Version 1
: Received: 14 October 2024 / Approved: 14 October 2024 / Online: 15 October 2024 (12:15:12 CEST)
How to cite:
PATIL, B. V.; Patil, D. P. S. A Hybrid Meta Model for Detecting Cotton Disease Employing an IoT-Based Platform and an Ensemble Learning Methodology. Preprints2024, 2024101122. https://doi.org/10.20944/preprints202410.1122.v1
PATIL, B. V.; Patil, D. P. S. A Hybrid Meta Model for Detecting Cotton Disease Employing an IoT-Based Platform and an Ensemble Learning Methodology. Preprints 2024, 2024101122. https://doi.org/10.20944/preprints202410.1122.v1
PATIL, B. V.; Patil, D. P. S. A Hybrid Meta Model for Detecting Cotton Disease Employing an IoT-Based Platform and an Ensemble Learning Methodology. Preprints2024, 2024101122. https://doi.org/10.20944/preprints202410.1122.v1
APA Style
PATIL, B. V., & Patil, D. P. S. (2024). A Hybrid Meta Model for Detecting Cotton Disease Employing an IoT-Based Platform and an Ensemble Learning Methodology. Preprints. https://doi.org/10.20944/preprints202410.1122.v1
Chicago/Turabian Style
PATIL, B. V. and Dr. Pravin Sahebrao Patil. 2024 "A Hybrid Meta Model for Detecting Cotton Disease Employing an IoT-Based Platform and an Ensemble Learning Methodology" Preprints. https://doi.org/10.20944/preprints202410.1122.v1
Abstract
In the textile industry, cotton plays a significant role as the primary raw material. However, cotton farming often faces challenges from various diseases, resulting in reduced productivity and financial losses for farmers. To address this issue, this study proposes a new method for predicting cotton diseases in Internet of Things (IoT)-based applications. The proposed solution combines meta-heuristic techniques with deep learning (DL). The aim of the proposed method leverages IoT, deep learning, and meta-heuristic techniques to detect and classify cotton plant diseases, providing an accurate and efficient solution for farmers and the textile industry. The process begins with capturing high-resolution images of cotton leaves in agricultural fields using a camera. These images are then processed using IoT to identify potential diseases. Noise removal and quality enhancement are performed using a Probabilistic Hybrid Wiener Filter (PHWF). Next, the Modified Dilated U-Net (MDU-Net) model segments significant disease regions from the images. Effective features are extracted from these segments using Improved Local Binary Pattern (ILBP), Gray Level Co-Occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRM). Feature dimensionality is reduced by selecting optimal features through a Binary Guided Whale-Dipper Throated Optimizer (BGW-DTO). Classification is carried out using a stacking ensemble model, which combines EfficientNet-B7, ResNet50, VGG19, DenseNet121, and InceptionV3 models. To optimize the ensemble, a Harris whale optimization algorithm determines optimal weight coefficients for each classifier. The optimized ensemble model classifies various diseases, including Army Worms, Powdery Mildew, Bacterial Blight, Aphids, and Target Spots. Using our dataset of cotton plant leaf images, the proposed technique achieves a high accuracy of 99.66%. By integrating IoT sensor data and DL, early detection of cotton plant leaf diseases is enabled.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
