IoT‐Enhanced Meta‐Heuristic Hybrid Deep Learning Model for Predicting Cotton Leaf Diseases

Abstract

In the textile industry, cotton serves as a crucial raw material; however, diseases affecting cotton leaves can result in substantial financial losses for farmers. Conventional illness detection techniques are frequently costly, time‐consuming, and inaccurate. Existing deep learning models can detect and classify affected leaves, but they face several limitations, including high error rates, excessive time consumption, a tendency for overfitting, and suboptimal performance. To overcome these issues, this work proposes a hybrid deep learning model with meta‐heuristic support integrated with Internet of Things applications to efficiently classify cotton plant diseases. This creative concept seeks to give the textile sector and farmers a more precise and efficient solution. The proposed approach consists of two phases: first, high‐resolution images of cotton leaves are captured using a Canon EOS 450D digital camera, and potential diseases are identified through IoT sensors. In the second step, advanced techniques like pre‐processing, segmentation, feature extraction, feature selection, and classification are implemented. Disease segmentation is accomplished via the modified dilated u‐net (MDU‐Net) model. Feature selection utilising the Binary Guided Whale‐Dipper Throated Optimizer (BGW‐DTO) helps to identify the most relevant properties. Using the Harris Whale Optimization Method, the best weight coefficients for every classifier are found; next, a stacking ensemble model using the most recent deep learning approaches performs classification. In a collection of photos of cotton plant leaves, the optimal ensemble model shows a 99.66% classification rate, thereby precisely diagnosing a range of illnesses comprising Army Worms, Powdery Mildew, Bacterial Blight, Aphids, and Target Spots.

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