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AgriSage: Android‐Based Application for Empowering Farmers With E‐Commerce and AI‐Driven Disease Detection

dc.contributor.authorShabeena Naveed
dc.contributor.authorMujeeb Ur Rehman
dc.contributor.authorMumtaz Ali Shah
dc.contributor.authorShahid Sultan
dc.contributor.authorZafar Ullah Khan
dc.contributor.authorSyed Zarak Shah
dc.contributor.authorMansoor Iqbal
dc.contributor.authorMuhammad Ahsan Amjed
dc.contributor.otherUniversity of Management and Technology Sialkot Punjab Pakistan
dc.contributor.otherUniversity of Management and Technology Sialkot Punjab Pakistan
dc.contributor.otherUniversity of Wah, Wah Cantonment Taxila Pakistan
dc.contributor.otherUniversity of Wah, Wah Cantonment Taxila Pakistan
dc.contributor.otherUS‐Pakistan Center for Advanced Studies in Energy, UET Peshawar Peshawar Pakistan
dc.contributor.otherDepartment of Computer Science Harrisburg University of Science and Technology Harrisburg Pennsylvania USA
dc.contributor.otherUniversity of Wah, Wah Cantonment Taxila Pakistan
dc.contributor.otherDepartment of Chemistry, Materials, and Chemical Engineering Politecnico di Milano Milan Italy
dc.date.accessioned2025-08-19T00:44:03Z
dc.date.accessioned2025-10-08T09:15:39Z
dc.date.available2025-10-08T09:15:39Z
dc.date.issued01-08-2025
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/39655
dc.description.abstractABSTRACT Agriculture faces critical challenges such as timely disease detection, fragmented market access, and limited use of real‐time technology in the field. To address these issues, we developed AgriSage, an Android‐based intelligent mobile application that integrates artificial intelligence, weather forecasts, and governmental scheme updates to support farmers, sellers, customers, and policymakers. The application incorporates two optimized deep learning models designed for on‐device deployment. The first model, based on MobileNetV2, performs binary classification to detect the presence of plants in images. It achieved a precision, recall, and F1‐score of 1.00 for both classes, indicating perfect classification performance on the test set. On‐device inference testing of the converted TensorFlow Lite model resulted in an average prediction time of approximately 3736.44 ms per image when evaluated through the validation pipeline. Another deep learning model, that is, a convolutional neural network designed for disease classification, was trained on the PlantVillage dataset across 38 classes. It achieved a macro average F1‐score of 0.8207 and a weighted average F1‐score of 0.8703. The optimized TensorFlow Lite version demonstrated an average inference time of 35.6 ms per image, confirming its suitability for real‐time, on‐device deployment. AgriSage delivers a robust and scalable platform integrating AI‐powered crop monitoring and disease detection. It also provides real‐time agricultural support services, contributing to improved decision‐making and promoting sustainable farming practices.
dc.language.isoEN
dc.publisherWiley
dc.subject.lccEngineering (General). Civil engineering (General)
dc.titleAgriSage: Android‐Based Application for Empowering Farmers With E‐Commerce and AI‐Driven Disease Detection
dc.typeArticle
dc.description.keywordsconvolutional neural networks (CNN)
dc.description.keywordsdeep learning in agriculture
dc.description.keywordsimage classification
dc.description.keywordsMobileNetV2
dc.description.keywordsplant disease detection
dc.description.keywordsTensorFlow lite (TFLITE)
dc.description.pagesn/a-n/a
dc.description.doi10.1002/eng2.70342
dc.title.journalEngineering Reports
dc.identifier.e-issn2577-8196
dc.identifier.oaioai:doaj.org/journal:0b69a131f3704413b2e09d4092e7b5d7
dc.journal.infoVolume 7, Issue 8


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