{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:11:39Z","timestamp":1763543499426,"version":"3.45.0"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,16]],"date-time":"2025-11-16T00:00:00Z","timestamp":1763251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Globally, potatoes are one of the major crops that significantly contribute to food security; hence, the field of machine learning has opened the gate for many advances in plant disease detection. For real-time agricultural applications, it has been found that real-time data processing is challenging; this is due to the limitations and constraints imposed by hardware platforms. However, such challenges can be handled by deploying simple and optimized AI models serving the need of accurate data classification while taking into consideration hardware resource limitations. Hence, the purpose of this study is to implement a customized and optimized convolutional neural network model for deployment on hardware platforms to classify both potato early blight and potato late blight diseases. Lastly, a thorough comparison between both embedded and PC simulation implementations was conducted for the three models: the implemented CNN model, VGG16, and ResNet50. Raspberry Pi3 was chosen for the embedded implementation in the intermediate stage and NVIDIA Jetson Nano was chosen for the final stage. The suggested model significantly outperformed both the VGG16 and ResNet50 CNNs, as evidenced by the inference time, number of FLOPs, and CPU data usage, with an accuracy of 95% on predicting unseen data.<\/jats:p>","DOI":"10.3390\/computers14110498","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T08:50:07Z","timestamp":1763542207000},"page":"498","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Embedded Convolutional Neural Network Model for Potato Plant Disease Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Laila","family":"Hammam","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, The British University in Egypt, Cairo 11837, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3199-5245","authenticated-orcid":false,"given":"Hany Ayad","family":"Bastawrous","sequence":"additional","affiliation":[{"name":"Department of Microelectronics, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology (TU Delft), 2628 CD Delft, The Netherlands"}]},{"given":"Hani","family":"Ghali","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Faculty of Engineering, The British University in Egypt, Cairo 11837, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6519-9511","authenticated-orcid":false,"given":"Gamal A.","family":"Ebrahim","sequence":"additional","affiliation":[{"name":"Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gado, T.A., and El-Agha, D.E. (2021). Climate Change Impacts on Water Balance in Egypt and Opportunities for Adaptations. Agro-Environmental Sustainability in MENA Regions, Springer.","DOI":"10.1007\/978-3-030-78574-1_2"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lago-Olveira, S., El-Areed, S.R., Moreira, M.T., and Gonz\u00e1lez-Garc\u00eda, S. (2023). Improving Environmental Sustainability of Agriculture in Egypt Through a Life-Cycle Perspective, Elsevier.","DOI":"10.1016\/j.scitotenv.2023.164335"},{"key":"ref_3","first-page":"241","article-title":"Challenges and Constraints Facing the Agricultural Extension System in Egypt","volume":"17","author":"Mansour","year":"2022","journal-title":"J. Agric. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1007\/s12571-012-0200-5","article-title":"Crop Losses Due to Diseases and Their Implications for Global Food Production Losses and Food Security","volume":"4","author":"Savary","year":"2012","journal-title":"Food Secur."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.4236\/ajps.2023.1411086","article-title":"Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review","volume":"14","author":"John","year":"2023","journal-title":"Am. J. Plant Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"59174","DOI":"10.1109\/ACCESS.2023.3284760","article-title":"A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends","volume":"11","author":"Shafik","year":"2023","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tugrul, B., Elfatimi, E., and Eryigit, R. (2022). Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review. Agriculture, 12.","DOI":"10.3390\/agriculture12081192"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"141748","DOI":"10.1109\/ACCESS.2020.3013005","article-title":"Overview of Edge Computing in the Agricultural Internet of Things: Key Technologies, Applications, Challenges","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Garcia-Perez, A., Mi\u00f1\u00f3n, R., Torre-Bastida, A.I., and Zulueta-Guerrero, E. (2023). Analysing Edge Computing Devices for the Deployment of Embedded AI. Sensors, 23.","DOI":"10.3390\/s23239495"},{"key":"ref_10","first-page":"2","article-title":"A comparative study of big data use in Egyptian agriculture","volume":"10","author":"Sayed","year":"2023","journal-title":"J. Electr. Syst. Inf. