal detection are triggered only when relevant movement is detected. The ESP32 model is employed for image capture, offering a robust and efficient platform for acquiring high-quality images in wildlife environments. This model is known for its capabilities in handling data and communication tasks. To provide real-time alerts, the system incorporates a buzzer. When the system detects an animal, the buzzer is triggered, instantly alerting users to the presence of the designated animal. This feature ensures prompt notification and response. specifically, trained models are used for the identification and categorization of animals. These models are designed to analyse captured images and determine the species of the detected animals with a high degree of   accuracy. The AI model is tailored for animal classification, allowing the system to not only detect the presence of animals but also categorize them based on predefined criteria. This classification capability enhances the system's ability to provide detailed information about the wildlife in its monitoring area. The project as a whole is characterized by its intelligence, reliability, and comprehensiveness. The intelligent AI algorithms enable accurate identification and classification, while the reliability of the system is enhanced by the integration of motion sensing and a buzzer for immediate alerts. The comprehensive nature of the solution encompasses the entire process from detection to alerting. The system ensures instant alerts by triggering the buzzer as soon as an animal is detected. This rapid alerting mechanism is crucial for timely responses to the presence of specific animals, allowing users to take appropriate actions or simply observe the wildlife in real-time. In summary, the Motion- Activated Picture Capture, Animal Detection, and Alert System represent a technologically advanced and well- integrated solution for wildlife monitoring. By combining motion sensing, image capture using the ESP32 model, a buzzer for alerts, and sophisticated AI technologies, this project stands as an intelligent, reliable, and comprehensive tool for detecting, classifying, and alerting the presence of animals in various environments.

II. LITERATURE REVIEW

This study focuses on Wildlife?monitoring and analysis. They have?used a camera-trap database that?has animal proposals using multilevel?graph cut. An animal detection?model using self-learned DCNN features is used. This?efficient feature set is then used for classification?using?support vector machine, KNN?and ensemble tree. They have used?Camera trap?images, DCNN,?SVM, KNN, ensemble tree,?deep learning. It?provides?accuracy of 91.4%?on?standard?camera- trap?dataset.[1] They measures the efficiency of an animal- activated scarecrow (AAS) and a 5-strand monofilament fence (MF) at reducing the white-tailed deer in Missouri, USA. AAS may be useful for short-term deterrence of deer from small areas because over time deer got habituated to the AAS and MF.[2]

A survey of Kati village, Rupnagar, India was conducted. It involves developing a device to protect crops from animal damage. They designed an Acoustic Repellant System using CNN based ML model and an IR camera to identify target animals. A Raspberry-Pi modulator has been integrated with camera, frequency generator to recognize different animals and produce frequencies to keep them away from the farm.[3] This system offers an IoT solution to protect crops from wildlife and monitor weather conditions in agriculture using low-power devices. It includes sensor networks, a robust architecture, and successful field testing, demonstrating its suitability for agricultural applications. [4] This system employs IoT and WSN technologies, featuring laser detectors, transceivers, microcontrollers, and a central server. It aims to detect and divert wildlife intrusions in agricultural fields, reducing crop damage and improving perimeter control. [5] An IoT system is proposed to mitigate conflicts between humans and wildlife in India by employing IoT, Artificial Intelligence (YOLO), and Raspberry Pi for wildlife intrusion detection and repelling, reducing injuries, property damage, and crop destruction, enhancing safety for both humans and wildlife.

[6] It introduces a smart scarecrow system to prevent crop raiding by wildlife and birds. This innovation aims to reduce human-animal conflicts and crop losses in Indian agriculture. The system uses object detection through PIR sensors, activating servo motors, speakers and light to deter animals and birds from crop raiding. It also automatically adjusts its height based on seasonal cropping patterns. [7]

It focuses on a Smart Agriculture application that defends crops from ungulate attacks using edge computing, IoT, and AI technologies. It involves the creation of an intelligent animal repellent system that can recognize and track ungulates in real-time and emit ultrasound waves to scare them away. It leverages AI, Edge Computing, IoT, and LPWAN to create an intelligent animal repulsion system. [8]A research conducted in Kerala, India evaluated crop damage caused by wild animals and identified 10 species that result in financial losses. Asian elephants, wild pigs, Indian crested porcupines, giant squirrels, and Indian peafowls. It tells importance of implementing protective measures to address human-wildlife conflict. It also emphasizes the need for such tech. To prevent economic losses. [9] This study introduces a smart scarecrow system to prevent crop raiding by wildlife and birds. This innovation aims to reduce human-animal conflicts and crop losses in Indian agriculture. The system uses object detection through PIR sensors, activating servo motors, speakers, and lights to deter animals and birds from crop raiding. It also automatically adjusts its height based on seasonal cropping patterns. [10]

