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Smart agriculture is crucial in ensuring food production by employing advanced technologies to monitor and manage plant health. Accurate detection of diseases is essential to safeguarding the long-term sustainability of agricultural systems. This paper presents an intelligent plant diseases detection system using a modified EfficientNetB2 model, incorporating new dense layers, activity_regularizer, bias_regularizer, and kernel_regularizer techniques. The model underwent training using PlantVillage dataset comprising 38 classes of plant leaves. The study achieved impressive results, with an accuracy of 97.70%. The results clearly indicated the efficacy of the suggested approach, highlighting its capacity to make a substantial contribution to intelligent agricultural practices. The method facilitated early disease identification, empowering farmers to implement preventive measures and foster sustainable crop farming.

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Designing an Effective Model for Plant Diseases Detection and Classification in Smart Agriculture

  • 会议论文

  • 发表日期：

    2026年

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  • Nedaa Jaber Abdulhussian,
  • Walaa Alajali

摘要

Smart agriculture is crucial in ensuring food production by employing advanced technologies to monitor and manage plant health. Accurate detection of diseases is essential to safeguarding the long-term sustainability of agricultural systems. This paper presents an intelligent plant diseases detection system using a modified EfficientNetB2 model, incorporating new dense layers, activity_regularizer, bias_regularizer, and kernel_regularizer techniques. The model underwent training using PlantVillage dataset comprising 38 classes of plant leaves. The study achieved impressive results, with an accuracy of 97.70%. The results clearly indicated the efficacy of the suggested approach, highlighting its capacity to make a substantial contribution to intelligent agricultural practices. The method facilitated early disease identification, empowering farmers to implement preventive measures and foster sustainable crop farming.