{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T19:51:04Z","timestamp":1784836264678,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T00:00:00Z","timestamp":1504483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The \u201cResearch Base Construction Fund Support Program\u201d funded by Chonbuk National University in 2016"},{"name":"This work was carried out with the support of &quot;Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0120642016)&quot; Rural Development Administration, Republic of Korea."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called \u201cdeep learning meta-architectures\u201d. We combine each of these meta-architectures with \u201cdeep feature extractors\u201d such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant\u2019s surrounding area.<\/jats:p>","DOI":"10.3390\/s17092022","type":"journal-article","created":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T11:11:52Z","timestamp":1504523512000},"page":"2022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1335,"title":["A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8847-1541","authenticated-orcid":false,"given":"Alvaro","family":"Fuentes","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, Chonbuk National University, Jeonbuk 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sook","family":"Yoon","sequence":"additional","affiliation":[{"name":"Research Institute of Realistic Media and Technology, Mokpo National University, Jeonnam 534-729, Korea"},{"name":"Department of Computer Engineering, Mokpo National University, Jeonnam 534-729, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang","family":"Kim","sequence":"additional","affiliation":[{"name":"National Institute of Agricultural Sciences, Suwon 441-707, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Park","sequence":"additional","affiliation":[{"name":"IT Convergence Research Center, Chonbuk National University, Jeonbuk 54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.virol.2016.08.033","article-title":"Ongoing geographical spread of Tomato yellow leaf curl virus","volume":"498","author":"Mabvakure","year":"2016","journal-title":"Virology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1111\/ppa.12270","article-title":"Arabidopsis thaliana, an experimental host for tomato yellow leaf curl disease-associated begomoviruses by agroinoculation and whitefly transmission","volume":"64","author":"Canizares","year":"2015","journal-title":"Plant Pathol."},{"key":"ref_3","unstructured":"The World Bank (2017, June 20). 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