PLANT Detect Net: a hybrid IoT and deep learning framework for secure plant disease detection and classification

Internet of Things has been increasingly well-known in recent days as a result of its numerous applications. The Internet of Things plays a pivotal role in transforming traditional agricultural practices by enabling smart and precise plant disease detection and classification. Through a network of interconnected sensors and devices, the Internet of Things facilitates real-time monitoring and data collection from agricultural fields. Yet, the traditional detection techniques are limited by poor accuracy, high computational complexity, class imbalance, and overfitting issues. This work introduces a hybrid deep learning methodology known as PlantDetectNet with the IoT to overcome the above difficulties and achieve enhanced outcomes in the plant disease detection and classification process. In this research, the proposed framework employs sensor data and PlantVillage dataset images for accurate disease identification. In the proposed scheme, a gated recurrent unit is applied for extracting the sensor data's temporal features and Depthcat convolutional neural network is utilized for extracting the spatial features from the input data. The Global Visual Geometry Group 16 framework is employed for mitigating the overfitting, a number of parameters, and refining the intermediate layer features. The Gated ConvNeXt model is utilized for enhancing the classification outcome of the model and effectively gathering the information in the modeling of channel-wise relationships. Additionally, the research introduces a Residual DenseNet approach for eliminating the invalid features and improving the significant features. The experimental results show that the proposed framework attained a high accuracy of 98.8% and a higher recall of 95.9% compared to existing approaches. These simulation findings prove that the proposed methodology enhanced efficiency, accuracy, and scalability in leaf disease detection and improved crop yields.