{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:03:45Z","timestamp":1784300625802,"version":"3.55.0"},"reference-count":49,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease\u2019s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.<\/jats:p>","DOI":"10.3390\/s21237987","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":369,"title":["Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network"],"prefix":"10.3390","volume":"21","author":[{"given":"Naresh K.","family":"Trivedi","sequence":"first","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vinay","family":"Gautam","sequence":"additional","affiliation":[{"name":"School of Computing, DIT University, Dehradun 248009, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3505-8563","authenticated-orcid":false,"given":"Abhineet","family":"Anand","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani Moaiteq","family":"Aljahdali","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 37848, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7065-4793","authenticated-orcid":false,"given":"Santos Gracia","family":"Villar","sequence":"additional","affiliation":[{"name":"Higher Polytechnic School\/Industrial Organization Engineering, Universidad Europea del Atl\u00e1ntico, Isabel Torres 21, 39011 Santander, Spain"},{"name":"Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4915-8426","authenticated-orcid":false,"given":"Divya","family":"Anand","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7878-363X","authenticated-orcid":false,"given":"Nitin","family":"Goyal","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-4842","authenticated-orcid":false,"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[{"name":"Faculty of Applied Computing and Technology, Noroff University College, 4608 Kristiansand, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.gfs.2017.09.005","article-title":"Tapping the economic and nutritional power of vegetables","volume":"16","author":"Schreinemachers","year":"2018","journal-title":"Glob. 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