Application of IOT and machine learning in crop protection against animal intrusion

Balakrishna, K; Mohammed, Fazil P.; Ullas, C.R.; Hema, Chittibomma; Sonakshi, S.K.

doi:10.1016/j.gltp.2021.08.061

Cited by 48 publications

(12 citation statements)

References 8 publications

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“…Moreover, the system's notification mechanisms, whether through applications or Line notification, are consistent with previous research on application and SMS notifications [28], [31]. The integration of IoT with deep learning in this study aligns with earlier research supporting enhancements in notification systems [31], [32], [33], [34], [35]. Notably, a key distinction is observed, as much of the extant research has yet to progress into application development [28], [32].…”

Section: Discussionsupporting

confidence: 87%

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Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

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Section: Discussionsupporting

confidence: 87%

“…Limitations are unspecified [34]. Lastly, the use of IoT and machine learning to detect animals and prevent agricultural land and crop damage is discussed, with a classification accuracy ranging from 85% to 85.89% [35].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

View full text Add to dashboard Cite

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“…[189] utilized ESP8266 NodeMCU for developing an IoT and ML-based optimized smart irrigation system. Similarly, studies by [74,75,82,190,191] employed ESP8266 and [93,97,138,168,192,193] utilized ESP32. Deploying AI/ML models in the fog positions the processing near the edge but with fewer resources compared to the cloud.…”

Section: Computation Componentsmentioning

confidence: 99%

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

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The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

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“…The integration of IoT devices facilitates real-time monitoring, allowing for instant image capture and analysis. In [11], the authors examined a model that combines IoT and machine learning to address the problem of animal intrusion in agriculture. The model employs a Raspberry Pi, along with various hardware components, for surveillance and communication.…”

Section: Related Workmentioning

confidence: 99%

“…In the existing research works [11], [7], [9], [12], a comprehensive approaches is proposed for employing IoT, WSNs, and deep learning models are presented for safe-guarding crops against animal intrusion. These methodologies, exhibit a limitation in affording complete flexibility to farmers in assessing the situation firsthand.…”

Section: Related Workmentioning

confidence: 99%

Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops From Wildlife Threats

Reddy,

Goutham

et al. 2024

IEEE Access

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The relentless global population growth and the ever-increasing food demand pose formidable challenges to the agricultural sector. Farmers grapple with the ongoing challenge of wildlifeinduced crop damage and human loss, which not only impedes food production but also exacerbates supply and demand imbalances. However, the rise of TinyML enables Edge AI as a promising avenue for implementing resource-efficient deep learning techniques on low-end edge devices. In this paper, we introduce an innovative solution that harnesses the power of Edge AI using tinyML-based deep learning algorithms in conjunction with the Internet of Things (IoT) for animal intrusion detection and deterrence system. The proposed system is developed to create remotely managed defense system tailored to safeguard vast agricultural expanses. It integrates a laser detection system and an AI-CAM with light weight deep learning algorithms for animal intrusion detection and classification. This system also ensures efficient animal deterrence and real-time monitoring for farmers, enabling them to assess the situation with the assistance of an intelligent rover build using IoT. This work emphasizes on proposing a light-weight deep learning model named EvoNet for animal classification. Results reveal that the proposed model achieves the highest accuracy at 96.7%, surpassing other models presented in this paper. However, for edge devices where compact file sizes are crucial, the model also offers comparable accuracy with file sizes as low as 1.63MB with the help of pruning and quantization techniques. This conceptualized solution has the potential to revolutionize agricultural wildlife management, ushering in a new era of crop protection and economic resilience.

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Application of IOT and machine learning in crop protection against animal intrusion

Cited by 48 publications

References 8 publications

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops From Wildlife Threats

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