AgriGuard: IoT-Powered Real-Time Object Detection and Alert System for Intelligent Surveillance

doi:10.23940/ijpe.24.04.p5.232241

Abstract

Abstract: Within the realm of sustainable agriculture and the protection of crops, the presence of intruders poses a substantial risk, leading to potential crop damages and injuries. In our paper, we conceptualize intruders as objects. Consequently, the implementation of object detection emerges as a crucial safety and field protection measure, effectively mitigating the adverse consequences of such encounters. While deep learning techniques have proven to yield superior results in object detection, their computational and parameter requirements have posed challenges in their widespread implementation. This research paper presents an alert system AgriGuard equipped with a lightweight object detection model for enhancing security in agriculture fields. The proposed embedded system utilizes EmbdYOLOv3 and TinyEmbdYOLOv3 models, modified versions of YOLOv3 and TinyYOLOv3, respectively, to overcome challenges in object detection, such as occluded or look-alike animals. The system integrates ultrasonic sensors, raspberry pi, real-time object detectors, google firebase, and an android application to detect and alert farmers for unauthorized access. Experimental results show that EmbdYOLOv3 outperforms YOLOv3 by 36.85%, and TinyEmbdYOLOv3 outperforms Tiny-YOLOv3 by 14.48% under the same embedded environment. The study highlights the effectiveness of low-power IoT devices and deep learning techniques in providing robust security solutions for the agriculture industry, offering a potential approach for mitigating crop damages and ensuring field safety.

Key words: deep learning, internet of things (IoT), object detection, embedded system, raspberry pi