III. METHODOLOGY

The model is successfully assembled the necessary hardware components including motion sensors, cameras, and microcontrollers. Initial stages of software development are complete. We've written code to capture images upon motion detection and begun implementing algorithms for animal detection. Basic functionality testing has been conducted to ensure that the motion sensors trigger image capture reliably. Developing accurate animal detection algorithms has proved to be more challenging than anticipated. We are continuously refining these algorithms to improve accuracy and reduce false positives. Integrating different hardware components and ensuring seamless communication between them has presented some challenges. We are actively working to resolve these issues. Our immediate focus will be on optimizing the animal detection algorithms to enhance accuracy and efficiency. We conducted field tests to evaluate its performance in real-world scenarios. The system is now capable of reliably detecting motion, capturing images, and accurately identifying animals within the monitored area.

This initial step involves a motion sensor detecting movement. The sensor could be part of a security system, surveillance camera, or any other application where motion triggers subsequent actions. Upon detecting motion, a camera module (ESP-32) captures an image. This image is a snapshot of the area where the motion was detected. The purpose of capturing the image is to provide visual information of animal present in area. The captured image undergoes processing. Image processing techniques Identifying relevant features or patterns. Then Locate specific objects within the image. After processing, the image is compared with a pre-existing dataset. This dataset likely contains reference images or patterns.

The comparison aims to identify any matches or anomalies. If the dataset includes known intruders’ images, a match could trigger the buzzer. The purpose of the buzzer is the animal will get frightened of that sound and run away from the farm. Finally, the farmer receives the alert. A message sent to the farmer’s phone. The farmer can then take appropriate action based on the alert received.

This flowchart outlines a process where motion detection triggers image capture, processing, and alerting mechanisms.

IV. RESULTS AND DISCUSSION

The Motion-Activated Picture Capture, Animal Detection, and Alert System, conceptualized as an innovative scarecrow for farms, has yielded promising results in its application to wildlife deterrence and crop protection. Employing motion sensors and the ESP32 CAM module, the system effectively detected wildlife within farm perimeters, promptly triggering the capture of high-resolution images. The image capture quality facilitated clear identification of wildlife species, enhancing the system's utility for assessing potential threats to crops. The real-time alert mechanism successfully notified farmers upon wildlife intrusion, empowering them to take immediate action to prevent or minimize damage. The system's adaptability to diverse environmental conditions was evident, ensuring consistent performance across changing seasons. Moreover, the integration of machine learning algorithms minimized false alarms by distinguishing between harmless and potentially harmful animals. The system's cost-effectiveness, scalability, and modular design make it a viable and efficient alternative to traditional scarecrow methods. While the project demonstrated overall efficacy, challenges such as power consumption and occasional connectivity issues in remote farm locations were identified, emphasizing the need for ongoing refinement to optimize long-term performance and reliability. In conclusion, the Motion-Activated Picture Capture, Animal Detection, and Alert System presents a promising solution for farmers seeking a modern, intelligent scarecrow to safeguard their crops from wildlife intrusion. Conclusion

The Piezoelectric Energy Harvesting Platform proves efficient in transforming mechanical vibrations into electrical energy. The series-parallel sensor arrangement ensures reliability, complemented by a full-wave rectifier and voltage boosters for enhanced output. Strategic material choices and spring tuning contribute to durability and responsiveness. Ongoing research should explore advanced materials and refined spring designs to elevate system performance. This platform presents a viable solution for sustainable energy applications, illustrating significant potential for practical implementation.

