The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

Eye state detection refers to the technology and techniques used to determine the state or condition of a person's eyes, such as whether they are open or closed, focused or unfocused, or exhibiting specific eye movements. Many disciplines, including medicine, neurology, and human-computer interaction, depend on the accurate and reliable detection of eye open and eye close states. Findings indicating eye-state behavior, such as blinking frequency, may indicate stress reaction have recently sparked interest in determining eye-state using EEG signals. In this study the performance of three deep learning-based algorithms (Gated Recurrent Unit, Long Short-Term Memory and Recurrent Neural Network) for eye state detection have been investigated. Principal Component Analysis has been used for dimensionality reduction. Finally, results are compared on EEG data collected from UCI machine-learning repository.