Priya Singh and Rajalakshmi Krishnamurthi. AgriGuard: IoT-Powered Real-Time Object Detection and Alert System for Intelligent Surveillance [J]. Int J Performability Eng, 2024, 20(4): 232-241.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] Elijah O., Rahman T.A., Orikumhi I., Leow C.Y., and Hindia M.N.An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges. IEEE Internet of things Journal, vol. 5, no. 5, pp. 3758-3773, 2018.
[2] Farooq M.S., Riaz S., Abid A., Abid K., and Naeem M.A.A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming. IEEE Access, 7, pp.156237-156271, 2019.
[3] Liu Y., Ma X., Shu L., Hancke G.P., and Abu-Mahfouz, A.M. from Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4322-4334, 2020.
[4] Kamilaris, A. and Prenafeta-Boldú, F.X. Deep Learning in Agriculture: A Survey. Computers and electronics in agriculture, vol. 147, pp. 70-90, 2018.
[5] Silva-Rodríguez, E.A., Gálvez, N., Swan, G.J., Cusack, J.J., and Moreira-Arce, D. Urban Wildlife in Times of COVID-19: What Can We Infer from Novel Carnivore Records in Urban Areas?. Science of the Total Environment, vol. 765, pp. 142713, 2021.
[6] Roy S.K., Roy A., Misra S., Raghuwanshi N.S., and Obaidat M.S.AID: A Prototype for Agricultural Intrusion Detection using Wireless Sensor Network. In2015 IEEE international conference on communications (icc), IEEE, pp. 7059-7064, 2015.
[7] Singh, P. and Krishnamurthi, R.Object Detection using Deep Ensemble Model for Enhancing Security Towards Sustainable Agriculture.International Journal of Information Technology, pp. 1-14, 2023.
[8] Ibraheam M., Li K.F., and Gebali F.An Accurate and Fast Animal Species Detection System for Embedded Devices. IEEE Access, vol. 11, pp. 23462-23473, 2023.
[9] Adami D., Ojo M.O., and Giordano S. Design,Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI. IEEE Access, vol. 9, pp. 132125-132139, 2021.
[10] Ramanadham, K.L. and Savarimuthu, N. vCrop: An Automated Plant Disease Prediction using Deep Ensemble Framework using Real Field Images. Sādhanā, vol. 47, no. 4, pp. 268, 2022.
[11] Zheng Y.Y., Kong J.L., Jin X.B., Wang X.Y., Su T.L., and Zuo M.CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture. Sensors, vol. 19, no. 5, pp. 1058, 2019.
[12] Hong S.J., Han Y., Kim S.Y., Lee A.Y., and Kim G.Application of Deep-Learning Methods to Bird Detection using Unmanned Aerial Vehicle Imagery. Sensors, vol. 19, no. 7, pp. 1651, 2019.
[13] Partel V., Nunes L., Stansly P., and Ampatzidis Y.Automated Vision-Based System for Monitoring Asian Citrus Psyllid in Orchards Utilizing Artificial Intelligence. Computers and Electronics in Agriculture, vol. 162, pp. 328-336, 2019.
[14] Han L., Tao P., and Martin R.R.Livestock Detection in Aerial Images using a Fully Convolutional Network. Computational Visual Media, vol. 5, pp. 221-228, 2019.
[15] Tzanidakis C., Tzamaloukas O., Simitzis P., and Panagakis P.Precision Livestock Farming Applications (PLF) for Grazing Animals. Agriculture, vol. 13, no. 2, pp. 288, 2023.
[16] Zhao, L. and Li, S.Object Detection Algorithm Based on Improved YOLOv3. Electronics, vol. 9, no. 3, pp. 537, 2020.
[17] Ahmad M., Abbas S., Fatima A., Issa G.F., Ghazal T.M., and Khan M.A.Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach. Applied Sciences, vol. 13, no. 2, pp. 1178, 2023.
[18] Battu, T. and Lakshmi, D.S.R. Animal Image Identification and Classification using Deep Neural Networks Techniques. Measurement: Sensors, vol. 25, pp. 100611, 2023.
[19] Song S., Liu T., Wang H., Hasi B., Yuan C., Gao F., and Shi, H. using Pruning-Based YOLOv3 Deep Learning Algorithm for Accurate Detection of Sheep Face. Animals, vol. 12, no. 11, pp. 1465, 2022.
[20] Jiang B., Wu Q., Yin X., Wu D., Song H., and He D.FLYOLOv3 Deep Learning for Key Parts of Dairy Cow Body Detection. Computers and Electronics in Agriculture, vol. 166, pp. 104982, 2019.
[21] Alqaralleh B.A., Mohanty S.N., Gupta D., Khanna A., Shankar K., and Vaiyapuri T.Reliable Multi-Object Tracking Model using Deep Learning and Energy Efficient Wireless Multimedia Sensor Networks. IEEE Access, vol. 8, pp. 213426-213436, 2020.
[22] Yadahalli S., Parmar A., and Deshpand e A.Smart Intrusion Detection System for Crop Protection by using Arduino. In2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, pp. 405-408, 2020.
[23] Ukwuoma C.C., Qin Z., Yussif S.B., Happy M.N., Nneji G.U., Urama G.C., Ukwuoma C.D., Darkwa N.B., and Agobah H.Animal Species Detection and Classification Framework Based on Modified Multi-Scale Attention Mechanism and Feature Pyramid Network. Scientific African, vol. 16, pp. e01151, 2022.
[24] Bapat V., Kale P., Shinde V., Deshpand e N., and Shaligram A.WSN Application for Crop Protection to Divert Animal Intrusions in the Agricultural Land . Computers and electronics in agriculture, vol. 133, pp. 88-96, 2017.
[25] Feng, J. and Xiao, X.Multiobject Tracking of Wildlife in Videos using Few-Shot Learning. Animals, vol. 12, no. 9, pp. 1223, 2022.
[26] Trnovszky T., Kamencay P., Orjesek R., Benco M., and Sykora P.Animal Recognition System Based on Convolutional Neural Network. Advances in Electrical and Electronic Engineering, vol. 15, no. 3, pp. 517-525, 2017.
[27] Meena, S.D. and Agiland eeswari, L.Smart Animal Detection and Counting Framework for Monitoring Livestock in an Autonomous Unmanned Ground Vehicle using Restricted Supervised Learning and Image Fusion. Neural Processing Letters, vol. 53, pp. 1253-1285, 2021.
[28] Zhang M., Gao F., Yang W., and Zhang H.Wildlife Object Detection Method Applying Segmentation Gradient Flow and Feature Dimensionality Reduction. Electronics, vol. 12, no. 2, pp. 377, 2023.