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mohanty, S.P., Hughes, D.P., and Salath\u00e9, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci., 7.","DOI":"10.3389\/fpls.2016.01419"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep Learning Models for Plant Disease Detection and Diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106184","DOI":"10.1016\/j.compag.2021.106184","article-title":"Recognition of Rice Leaf Diseases and Wheat Leaf Diseases Based on Multi-Task Deep Transfer Learning","volume":"186","author":"Jiang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","article-title":"Deep Learning for Tomato Diseases: Classification and Symptoms Visualization","volume":"31","author":"Brahimi","year":"2017","journal-title":"Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"042621","DOI":"10.1117\/1.JRS.11.042621","article-title":"Deep Convolutional Neural Network for Classifying Fusarium Wilt of Radish from Unmanned Aerial Vehicles","volume":"11","author":"Ha","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_17","first-page":"718","article-title":"Plant Disease Detection using AI based VGG-16 Model","volume":"13","author":"Alatawi","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.biosystemseng.2019.12.003","article-title":"Computer Vision Based Detection of External Defects on Tomatoes Using Deep Learning","volume":"190","author":"Costa","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100313","DOI":"10.1016\/j.array.2023.100313","article-title":"A Real-Time Application-Based Convolutional Neural Network Approach for Tomato Leaf Disease Classification","volume":"19","author":"Paul","year":"2023","journal-title":"Array"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106042","DOI":"10.1016\/j.compag.2021.106042","article-title":"A Deep Learning Approach for RGB Image-Based Powdery Mildew Disease Detection on Strawberry Leaves","volume":"183","author":"Shin","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105951","DOI":"10.1016\/j.compag.2020.105951","article-title":"Disease Detection in Tomato Leaves via CNN with Lightweight Architectures Implemented in Raspberry Pi 4","volume":"181","author":"Rodriguez","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","first-page":"149","article-title":"Usage of Nvidia Jetson Nano in Agriculture as an Example of Plant Leaves Illness Real-Time Detection and Classification","volume":"2","author":"Galstyan","year":"2022","journal-title":"Agron. Agroecol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Routis, G., Michailidis, M., and Roussaki, I. (2024). Plant Disease Identification Using Machine Learning Algorithms on Single-Board Computers in IoT Environments. Electronics, 13.","DOI":"10.3390\/electronics13061010"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dobnik, D., Gruden, K., Ram\u0161ak, \u017d., and Coll, A. (2021). Importance of Potato as a Crop and Practical Approaches to Potato Breeding. Solanum tuberosum, Springer.","DOI":"10.1007\/978-1-0716-1609-3"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chakrabart, S.K., Sharma, S., and Shah, M.A. (2022). Potato Pests and Diseases: A Global Perspective. Sustainable Management of Potato Pests and Diseases, Springer.","DOI":"10.1007\/978-981-16-7695-6"},{"key":"ref_26","first-page":"42","article-title":"The Current Situation of Egyptian Potatoes Exports in the shadow of Covid 19 Pandemic","volume":"18","author":"Mostafa","year":"2022","journal-title":"J. Am. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kaur, S., and Mukerji, K.G. (2004). Potato Diseases and their Management. Fruit and Vegetable Diseases, Kluwer Academic Publishers.","DOI":"10.1007\/0-306-48575-3"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.aac.2022.10.001","article-title":"Understanding the Potential Applications of Artificial Intelligence in Agriculture Sector","volume":"2","author":"Javaid","year":"2023","journal-title":"Adv. Agrochem"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lal, M., Chaudhary, S., Sharma, S., Subhash, S., and Kumar, M. (2022). Bio-Intensive Management of Fungal Diseases of Potatoes. Sustainable Management of Potato Pests and Diseases, Springer.","DOI":"10.1007\/978-981-16-7695-6_19"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"77","DOI":"10.26480\/trab.02.2021.77.81","article-title":"Integrated Disease Management of Early Blight (Alternaria solani) of Potato","volume":"2","author":"Chaudhary","year":"2021","journal-title":"Trop. Agrobiodiversity (TRAB)"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1094\/PDIS.1997.81.12.1349","article-title":"Re-emergence of Potato and Tomato Late Blight in the United States","volume":"81","author":"Fry","year":"1997","journal-title":"Plant Dis."