Shafik W, Tufail A, Liyanage CDS, Apong RAAHM. Using a Novel Convolutional Neural Network for Plant Pests Detection and Disease Classification.
J Sci Food Agric 2023. [PMID:
37177888 DOI:
10.1002/jsfa.12700]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/16/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND
Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security.
METHODOLOGY
An enhanced convolutional neural network (CNN) along with long short-term memory (LSTM) using a majority voting (MVE) ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre-trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM-CNN model for detecting plant pests and diseases. Experiments were carried out using Turkey Dataset, with 4,447 apple pests and diseases categorized into 15 different classes.
RESULTS
The study has been evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting (EMV) classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1-score, accuracy, and specificity using LSTM, ELM, and LR classifiers.
CONCLUSION
The presented model attained 99.2% accuracy in comparison to the cutting-edge models on different classifiers like LSTM, LR, and ELM, and performed better in comparison to transfer learning (TL). Pre-trained models, like VGG-19, VGG-18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared to other layers. This article is protected by copyright. All rights reserved.
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