{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:47:51Z","timestamp":1776444471261,"version":"3.51.2"},"reference-count":80,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry for Education and Research in form of Brandenburg\/Bayern Initiative for Integration of Artificial Intelligence\u2014Hardware Subjects in University Curriculum (BB-KI-Chips)","award":["16DHBKIO20"],"award-info":[{"award-number":["16DHBKIO20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%).<\/jats:p>","DOI":"10.3390\/s24185965","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T11:36:37Z","timestamp":1726486597000},"page":"5965","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing"],"prefix":"10.3390","volume":"24","author":[{"given":"Du\u0161an","family":"Markovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Agronomy in \u010ca\u010dak, University of Kragujevac, Cara Du\u0161ana 34, 32102 \u010ca\u010dak, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6078-413X","authenticated-orcid":false,"given":"Zoran","family":"Stamenkovi\u0107","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany"},{"name":"IHP\u2014Leibniz-Institutf\u00fcr innovative Mikroelektronik, ImTechnologiepark 25, 15236 Frankfurt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6145-4490","authenticated-orcid":false,"given":"Borislav","family":"\u0110or\u0111evi\u0107","sequence":"additional","affiliation":[{"name":"Institute Mihailo Pupin, Volgina 15, 11060 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7676-4942","authenticated-orcid":false,"given":"Sini\u0161a","family":"Ran\u0111i\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Technical Sciences \u010ca\u010dak, University of Kragujevac, Svetog Save 65, 32102 \u010ca\u010dak, Serbia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compeleceng.2018.02.047","article-title":"Towards Energy-Aware Fog-Enabled Cloud of Things for Healthcare","volume":"67","author":"Mahmoud","year":"2018","journal-title":"Comput. 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