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Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.
Khan, Bodruzzaman; Das, Subhabrata; Fahim, Nafis Shahid; Banerjee, Santanu; Khan, Salma; Al-Sadoon, Mohammad Khalid; Al-Otaibi, Hamad S; Islam, Abu Reza Md Towfiqul.
Affiliation
  • Khan B; Department of Agricultural Construction and Environmental Engineering, Sylhet Agricultural University, Sylhet, 3100, Bangladesh. bodruzzamankhan.sau@gmail.com.
  • Das S; Langmuir Center of Colloids and Interfaces, Columbia University in the City of New York, New York, USA.
  • Fahim NS; Department of Agricultural Construction and Environmental Engineering, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
  • Banerjee S; Department of Agriculture, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, 208012, India.
  • Khan S; Institute of Leather Engineering and Technology, University of Dhaka, Dhaka, 1209, Bangladesh.
  • Al-Sadoon MK; Department of Zoology, College of Science, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia.
  • Al-Otaibi HS; Department of Zoology, College of Science, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia.
  • Islam ARMT; Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh. towfiq_dm@brur.ac.bd.
Sci Rep ; 14(1): 21525, 2024 09 14.
Article in En | MEDLINE | ID: mdl-39277634
ABSTRACT
Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Plant Diseases / Bayes Theorem / Solanum lycopersicum / Plant Leaves / Deep Learning Language: En Journal: Sci Rep Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Plant Diseases / Bayes Theorem / Solanum lycopersicum / Plant Leaves / Deep Learning Language: En Journal: Sci Rep Year: 2024 Document type: Article