A Hybrid Deep Learning Approach for Crop Disease Severity Level Prediction

Authors

  • Vishnu C. Khade Research Scholar, SCOE, SPPU Pune, Assistant Professor, RIT Islampur, Maharashtra, India
  • Sanjay B. Patil Principal, SCSOCE, Dhangawadi, Pune, Maharashtra, India

Keywords:

Disease Severity, Maize Crop, CNN Model, Transfer Learning, Attention Layer.

Abstract

The threat of crop diseases and the need for efficient management are critical concerns, particularly in the context of maize cultivation. Early detection and accurate estimation of disease severity play a pivotal role in safeguarding maize crops and ensuring optimal yield. Convolutional Neural Networks (CNNs) have emerged as invaluable tools for this purpose, showcasing their prowess in automatic feature extraction. In the realm of maize disease severity estimation, the distinct characteristics of diseases, such as variations in lesions texture along with variations its color serve as crucial factors for automated assessment through machine learning. In this paper, a CNN model is developed with combination of transfer learning features from ResNet101 and Inception-V3 models. The features obtained from these models are then combined and passed through the attention layer ensures optimal performance.  With tuning of hyper parameters and 5-fold analysis model is set for highest performance of 0.956 of accuracy. The high specificity of 0.985 shows models suitability for primary stage disease detection. This approach reflects a proactive strategy in addressing the challenges associated with disease severity estimation in maize cultivation, utilizing cutting-edge technologies for the benefit of agricultural sustainability.

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Published

27.12.2023

How to Cite

Khade, V. C. ., & Patil, S. B. . (2023). A Hybrid Deep Learning Approach for Crop Disease Severity Level Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 215–224. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4267

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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