},{"key":"ref_32","first-page":"16","article-title":"Late Blight Disease of Potato and its Management","volume":"41","author":"Arora","year":"2014","journal-title":"Plant J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kassaw, A., Abera, M., and Eshetu, B. (2021). The Response of Potato Late Blight to Potato varieties and Fungicide Spraying Frequencies at Meket, Ethiopia. Cogent Food Agric., 7.","DOI":"10.1080\/23311932.2020.1870309"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, O.A.M., L\u00f3pez, A.M., and Crossa, J. (2022). Convolutional Neural Networks. Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer.","DOI":"10.1007\/978-3-030-89010-0_13"},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations, ICLR."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jia, X., Jiang, X., Li, Z., Mu, J., Wang, Y., and Niu, Y. (2023). Application of Deep Learning in Image Recognition of Citrus Pests. Agriculture, 13.","DOI":"10.3390\/agriculture13051023"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, L., Xu, S., and Yu, X. (2023). A New Model Based on Improved VGG16 For Corn Weed Identification. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1205151"},{"key":"ref_38","first-page":"143","article-title":"Transfer Learning Using VGG-16 with Deep Convolutional Neural Network for Classifying Images","volume":"9","author":"Tammina","year":"2019","journal-title":"Int. J. Sci. Res. Publ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pranesh, M., Atchaya, A.J., Anitha, J., and Hemanth, D.J. (2023). Scene Classification in Enhanced Remote Sensing Images Using Pre-trained RESNET50 Architecture. Electronic Governance with Emerging Technologies, Springer.","DOI":"10.1007\/978-3-031-43940-7_7"},{"key":"ref_41","unstructured":"Bhattarai, S. (2025, July 01). New Plant Diseases Dataset, Kaggle, 18 November 2018. Available online: https:\/\/www.kaggle.com\/datasets\/vipoooool\/new-plant-diseases-dataset."},{"key":"ref_42","first-page":"959","article-title":"Comparative Analysis of Optimizers in Deep Neural Networks","volume":"5","author":"Desai","year":"2020","journal-title":"Int. J. Innov. Sci. Res. Technol."},{"key":"ref_43","unstructured":"Zhou, P., Feng, J., and Ma, C. (2020, January 6\u201312). Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning. Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, BC, Canada."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, P., He, X., Song, D., Ding, Z., Qiao, M., Cheng, X., and Li, R. (2021). Improved Categorical Cross-Entropy Loss for Training Deep Neural Networks with Noisy Labels. Pattern Recognition and Computer Vision, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-88013-2_7"},{"key":"ref_46","unstructured":"L\u00f3pez, O.A.M., L\u00f3pez, A.M., and Crossa, J. (2022). Fundamentals of Artificial Neural Networks and Deep Learning. Multivariate Statistical Machine Learning Methods for Genomic Prediction, Springer."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.icte.2020.04.010","article-title":"The Effect of Batch Size on The Generalizability of the Convolutional Neural Networks on a Histopathology Dataset","volume":"6","author":"Kandel","year":"2020","journal-title":"ICT Express"},{"key":"ref_48","unstructured":"(2025, July 01). Plant Disease Detection Using VGG16, Kaggle, 20 May 2020. Available online: https:\/\/www.kaggle.com\/code\/amitkrjha\/plant-disease-detection-using-vgg16\/notebook."},{"key":"ref_49","unstructured":"(2025, July 01). Cotton Plant Disease Detection Using Resnet50, Kaggle, 25 November 2020. Available online: https:\/\/www.kaggle.com\/code\/sayooj98\/cotton-plant-disease-detection-using-resnet50."},{"key":"ref_50","unstructured":"(2025, July 03). Raspberry Pi. Available online: https:\/\/www.raspberrypi.com\/products\/raspberry-pi-3-model-b\/."},{"key":"ref_51","unstructured":"NVIDIA Jetson Nano (2025, July 03). NVIDIA Corporation. Available online: https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-nano\/product-development\/."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/11\/498\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T09:07:50Z","timestamp":1763543270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/11\/498"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,16]]},"references-count":51,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["computers14110498"],"URL":"https:\/\/doi.org\/10.3390\/computers14110498","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,16]]}}}