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Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based Model

Author

Listed:
  • Adel Sulaiman

    (Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia)

  • Vatsala Anand

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India)

  • Sheifali Gupta

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India)

  • Hani Alshahrani

    (Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia)

  • Mana Saleh Al Reshan

    (Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia)

  • Adel Rajab

    (Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia)

  • Asadullah Shaikh

    (Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia)

  • Ahmad Taher Azar

    (College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Automated Systems and Soft Computing Lab (ASSCL), Prince Sultan University, Riyadh 11586, Saudi Arabia)

Abstract

Apple foliar diseases are a group of diseases that affect the leaves of apple trees. These diseases can significantly impact apple tree health and fruit yield. Ordinary apple foliar diseases include frog_eye_leaf_spots, powdery mildew, rust, apple scabs, etc. Early detection of these diseases is important for effective apple crop management to increase the yield of apples. Therefore, this research proposes a fine-tuned EfficientNetB3 model for the quick and precise assessment of these apple foliar diseases. A dataset containing 23,187 RGB images of eleven different apple foliar diseases is used for experimentation. The proposed model is compared with four transfer learning models, i.e., InceptionResNetV2, ResNet50, AlexNet, and VGG16. All models are fine-tuned by adding different layers like the global average pooling layer, flatten layer, dropout layer, and dense layer. The performance of these five models is compared in terms of the precision, recall, accuracy, and F1-score. The EfficientNetB3 outperformed the other models in terms of all performance parameters. The best model is further optimized with the help of three optimizers, i.e., Adam, SGD, and Adagrad. The proposed model achieved the precision, recall, and F1-score values of 86%, 88%, and 86%, respectively, at 32 batch sizes and 10 epochs. This research formulated a model for an apple foliar disease diagnosis within sustainable agriculture.

Suggested Citation

  • Adel Sulaiman & Vatsala Anand & Sheifali Gupta & Hani Alshahrani & Mana Saleh Al Reshan & Adel Rajab & Asadullah Shaikh & Ahmad Taher Azar, 2023. "Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based Model," Sustainability, MDPI, vol. 15(17), pages 1-15, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13228-:d:1232299
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