Edge AI-Powered System Architecture for Aloe Vera Plant Disease Detection
| International Journal of Electronics and Communication Engineering |
| © 2025 by SSRG - IJECE Journal |
| Volume 12 Issue 5 |
| Year of Publication : 2025 |
| Authors : Sakshi koli, Dev Baloni, Sunil Shukla, Anita Gehlot, Rajesh Singh, Lalit Mohan Joshi, Sachin Kumar |
10.14445/23488549/IJECE-V12I5P128
How to Cite?
Sakshi koli, Dev Baloni, Sunil Shukla, Anita Gehlot, Rajesh Singh, Lalit Mohan Joshi, Sachin Kumar, "Edge AI-Powered System Architecture for Aloe Vera Plant Disease Detection," SSRG International Journal of Electronics and Communication Engineering, vol. 12, no. 5, pp. 327-349, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I5P128
Abstract:
Aloe Vera has been widely cultivated for medicinal and cosmetic purposes, but its productivity is affected by different leaf diseases. They need to be detected early and accurately to avoid crop loss and to keep plants healthy. Conventional disease identification techniques are based on manual inspection, which is both time-consuming and predisposed to errors. Therefore, to cope with this delinquency, this study presents an edge AI-based system architecture for real-time aloe vera plant disease detection, which leads to more efficient and accurate detection. An Aloe Vera disease dataset was employed to train a Convolutional Neural Network (CNN), which was then deployed on an edge device to perform real-time inference. Environmental monitoring using IoT sensors is also part of the architecture. Experimental results specify that the proposed system can perceive diseases with high accuracy while considerably reducing latency compared to cloud-based methods. The proposed Aloe Vera leaf disease classification model, based on ResNet50, achieved 99.15% accuracy, 99.20% precision, 99.21% recall, and a 99.20% F1 score, ensuring high classification performance. The deployment of the quantized TFLite model on Raspberry Pi 4 B enables real-time disease detection with an inference latency of 4,922 ms (~4.9s) and a reduced model size of 23.4MB (INT8), making it suitable for edge computing applications in precision agriculture. Fine-grained deep learning with Edge AI empowers Real-Time Decision Making in Resource-Constrained Environments. This study provides a solution for Aloe Vera disease detection, characterized by low latency, energy efficiency, and scalability, emphasizing a tool for smart agriculture applications.
Keywords:
Edge AI, Aloe vera disease detection, Deep learning, Smart agriculture, IoT-based monitoring.
References:
[1] Giuseppe Cristiano, Bernardo Murillo-Amador, and Barbara De Lucia, “Propagation Techniques and Agronomic Requirements for the Cultivation of Barbados Aloe (Aloe vera (L.) Burm. F.)-A Review,” Frontiers Research Foundation, vol. 7, pp. 1-14, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Nadia Mohamed Said Arafa, Huda Mohammad Ahmad Hummadi, and Gehan Moustafa Badr, “The Potential of Aloe Vera Gel Utilization for Skin Wound Healing in Rats based on GC–MS and HPLC Chemical Profile,” The Journal of Basic and Applied Zoology, vol. 86, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[3] A. Catalano et al., “Aloe Vera-An Extensive Review Focused on Recent Studies,” Foods, vol. 13, no. 13, pp. 1-55, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Aloe Vera Extract Market Size Statement, 2019-2025, Grand View Research, pp. 1-90, 2025. [Online]. Available: https://www.grandviewresearch.com/industry-analysis/aloe-vera-extracts-market
[5] Aloe Vera Market Size and Share Outlook - Forecast Trends and Growth Analysis Report (2025-2034), Expert Market Research, 2025. [Online]. https://www.expertmarketresearch.com/reports/aloe-vera-market
[6] Olwen M. Grace et al., “Evolutionary History and Leaf Succulence as Explanations for Medicinal Use in Aloes and the Global Popularity of Aloe Vera,” BMC Evolutionary Biology, vol. 15, pp. 1-12, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xihai Zhang, Zhanyuan Cao, and Wenbin Dong, “Overview of Edge Computing in the Agricultural Internet of Things: Key Technologies, Applications, Challenges,” IEEE Access, vol. 8, pp. 141748-141761, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Prem Rajak et al., “Internet of Things and Smart Sensors in Agriculture: Scopes and Challenges,” Journal of Agriculture and Food Research, vol. 14, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Sebastian Sadowski, and Petros Spachos, “Wireless Technologies for Smart Agricultural Monitoring using Internet of Things Devices with Energy Harvesting Capabilities,” Computers and Electronics in Agriculture, vol. 172, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Fazeel Ahmed Khan, Adamu Abubakar Ibrahim, and Akram M. Zeki, “Environmental Monitoring and Disease Detection of Plants in Smart Greenhouse using Internet of Things,” Journal of Physics Communications, vol. 4, no. 5, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Rubina Rashid et al., “An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models,” IEEE Access, vol. 12, pp. 23149-23162, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Zhiyan Liu et al., “Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction-Blister Blight for Tea Plant,” IEEE Access, vol. 10, pp. 44934-44944, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Monalisa Mishra, Prasenjit Choudhury, and Bibudhendu Pati, “Modified Ride-NN Optimizer for the IoT based Plant Disease Detection,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 691-703, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Geetam Tomar, “Welcome from CSNT 2021 General Chair Proceedings,” 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India, pp. 