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This research presents an automated system for carambola disease diagnosis using Convolutional Neural Networks (CNNs), which contains two modules, i.e., Carambola Identification and Disease Classification. It accurately identifies carambola objects and categorizes diseases through extensive real-world dataset experiments. It also presents early detection and enhanced crop management. This system promises to transform agricultural diagnostics. Through the research, the developed Carambola Identification Model achieved an impressive F1 score of 1, while the Disease Classification Model achieved an F1 score of 0.98. This system holds significant potential for sustainable carambola cultivation practices, benefiting farmers, researchers, and policymakers alike. This innovation represents a significant step toward optimizing agricultural productivity and ensuring food security in carambola cultivation regions. Its scalability and adaptability suit broader agricultural applications.

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Disease Classification of Star Fruit (Averrhoa carambola L.) Using Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Samanawaya Datta,
  • Sakyojit Banerjee,
  • Susovan Jana

摘要

This research presents an automated system for carambola disease diagnosis using Convolutional Neural Networks (CNNs), which contains two modules, i.e., Carambola Identification and Disease Classification. It accurately identifies carambola objects and categorizes diseases through extensive real-world dataset experiments. It also presents early detection and enhanced crop management. This system promises to transform agricultural diagnostics. Through the research, the developed Carambola Identification Model achieved an impressive F1 score of 1, while the Disease Classification Model achieved an F1 score of 0.98. This system holds significant potential for sustainable carambola cultivation practices, benefiting farmers, researchers, and policymakers alike. This innovation represents a significant step toward optimizing agricultural productivity and ensuring food security in carambola cultivation regions. Its scalability and adaptability suit broader agricultural applications.