Novel Edge Device System for Plant Disease Detection with Deep Learning Approach
Keywords:
Internet of Things, Deep Learning, Machine Learning, Robotic system, Edge DeviceAbstract
Plant diseases result in significant economic losses and pose an annual threat to food security in agriculture. The key to minimizing these losses lies in the accurate and prompt identification and diagnosis of plant diseases. Despite the prevalent use of deep neural networks for plant disease identification, challenges such as low accuracy and a high number of parameters persist. There are multiple challenges while using trained deep learning model on edge devices. This paper proposed novel plant disease detection system with MobileNetV2 algorithms and developed novel edge device system with Sipeed Maixduino development board. The system has been trained with 10000 images of 10 different types of tomato plant diseased and healthy leafs. The system has been evaluated and tested with edge device for 10 different types of leaf diseases in tomato plant. The experimental results shows 94% of accurate results during validation of system performance.
Downloads
References
R, K., T, S. Development of plant disease detection for smart agriculture. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17687-7.
Jung, M., Song, J.S., Shin, AY. et al. Construction of deep learning-based disease detection model in plants. Sci Rep 13, 7331 (2023). https://doi.org/10.1038/s41598-023-34549-2
M. A. Rahman, M. M. Islam, G. M. Shahir Mahdee and M. W. Ul Kabir, "Improved Segmentation Approach for Plant Disease Detection," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 2019, pp. 1-5, doi: 10.1109/ICASERT.2019.8934895.
Yan Guo, Jin Zhang, Chengxin Yin, Xiaonan Hu, Yu Zou, Zhipeng Xue, Wei Wang, "Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming", Discrete Dynamics in Nature and Society, vol. 2020, Article ID 2479172, 11 pages, 2020. https://doi.org/10.1155/2020/2479172.
Z. Sarayloo and D. Asemani, "Designing a classifier for automatic detection of fungal diseases in wheat plant: By pattern recognition techniques," 2015 23rd Iranian Conference on Electrical Engineering, Tehran, Iran, 2015, pp. 1193-1197, doi: 10.1109/IranianCEE.2015.7146396.
Mezenner, H. Nemmour, Y. Chibani and A. Hafiane, "Local Directional Patterns for Plant Leaf Disease Detection," 2023 International Conference on Advances in Electronics, Control and Communication Systems (ICAECCS), BLIDA, Algeria, 2023, pp. 1-5, doi: 10.1109/ICAECCS56710.2023.10104754.
M. f. Kabir, F. C. Raisa, S. M. Dipto, A. Shakil and M. A. Alam, "Loss Function Computation using Machine Learning Algorithms based on the effects of Natural Disasters and Plant Diseases on Plant Growth," 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), Gold Coast, Australia, 2022, pp. 1-6, doi: 10.1109/CSDE56538.2022.10089283.
Chen R, Qi H, Liang Y, Yang M. Identification of plant leaf diseases by deep learning based on channel attention and channel pruning. Front Plant Sci. 2022 Nov.
[9] M. S. M. Asaari, S. Shamsudin and L. J. Wen, "Detection of Plant Stress Condition with Deep Learning Based Detection Models," 2023 International Conference on Energy, Power, Environment, Control, and Computing (ICEPECC), Gujrat, Pakistan, 2023, pp. 1-5, doi: 10.1109/ICEPECC57281.2023.10209458.
Dubey, Ankit and M, Shanmugasundaram, Agricultural Plant Disease Detection and Identification (June 27, 2020). International Journal of Electrical Engineering and Technology, 11(3), 2020, pp. 354-363.
J. Wu, U. Dar, M. H. Anisi, V. Abolghasemi, C. N. Wilkin and A. I. Wilkin, "Plant Disease Detection: Electronic System Design Empowered with Artificial Intelligence," 2023 IEEE Conference on AgriFood Electronics (CAFE), Torino, Italy, 2023, pp. 30-34, doi: 10.1109/CAFE58535.2023.10291622.
V. Udutalapally, S. P. Mohanty, V. Pallagani and V. Khandelwal, "sCrop: A Novel Device for Sustainable Automatic Disease Prediction, Crop Selection, and Irrigation in Internet-of-Agro-Things for Smart Agriculture," in IEEE Sensors Journal, vol. 21, no. 16, pp. 17525-17538, 15 Aug.15, 2021, doi: 10.1109/JSEN.2020.3032438.
S. V. Militante, B. D. Gerardo and N. V. Dionisio, "Plant Leaf Detection and Disease Recognition using Deep Learning," 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan, 2019, pp. 579-582, doi: 10.1109/ECICE47484.2019.8942686.
U. Shafi et al., "Embedded AI for Wheat Yellow Rust Infection Type Classification," in IEEE Access, vol. 11, pp. 23726-23738, 2023, doi: 10.1109/ACCESS.2023.3254430.
E. Moupojou et al., "FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning," in IEEE Access, vol. 11, pp. 35398-35410, 2023, doi: 10.1109/ACCESS.2023.3263042.
