Next-Generation Plant Disease Detection: A Efficient Approach to Plant Disease Identification with HW-CNNs and Wasserstein Metrics

Next-Generation Plant Disease Detection: A Efficient Approach to Plant Disease Identification with HW-CNNs and Wasserstein Metrics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Next-Generation Plant Disease Detection: A Efficient Approach to Plant Disease Identification with HW-CNNs and Wasserstein Metrics Daya Shankar Verma, Jitendra K. Mishra, Ankit Kumar, Linesh Raja, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6904746/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Timely identification and treatment of plant diseases are essential for boosting agricultural productivity and reducing economic losses. In this study, we present an innovative deep learning framework for patient-automated plant disease detection using a Hierarchical Wasserstein Convolutional Neural Network (HW-CNN). Specifically, we introduce depth-separable convolutions for computational cost savings and a new Hierarchical Wasserstein Distance (HWD) loss function which improves classification by leveraging inter-class relationships. The model was trained and validated on a large dataset containing 53200 Images across 38 different diseases in 14 different species of plants. Additionally, the proposed methodology provides a detailed description of the preprocessing steps (transformation of colour space to H, S, and V, pixel masking of green pixels), feature extraction using Hu moments, Haralick texture features, and colour histograms. The HW-CNN architecture is based on depth-separable convolutions which have been shown to yield very good performance with fewer parameters. The HWD loss function also helps build a more suitable loss landscape that enables the model to generalise across different types of diseases. The HW-CNN outperformed classical machine learning models (SVM, Random Forest, and Logistic Regression) and other deep learning architectures with an accuracy of 99.19%.The experimental results show that the HW-CNN has an accuracy of 99.19%. The experimental results showed similar improvements in performance, while significantly reducing complexity compared to existing methods. Throwing light on the effectiveness of advanced deep learning techniques to overcome significant obstacles in plant disease detection, including serendipitous symptoms and climate differences. The novel HW-CNN architecture forms a scalable, low-power circuitry with high energy efficiency that can tremendously benefit real-world scenario applications such as agriculture, reduce potential crop losses, and improve food security in society. Biological sciences/Biochemistry Biological sciences/Biophysics Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Plant leaves Diseases SVM KNN Deep learning CNN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6904746","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475934509,"identity":"5b3ab311-214e-4575-bdfc-c6ddd0c6e0b3","order_by":0,"name":"Daya Shankar Verma","email":"","orcid":"","institution":"Indian Institute of Information Technology Ranchi","correspondingAuthor":false,"prefix":"","firstName":"Daya","middleName":"Shankar","lastName":"Verma","suffix":""},{"id":475934510,"identity":"95d0995d-e54a-43ea-b687-266cef419954","order_by":1,"name":"Jitendra K. 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In this study, we present an innovative deep learning framework for patient-automated plant disease detection using a Hierarchical Wasserstein Convolutional Neural Network (HW-CNN). Specifically, we introduce depth-separable convolutions for computational cost savings and a new Hierarchical Wasserstein Distance (HWD) loss\u0026ensp;function which improves classification by leveraging inter-class relationships. The model was\u0026ensp;trained and validated on a large dataset containing 53200 Images across 38 different diseases in 14 different species of plants. Additionally, the proposed methodology provides a detailed description of the preprocessing steps (transformation of colour space to H, S, and V,\u0026ensp;pixel masking of green pixels), feature extraction using Hu moments, Haralick texture features, and colour histograms. The HW-CNN architecture is based\u0026ensp;on depth-separable convolutions which have been shown to yield very good performance with fewer parameters. The HWD loss function also helps\u0026ensp;build a more suitable loss landscape that enables the model to generalise across different types of diseases. The HW-CNN outperformed classical machine learning models (SVM, Random Forest, and Logistic Regression) and other deep learning architectures with an accuracy of 99.19%.The experimental results show that the HW-CNN\u0026ensp;has an accuracy of 99.19%. The experimental results showed similar improvements in performance, \u0026ensp;while significantly reducing complexity compared to existing methods. Throwing light on the effectiveness of advanced deep learning techniques to overcome significant obstacles in plant disease detection, including\u0026ensp;serendipitous symptoms and climate differences. The novel HW-CNN architecture forms a scalable, low-power circuitry with high energy efficiency that can tremendously benefit real-world scenario applications such as agriculture, reduce potential crop losses, and improve food security in\u0026ensp;society.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Next-Generation Plant Disease Detection: A Efficient Approach to Plant Disease Identification with HW-CNNs and Wasserstein Metrics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 14:24:39","doi":"10.21203/rs.3.rs-6904746/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33ab33d1-0be4-4a06-aa62-35be985cda02","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50539374,"name":"Biological sciences/Biochemistry"},{"id":50539375,"name":"Biological sciences/Biophysics"},{"id":50539376,"name":"Biological sciences/Biotechnology"},{"id":50539377,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":50539378,"name":"Physical sciences/Energy science and technology"},{"id":50539379,"name":"Physical sciences/Engineering"},{"id":50539380,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-07-31T08:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 14:24:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6904746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6904746","identity":"rs-6904746","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}