Analysis of Transfer Learning Methods in Optimizing Apple Tree Disease Detection

Document Type : Extension

Authors

1 Department of Artificial Intelligence, Faculty and Research Institute of Artificial Intelligence and Cognitive Sciences, Imam Hossein University (AS), Tehran, Iran

2 Faculty of Computer, Network and Communication, Imam Hossein University (AS), Tehran, Iran

Abstract

Accurate detection of apple leaf diseases is essential to prevent the reduction in both the quantity and quality of crop yield. With advancements in deep learning methods, the diagnosis of these diseases is improving, but data limitations remain a significant barrier. This research evaluates pretrained deep learning models with precise settings and demonstrates that high accuracy in disease detection is possible even with limited data. The selected models perform better than conventional methods and introduce transfer learning as an effective strategy to combat limited data and the diversity of diseases. These models aid farmers and horticulture specialists in identifying and managing apple leaf diseases more efficiently and rapidly. Additionally, these models are not only beneficial for farmers but also for agricultural consultants, students of agricultural sciences, and researchers in this field. Moreover, these methods are adaptable to other diseases and plants, promising advancements in plant disease detection systems in the future. The present study has the potential to revolutionize horticultural processes through optimization and automation, leading to a transformation in plant disease management.

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History

  • Receive Date: 07 November 2023
  • Revise Date: 06 January 2024
  • Accept Date: 06 February 2024

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