R. Bompilwar, S. P. Singh Rathor and D. Das, "tCrop: Thermal Imaging Based Plant Stress Identification Using On-Edge Deep Learning," 2022 IEEE Region 10 Symposium (TENSYMP), Mumbai, India, 2022, pp. 1-6, doi: 10.1109/TENSYMP54529.2022.9864547.
K. N. P. Wicaksono and C. Apriono, "Practical Comparison of Plant Pest and Disease Control Technologies Based on Neural Networks, IoT, and AI: A Systematic Review," 2023 International Conference on Converging Technology in Electrical and Information Engineering (ICCTEIE), Bandar Lampung, Indonesia, 2023, pp. 71-73, doi: 10.1109/ICCTEIE60099.2023.10366578.
K. Khuwaja et al., "Sustainable Agriculture: An IoT-Based Solution for Early Disease Detection in Greenhouses," 2023 17th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania, 2023, pp. 1-4, doi: 10.1109/EMES58375.2023.10171676.
H. Rizk and M. K. Habib, "Robotized Early Plant Health Monitoring System," IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, 2018, pp. 3795-3800, doi: 10.1109/IECON.2018.8592833.
L. N. Thalluri et al., "Drone Technology Enabled Leaf Disease Detection and Analysis system for Agriculture Applications," 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2021, pp. 1079-1085, doi: 10.1109/ICOSEC51865.2021.9591837.
Y. Lakhdari, E. Soldevila, J. Rezgui and É. Renault, "Detection of Plant Diseases in an Industrial Greenhouse: Development, Validation & Exploitation," 2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar, 2023, pp. 1-6, doi: 10.1109/ISNCC58260.2023.10323932.
P. Shah, G. Rathod, R. Gajjar, N. Gajjar and M. I. Patel, "Plant Leaf Disease Classification using Convolutional Neural Network on FPGA," 2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT), Dehradun, India, 2023, pp. 307-311, doi: 10.1109/DICCT56244.2023.10110124.
E. Sennik, S. Kinoshita-Millard, Y. Oh, C. W. Kafer, R. A. Dean and Ö. Oralkan, "Plant Disease Detection Using an Electronic Nose," 2023 IEEE SENSORS, Vienna, Austria, 2023, pp. 1-4, doi: 10.1109/SENSORS56945.2023.10325015.
C. S, S. Ghana, S. Singh and P. Poddar, "Deep Learning Model for Image-Based Plant Diseases Detection on Edge Devices," 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, 2021, pp. 1-5, doi: 10.1109/I2CT51068.2021.9418124.
S. M. Shahidur Harun Rumy, M. I. Arefin Hossain, F. Jahan and T. Tanvin, "An IoT based System with Edge Intelligence for Rice Leaf Disease Detection using Machine Learning," 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), Toronto, ON, Canada, 2021, pp. 1-6, doi: 10.1109/IEMTRONICS52119.2021.9422499.
G. K. Pandey and S. Srivastava, "ResNet-18 comparative analysis of various activation functions for image classification," 2023 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, 2023, pp. 595-601, doi: 10.1109/ICICT57646.2023.10134464.
K. Sujatha and B. S. Rao, "Densenet201:A Customized DNN Model for Multi-Class Classification and Detection of Tumors Based on Brain MRI Images," 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023, pp. 1-7, doi: 10.1109/ICECCT56650.2023.10179642.
P. Ahammed, M. F. Faruk, N. Raihan and M. Mondal, "Inception V3 Based Transfer Learning Model for the Prognosis of Acute Lymphoblastic Leukemia from Microscopic Images," 2022 4th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), Rajshahi, Bangladesh, 2022, pp. 1-4, doi: 10.1109/ICECTE57896.2022.10114522.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. -C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 4510-4520, doi: 10.1109/CVPR.2018.00474.
https://maixduino.sipeed.com/en/hardware/module.html
A. Gahane and C. Kotadi, "An Analytical Review of Heart Failure Detection based on IoT and Machine Learning," 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2022, pp. 1308-1314, doi: 10.1109/ICAIS53314.2022.9742913.
H. Andrianto, Suhardi, A. Faizal and F. Armandika, "Smartphone Application for Deep Learning-Based Rice Plant Disease Detection," 2020 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, 2020, pp. 387-392, doi: 10.1109/ICITSI50517.2020.9264942.
] U. Obu, Y. Ambekar, H. Dhote, S. Wadbudhe, S. Khandelwal and S. Dongre, "Crop Disease Detection using Yolo V5 on Raspberry Pi," 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), Salem, India, 2023, pp. 528-533, doi: 10.1109/ICPCSN58827.2023.00092.
U. Dar, M. H. Anisi, V. Abolghasemi, C. Newenham and A. Ivanov, "Visual sensor network based early onset disease detection for strawberry plants," 2023 IEEE Applied Sensing Conference (APSCON), Bengaluru, India, 2023, pp. 1-3, doi: 10.1109/APSCON56343.2023.10101121.
