{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:36:56Z","timestamp":1760233016677,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Czech Technical University\u2013National Taiwan University of Science and Technology Joint Research Program","award":["CTU-TAIWAN TECH-2022-01"],"award-info":[{"award-number":["CTU-TAIWAN TECH-2022-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The utilization of computer vision in smart farming is becoming a trend in constructing an agricultural automation scheme. Deep learning (DL) is famous for the accurate approach to addressing the tasks in computer vision, such as object detection and image classification. The superiority of the deep learning model on the smart farming application, called Progressive Contextual Excitation Network (PCENet), has also been studied in our recent study to classify cocoa bean images. However, the assessment of the computational time on the PCENet model shows that the original model is only 0.101s or 9.9 FPS on the Jetson Nano as the edge platform. Therefore, this research demonstrates the compression technique to accelerate the PCENet model using pruning filters. From our experiment, we can accelerate the current model and achieve 16.7 FPS assessed in the Jetson Nano. Moreover, the accuracy of the compressed model can be maintained at 86.1%, while the original model is 86.8%. In addition, our approach is more accurate than ResNet18 as the state-of-the-art only reaches 82.7%. The assessment using the corn leaf disease dataset indicates that the compressed model can achieve an accuracy of 97.5%, while the accuracy of the original PCENet is 97.7%.<\/jats:p>","DOI":"10.3390\/s22249717","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T05:42:00Z","timestamp":1670823720000},"page":"9717","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Implementing a Compression Technique on the Progressive Contextual Excitation Network for Smart Farming Applications"],"prefix":"10.3390","volume":"22","author":[{"given":"Setya Widyawan","family":"Prakosa","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering (ECE), National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7197-9912","authenticated-orcid":false,"given":"Jenq-Shiou","family":"Leu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering (ECE), National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}]},{"given":"He-Yen","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering (ECE), National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4968-3450","authenticated-orcid":false,"given":"Cries","family":"Avian","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering (ECE), National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}]},{"given":"Chia-Hung","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering (ECE), National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3185-1495","authenticated-orcid":false,"given":"Stanislav","family":"V\u00edtek","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 16627 Prague, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., and Vento, M. (2021). Progressive Contextual Excitation for Smart Farming Application. Computer Analysis of Images and Patterns. CAIP 2021, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-89131-2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1109\/TMI.2018.2879369","article-title":"Unsupervised Feature Extraction via Deep Learning for Histopathological Classification of Colon Tissue Images","volume":"38","author":"Sari","year":"2019","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mulyanto, M., Faisal, M., Prakosa, S.W., and Leu, J.-S. (2020). 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