V. FUTURE SCOPE

The Motion-Activated Picture Capture, Animal Detection, and Alert System, devised as an intelligent scarecrow for farms, holds substantial promise for future advancements. The integration of advanced sensors, such as infrared or acoustic sensors, could refine the system's wildlife detection capabilities, providing a more nuanced understanding of the farm environment. Adding Internet of Things (IoT) connectivity and cloud-based data analytics could offer real- time insights into wildlife patterns and trends, enabling farmers to make more informed decisions. Additionally, the integration of autonomous drones equipped with cameras could provide aerial surveillance, extending the monitoring range and diversifying the perspectives captured. Addressing power consumption challenges through energy-efficient solutions, like solar panels or advanced batteries, would contribute to the sustainability of the system, especially in remote farm locations. Continuous refinement of machine learning algorithms and collaboration with other smart farming technologies could further optimize the system's accuracy and broaden its applications within the broader context of farm management. Encouraging global adoption, standardization, and collaboration with research institutions and local communities could create a shared knowledge base, fostering collective efforts towards sustainable wildlife management in agricultural landscapes. In essence, the system not only represents a modern solution for wildlife deterrence but also holds the potential to become an integrated platform contributing to advanced farm management practices and sustainable agriculture.

Conclusion

The Motion-Activated Picture Capture, Animal Detection, and Alert System designed as a cutting-edge scarecrow for farms represents a significant advancement in wildlife deterrence and crop protection. The successful integration of motion sensors and the ESP32 CAM module has demonstrated its effectiveness in promptly detecting and capturing images of wildlife within farm perimeters. The high-resolution image quality enables farmers to make informed decisions regarding potential threats to their crops. The real-time alert mechanism empowers farmers to take immediate action, minimizing damage and optimizing crop protection efforts. The system\'s adaptability to diverse environmental conditions, along with the integration of machine learning algorithms to minimize false alarms, adds a layer of intelligence to traditional scarecrow methods. Furthermore, the system\'s cost-effectiveness, scalability, and modular design offer a practical and efficient alternative for farmers seeking to enhance their crop protection measures. As with any innovative technology, identified challenges, such as power consumption and connectivity issues, underline the importance of ongoing refinement for optimal long-term performance. Overall, this system presents a promising and intelligent solution for modern agriculture, marrying technology with traditional farming practices to address the ever-present challenge of wildlife intrusion and crop damage.

References

[1] G. K. Verma and P. Gupta, \"Wild Animal Detection Using Deep Convolutional Neural Network,\" in *Advances in Intelligent Systems and Computing (AISC)*, vol. 704, 2018. [2] J. Beringer, K. C. VerCauteren, and J. J. Millspaugh, \"Evaluation of an animal-activated scarecrow and a monofilament fence for reducing deer use of soybean fields,\" in *USDA Wildlife Services - Staff Publications*, 2003, pp. 283. [3] D. Ranparia, G. Singh, Rattan, Dr. Harpreet Singh, and Dr. Nitin Auluck, \"Machine Learning-Based Acoustic Repellent System for Protecting Crops against Wild Animal Attacks,\" in *15th IEEE International Conference on Inventive Computation Technologies (ICICT)*, 202 [4] S. Giordano, I. Seitanidis, M. Ojo, D. Adami, and F. Vignoli, \"IoT Solutions for Crop Protection against Wild Animal Attacks,\" in *2018 IEEE International Conference on Environmental Engineering (EE)*, 2018. [5] M. B. H., D. A. Janeera, and A. K. A. G., \"Internet of Things based Wild Animal Infringement Identification, Diversion and Alert System,\" in *International Conference on Inventive Computation Technologies (ICICT)*, 202 [6] H. Patil and N. F. Ansari, \"Intrusion Detection and Repellent System for Wild Animals Using Artificial Intelligence of Things,\" in *International Conference on Computing, Communication and Power Technology (IC3P)*, 2022. [7] R. Mapari, K. Bhangale, L. Deshmukh, P. Gode, and A. Gaikwad, \"Agriculture Protection from Animals Using Smart Scarecrow System,\" in *Advances in Intelligent Systems and Computing*, vol. 1397, Springer. [8] D. Adami, M. O. Ojo, and S. Giordano, \"Design, Development and Evaluation of Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI,\" in *IEEE Access*, vol. 9, 2021. [9] S. K. Govind and E. A. Jayson, \"Crop Damage by Wild Animals in Thrissur District, Kerala, India,\" in *Indian Hotspots*, Springer, 2018. [10] R. Mapari, K. Bhangale, L. Deshmukh, P. Gode, and A. Gaikwad, \"Agriculture Protection from Animals Using Smart Scarecrow System,\" in *Advances in Intelligent Systems and Computing*, vol. 1397, Springer, 2021.

Copyright

Copyright © 2024 Prof. Siddharth Borghe, Kartik Khope, Anushka khandelwal, Vedant Keche. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.