P. Nirale and M. Madankar, "Design of an IoT Based Ensemble Machine Learning Model for Fruit Classification and Quality Detection," 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22), Nagpur, India, 2022, pp. 1-6, doi: 10.1109/ICETET-SIP-2254415.2022.9791718.
M. Belmir, W. Difallah and A. Ghazli, "Plant Leaf Disease Prediction and Classification Using Deep Learning," 2023 International Conference on Decision Aid Sciences and Applications (DASA), Annaba, Algeria, 2023, pp. 536-540, doi: 10.1109/DASA59624.2023.10286672.
V. Bahel and M. Gaikwad, "A Study of Light Intensity of Stars for Exoplanet Detection using Machine Learning," 2022 IEEE Region 10 Symposium (TENSYMP), Mumbai, India, 2022, pp. 1-5, doi: 10.1109/TENSYMP54529.2022.9864366.
H. Xinru, C. Limin, X. Qing, L. Chongyuan, W. Yinchai and F. Peijun, "Plant disease detection based on improved YOLOv5," 2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), Mianyang, China, 2023, pp. 162-165, doi: 10.1109/RAIIC59453.2023.10280962.
H. F. Ng, C. -Y. Lin, J. H. Chuah, H. K. Tan and K. H. Leung, "Plant Disease Detection Mobile Application Development using Deep Learning," 2021 International Conference on Computer & Information Sciences (ICCOINS), Kuching, Malaysia, 2021, pp. 34-38, doi: 10.1109/ICCOINS49721.2021.9497190.
Yerlekar, N. Mungale and S. Wazalwar, "A multinomial technique for detecting fake news using the Naive Bayes Classifier," 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), Nagpur, India, 2021, pp. 1-5, doi: 10.1109/ICCICA52458.2021.9697244.
Q. Wang, G. He, F. Li and H. Zhang, "A novel database for plant diseases and pests classification," 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Macau, China, 2020, pp. 1-5, doi: 10.1109/ICSPCC50002.2020.9259502.
L. Li, S. Zhang and B. Wang, "Plant Disease Detection and Classification by Deep Learning—A Review," in IEEE Access, vol. 9, pp. 56683-56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
P. Kolhe, A. Baseshankar, M. Murekar, S. Sadhankar, K. Kalbande and A. Deshmukh, "Smart Communication System for Agriculture," 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India, 2022, pp. 1122-1126, doi: 10.1109/ICICICT54557.2022.9917715.
S. Lingawar, H. Rathod, M. Katre, A. Somkuwar, N. Sakharkar and K. Jajulwar, "Design of Autonomous Tractor for Agricultural Applications Using Swarm Intelligence," 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India, 2023, pp. 1-4, doi: 10.1109/ICETET-SIP58143.2023.10151551.
Y. Ye, X. Zhu, H. Chen, F. Wang and C. Wang, "Identification of plant diseases based on yolov5," 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, 2023, pp. 1731-1734, doi: 10.1109/ICSP58490.2023.10248628.
B. Vidhale, G. Khekare, C. Dhule, P. Chandankhede, A. Titarmare and M. Tayade, "Multilingual Text & Handwritten Digit Recognition and Conversion of Regional languages into Universal Language Using Neural Networks," 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, 2021, pp. 1-5, doi: 10.1109/I2CT51068.2021.9418106.
J. B. Awotunde et al., "Plant Disease Diagnosis and Detection using Type-2 Fuzzy Logic System," 2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), Omu-Aran, Nigeria, 2023, pp. 1-11, doi: 10.1109/SEB-SDG57117.2023.10124608.
K. M. Hosny, W. M. El-Hady, F. M. Samy, E. Vrochidou and G. A. Papakostas, "Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern," in IEEE Access, vol. 11, pp. 62307-62317, 2023, doi: 10.1109/ACCESS.2023.3286730.
S. B. Derkar, D. Biranje, L. P. Thakare, S. Paraskar and R. Agrawal, "CaptionGenX: Advancements in Deep Learning for Automated Image Captioning," 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, India, 2023, pp. 1-8, doi: 10.1109/ASIANCON58793.2023.10270020.
N. Kosarkar, P. Basuri, P. Karamore, P. Gawali, P. Badole and P. Jumle, "Disease Prediction using Machine Learning," 2022 10th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-22), Nagpur, India, 2022, pp. 1-4, doi: 10.1109/ICETET-SIP-2254415.2022.9791739.
B. Tlhobogang and M. Wannous, "Design of plant disease detection system: A transfer learning approach work in progress," 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, Japan, 2018, pp. 158-161, doi: 10.1109/ICASI.2018.8394556.
J. Liu, G. Zhang, B. Feng, Y. Hou, W. Kang and B. Shen, "A Method for Plant Diseases Detection Based on Transfer Learning and Data Enhancement," 2022 International Conference on High Performance Big Data and Intelligent Systems (HDIS), Tianjin, China, 2022, pp. 154-158, doi: 10.1109/HDIS56859.2022.9991621.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
