An internet of things based smart agriculture monitoring system using convolution neural network algorithm


  • Balamurugan K S Karpaga Vinayaga College of Engineering and Technology
  • Chinmaya Kumar Pradhan Vignan's Lara Institute of Technology and Science
  • Venkateswarlu A N Vignan's Lara Institute of Technology and Science
  • Harini G Karpaga Vinayaga College of Engineering and Technology
  • Geetha P Karpaga Vinayaga College of Engineering and Technology



Convolution Neural Netowrk algorithm, Repeller alarms, Android application, 1800 Rotational cameras


Farming is a crucial vocation for survival on this planet because it meets the majority of people's necessities to live. However, as technology developed and the Internet of Things was created, automation (smarter technologies) began to replace old approaches, leading to a broad improvement in all fields. Currently in an automated condition where newer, smarter technologies are being upgraded daily throughout a wide range of industries, including smart homes, waste management, automobiles, industries, farming, health, grids, and more. Farmers go through significant losses as a result of the regular crop destruction caused by local animals like buffaloes, cows, goats, elephants, and others. To protect their fields, farmers have been using animal traps or electric fences. Both animals and humans perish as a result of these countless deaths. Many individuals are giving up farming because of the serious harm that animals inflict on crops. The systems now in use make it challenging to identify the animal species. Consequently, animal detection is made simple and effective by employing the Artificial Intelligence based Convolution Neural Network method. The concept of playing animal-specific sounds is by far the most accurate execution. Rotating cameras are put to good use. The percentage of animals detected by this technique has grown from 55% to 79%.


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Stefano, G, Davide, A, Fabio, V. IoT Solutions for Crop Protection against Wild Animal Attacks: Int. J. Elec. Comp. in Agri. 2022; 61: 618-625.

Mathew, T, Sabu, A, Sengan, S. Microclimate monitoring system for irrigation water optimization using IoT: Measurement: Sensors. 2023; 27:. 1-12. DOI:

Balakrishna, K, Fazil, M, Ullas, C, Hema, C, Sonakshi, S. Application of IoT and machine learning in crop protection against animal intrusion: Int. J. of Sens.. 2016; 15: 72-82.

Hardik, D, Patil, Namrata, F. Automated Wild-Animal Intrusion Detection and Repellent System Using Artificial Intelligence of Thing: IEEE IoT J. 2018; 5: 4428–4440.

Mathanraj, S, Akshay, K, Muthukrishnan, R, Hrishikesh, A, Mohitkar, Shubham, S, Gorwade, S. Animal Repellent System for Smart Farming Using AI and Deep Learning: Int. J. Eng. Advan. Tech. 2019; 7: 2249–8958.

Margret, S, Amal, K, Angelin, V, Sarayu, E. Animal Repellent System for Smart Farming using AI and Edge Computing. Int. J. Eng. Tech. 2018; 7: 125–129.

Lekhaa, T, Sumathi, P, saravanarajan, B, Surya, N, Sakthi murugan, A, Dhineshkumar, S. AI and IoT Based Animal Recognition and Repelling System For Smart Farming: Int. J. Sensors. 2017; 15: 72-82.

Abirami, R, MohanaSelvi, S, Narmadha, S, Anitha, S. Animal Repellent System for Smart Farming Using Artificial Intelligence and Deep Learning: IEEE Trans. Ind. Elec. 2015; 61: 6812-6821.

Adelantado, F, Vilajosana, X, Tuset-Peiro, P, Martinez, B, Melia-Segui, J, Watteyne, T. Understanding the limits of LoRaWAN: IEEE Communications magazine. 2017; 55: 34–40. DOI:

Jayalakshmi, S, Aswini, V, Jaya, V, Dayana, R. Smart Farming Application for Protecting Animal Intrusion Using AI: IEEE Comm. magazine. 2021; 55: 34–40.

Prajna. P, Soujanya, B, Divya, S. IoT-based Wild Animal Intrusion Detection System: Int. J. Comp. elec. Agri. 2019; 156: 467-474.

Srikanth, N, Aishwarya, Kavita, H, Rashmi Reddy, K, Soumya, B. Smart Crop Protection System from Animals and Fire using Arduino: IEEE IoT. J. 2018; 5: 4428–4440.

Venkateswara Rao, P, Siva, R, Samba, R. A Smart Crop Protection against Animals Attack: Int. J. Eng. Tech. 2020; 7: 125–129.

Hema, N. Solar-powered Smart Ultrasonic Rodent Repellent with DTMF and Manual Control for Poultry Farms: Int. J. Sensors. 2020; 7: 125–129.

Muangprathub, J, Boonnam, N, Kajornkasirat, S, Lekbangpong, S, Wanichsombat, A, Nillaor, P. IoT and agriculture data analysis for smart farm: Int. J. Comp. elec. Agri. 2019; 156: 467-474. DOI:

Prasanth, A, Sabeena, G, Sowndarya, Pushpalatha, N. An artificial intelligence approach for energy-aware intrusion detection and secure routing in the Internet of things-enabled wireless sensor networks: Concurrency and Computation: Practice and Experience. 2023; 131: 1-21.

Paruthi Ilam Vazhuthi, P, Prasanth, A, Manikandan, S, P, Devi Sowndarya, K, K. A hybrid ANFIS reptile optimization algorithm for energy-efficient inter-cluster routing in Internet of Things-enabled Wireless Sensor Networks: Peer- to-Peer Net. Appli. 2023; 16: 1049-1068. DOI:

Shantha R, Mahender K, Jenifer A: Security analysis of hybrid one time password generation algorithm for IoT data. AIP Conference Proceedings. 2022; 2418:1-10. DOI:

Kavitha, M, Roobini, S, Systematic View and Impact of Artificial Intelligence in Smart Healthcare Systems, Principles, Challenges and Applications, Machine Learning and Artificial Intelligence in Healthcare Systems. 2023; 25-56. DOI:




How to Cite

B. K S, C. K. Pradhan, V. A N, H. G, and G. P, “An internet of things based smart agriculture monitoring system using convolution neural network algorithm”, EAI Endorsed Trans IoT, vol. 10, Feb. 2024.