Potato Leaf Disease Identification using Hybrid Deep Learning Model

Title

Potato Leaf Disease Identification using Hybrid Deep Learning Model

Creator

Patil M.A.; Manohar M.

Description

The potato is one of the most significant crops in the world. However, it is prone to several leaf diseases that can result in significant productivity losses, leading to economic challenges. Early and precise disease identification is essential for sensitive crops like potatoes. Deep learning approaches have demonstrated excellent potential in image-based disease classification tasks in recent years. This paper presents a hybrid strategy for classifying potato leaf image diseases by integrating Optimised Convolutional Neural Network (OCNN) and Long Short-Term Memory (LSTM) networks. The Adaptive Shark Smell Optimisation (ASSO) technique is used to optimize the weights of CNN models. The CNN component is initially used to extract pertinent characteristics from Potato leaves, capturing significant visual patterns related to various diseases. These extracted features are then fed into the LSTM model, which utilizes its sequential learning capability to model the temporal dependencies among the extracted features. The model performance is analyzed based on Accuracy, Precision, Recall, and F1-score criteria. Experimental results showed that the hybrid OCNN-LSTM model outperforms the individual CNN, LSTM, and MobileNet models. The proposed model results are compared with existing state-of-the-art work, and it was found that the OCNN-LSTM model performed better and received 99.02% accuracy. 2023 IEEE.

Source

2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

CNN; leaf disease; LSTM; OCNN-LSTM; Potato

Coverage

Patil M.A., School O F Engineering and Technology, Dept, of Computer Science and Engineering Christ (Deemed to Be University), Bengaluru, India; Manohar M., School O F Engineering and Technology, Dept, of Computer Science and Engineering Christ (Deemed to Be University), Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 979-835030082-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/NMITCON58196.2023.10276091

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