This research presents an advanced paddy disease classification model utilizing the EfficientNet B4 deep learning architecture trained on the publicly available Paddy Doctor dataset, which includes 19,131 labeled images for training and 4,785 images for testing. The model aims to classify paddy leaf samples into nine disease categories or into normal specimens. EfficientNet B4, known for its structured scaling technique and Swish activation function, was trained using pretrained weights from ImageNet and optimized with the Adam optimizer. It achieved a peak accuracy of 99.09% during training and 96.91% during testing, highlighting its superior performance in image classification tasks. The model’s architecture, featuring mobile inverted bottleneck convolution (MBConv) and deep separable convolutions, facilitated efficient feature extraction, resulting in high accuracy, recall, and F1-scores, along with low loss values. Our findings demonstrate the model’s reliability and efficiency in diagnosing various paddy illnesses, underscoring the transformative potential of deep learning techniques such as EfficientNet B4 in enhancing agricultural disease management.

This review paper presents a thorough analysis of 99 recent studies focused on applying deep learning techniques for the automated diagnosis of various eye diseases, including glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. The advent of deep learning methodologies has revolutionized the field of ophthalmic diagnostics, offering promising solutions for enhancing the accuracy, efficiency, and accessibility of disease detection. The comprehensive examination encompasses diverse deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, along with the exploration of diverse imaging modalities, such as fundus photography, optical coherence tomography (OCT), and visual field testing. Each disease category was scrutinized individually, highlighting the unique challenges and opportunities for automated diagnosis. Key findings and advancements in the field are discussed, shedding light on the potential of deep learning algorithms for early disease detection and timely intervention. This review also addresses existing limitations, including data variability, interpretability, and the need for large, diverse datasets. Insights from this literature synthesis aim to guide future research directions, fostering the continued development of reliable and efficient deep learning-based diagnostic tools for eye diseases.