Hybrid Deep Learning Techniques for Plant Pathogen and Disease Diagnosis using the Internet of Bio-Nano Things

Plant pathogens profoundly impact crop yields and pose a significant barrier to achieving environmentally friendly goals regarding farming, cultivating food, and mitigating hunger-related concerns. The utilization of traditional methods for generalized seasonal pesticide application has been found to cause significant harm to both the surroundings and human well-being. In contrast, nanomaterial biosensors have emerged as cost-effective, highly efficient, specific, rapid, and accurate approaches for diagnosing plant pathogens and diseases. The Internet of Bio-Nano Things (IoBNT) is an emerging networking paradigm that utilizes small, biodegradable, and nonintrusive devices to gather and detect biological signals in the surrounding environment. Integrating the biological and digital realms of the Internet is facilitated by a technologically advanced device known as the Bio Cyber Interface (BCI). The BCI is a technology that facilitates the translation of biochemical messages from nanonetworks within the human body into electromagnetic radiation and, conversely, the translation of electromagnetic radiation into biochemical information. The security of BCIs is a significant concern regarding their practical application. This is because connecting BCIs to the Internet (5G) reveals their interfaces to potential external threats. To effectively address this concern, it is necessary to classify abnormal patterns in BCI traffic accurately. The utilization of current Machine Learning (ML) anomaly detection techniques is hindered by the complexity of parameters and the intricate correlations that exist among BBI traffic parameters. These methods often necessitate the manual design of features. To achieve this objective, the current study examines the utilization of a hybrid ensemble consisting of Convolutional Neural Networks and Long Short-Term Memory (CNN + LSTM) that enable flexible and scalable feature design to distinguish between conventional and abnormal BCI traffic. Through rigorous validation utilizing singular and multi-dimensional models on the generated dataset, our hybrid ensemble Deep Learning (DL) of CNN+LSTM achieved an accuracy of approximately 94.6%, surpassing other DL architectures.