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Munira Akter Lata et al., “Development of an IoT based Smart Potato Leaf Diseases Monitoring and Controlling System with Image Processing,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 22, no. 6, pp. 1502-1510, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Zinon Zinonos et al., “Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology,” IEEE Access, vol. 10, pp. 122-133, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Juan Contreras-Castillo et al., “SAgric-IoT: An IoT-Based Platform and Deep Learning for Greenhouse Monitoring,” Applied Sciences, vol. 13, no. 3, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] S. Aasha Nandhini et al., “Web Enabled Plant Disease Detection System for Agricultural Applications Using WMSN,” Wireless Personal Communications, vol. 102, pp. 725-740, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Sehan Kim et al., “IoT-Based Strawberry Disease Prediction System for Smart Farming,” Sensors, vol. 18, no. 11 pp. 1-17, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ninhi Kundu et al., “IoT and Interpretable Machine Learning based Framework for Disease Prediction in Pearl Millet,” Sensors, vol. 21, no. 16, pp. 1-23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] S.V.S. Ramakrishnam Raju et al., “Design and Implementation of Smart Hydroponics Farming Using IoT-Based AI Controller with Mobile Application System,” Journal of Nanomaterials, vol. 2022, no. 1, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Aminu Musa et al., “Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques,” Journal of Low Power Electronics and Applications, vol. 12, no. 2, pp. 1-20, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Sushruta Mishra et al., “A Smart and Sustainable Framework for Millet Crop Monitoring Equipped with Disease Detection using Enhanced Predictive Intelligence,” Alexandria Engineering Journal, vol. 83, pp. 298-306, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Nermeen Gamal Rezk et al., “An Efficient Plant Disease Recognition System Using Hybrid Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) for Smart IoT Applications in Agriculture,” International Journal of Computational Intelligence Systems, vol. 15, pp. 1-21, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ritesh Maurya, Satyajit Mahapatra, and Lucky Rajput, “A Lightweight Meta-Ensemble Approach for Plant Disease Detection Suitable for IoT-Based Environments,” IEEE Access, vol. 12, pp. 28096-28108, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Hiren K. Mewada, and Jignesh J. Patoliya, “IoT based Automated Plant Disease Classification using Support Vector Machine,” International Journal of Electronics and Telecommunications, vol. 67, no. 3, pp. 517-522, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Oumayma Jouini et al., “Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement,” Agri Engineering, vol. 6, no. 3, pp. 2001-2022, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Hoang Trong Minh, Tuan Pham Anh, and Van Nguyen Nhan, “A Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devices,” MENDEL-Soft Computing Journal, vol. 28, no. 2, pp. 2571-3701, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Asad Ul Haq Hashmi et al., “Effects of IoT Communication Protocols for Precision Agriculture in Outdoor Environments,” IEEE Access, vol. 12, pp. 46410-46421, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Stephan Patrick Baller et al., “DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices,” 2021 IEEE International Conference on Cloud Engineering (IC2E), San Francisco, CA, USA, pp. 20-30, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Lameya Aldhaheri et al., “LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions,” IEEE Internet of Things Journal, vol. 12, no. 12, pp. 1380-1407, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Carlos Victorino Padeiro et al., “Lightweight Maize Disease Detection through Post-Training Quantization with Similarity Preservation,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, pp. 2111-2120, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Pierre-Emmanuel Novac, “Quantization and Deployment of Deep Neural Networks on Microcontrollers,” Sensors, vol. 21, no. 9, pp. 1-32, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[34] Connor Shorten, and Taghi M. Khoshgoftaar, “A Survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, pp. 1-48, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[35] S. Jayashree, and V. Sumalatha, “Plant Leaf Disease Detection Using Resnet-50 Based on Deep Learning,” Proceedings of the International Conference on Digital Transformation in Business: Navigating the New Frontiers Beyond Boundaries, pp. 150-166, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Jin Liang, and Wenping Jiang, “A ResNet50-DPA Model for Tomato Leaf Disease Identification,” Frontiers in Plant Science, vol. 14, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[37] Mattia Segu, Alessio Tonioni, and Federico Tombari, “Batch Normalization Embeddings for Deep Domain Generalization,” Pattern Recognition, vol. 135, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[38] Imrus Salehin, and Dae-Ki Kang, “A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain,” Electronics, vol. 12, no. 14, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[39] Oumayma Jouini et al., “A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions,” Technologies, vol. 12, no. 6, pp. 1-34, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[40] Konkala Venkateswarlu Reddy et al., “Edge AI in Sustainable Farming: Deep Learning-Driven IoT Framework to Safeguard Crops from Wildlife Threats,” IEEE Access, vol. 12, pp. 77707-77723, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[41] Dušan Markovic et al., “Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing,” Sensors, vol. 24, no. 18, pp. 1-26, 2024.
[CrossRef] [Google Scholar] [Publisher Link]