{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:30:38Z","timestamp":1774967438542,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Crop failure is defined as crop production that is significantly lower than anticipated, resulting from plants that are harmed, diseased, destroyed, or influenced by climatic circumstances. With the rise in global food security concern, the earliest detection of crop diseases has proven to be pivotal in agriculture industries to address the needs of the global food crisis and on-farm data protection, which can be met with a privacy-preserving deep learning model. However, deep learning seems to be a largely complex black box to interpret, necessitating a prerequisite for the groundwork of the model\u2019s interpretability. Considering this, the aim of this study was to follow up on the establishment of a robust deep learning custom model named CropsDisNet, evaluated on a large-scale dataset named \u201cNew Bangladeshi Crop Disease Dataset (corn, potato and wheat)\u201d, which contains a total of 8946 images. The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. To classify corn, potato, and wheat leaf diseases, we used three representative CNN models for image classification (VGG16, Inception Resnet V2, Inception V3) along with our custom model, and the classification accuracy for these three different crops varied from 92.09% to 98.29%. In addition, demonstration of the model\u2019s interpretability gave us insight into our model\u2019s decision making and classification results, which can allow farmers to understand and take appropriate precautions in the event of early widespread harvest failure and food crises.<\/jats:p>","DOI":"10.3390\/data10020025","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T03:47:06Z","timestamp":1739850426000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["CropsDisNet: An AI-Based Platform for Disease Detection and Advancing On-Farm Privacy Solutions"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7055-332X","authenticated-orcid":false,"given":"Mohammad Badhruddouza","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Khulna University of Engineering and Technology, Khulna 9203, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salwa","family":"Tamkin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Brac University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2476-8984","authenticated-orcid":false,"given":"Jinat","family":"Ara","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem u. 10, 8200 Veszprem, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1502-5617","authenticated-orcid":false,"given":"Mobashwer","family":"Alam","sequence":"additional","affiliation":[{"name":"Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanif","family":"Bhuiyan","sequence":"additional","affiliation":[{"name":"Monash Data Futures Institute, Monash University, Clayton Campus, Clayton, VIC 3800, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1038\/nature01019","article-title":"Evolution, consequences and future of plant and animal domestication","volume":"418","author":"Diamond","year":"2002","journal-title":"Nature"},{"key":"ref_2","unstructured":"Hughes, D., and Salath\u00e9, M. 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