Tea is a globally important economic crop, and the ability to quickly and accurately identify tea leaf diseases can significantly improve both the yield and quality of tea production. With advances in deep learning, many recent studies have demonstrated that convolutional neural networks are both feasible and effective for identifying tea leaf diseases. In this paper, we propose a modified EfficientNetB0 lightweight convolutional neural network, enhanced with the ECA module, to reliably identify various tea leaf diseases. We used two tea leaf disease datasets from the Kaggle platform: the Tea_Leaf_Disease dataset, which contains six categories, and the teaLeafBD dataset, which includes seven categories. Experimental results show that our method substantially reduces computational costs, the number of parameters, and overall model size. Additionally, it achieves accuracies of 99.49% and 90.73% on these widely used datasets, making it highly suitable for practical deployment on resource-constrained edge devices.
Automated face mask detection remains an important component of hygiene compliance, occupational safety, and public health monitoring, even in post-pandemic environments where real-time and non-intrusive surveillance is required. Traditional deep learning models provide strong re…
Lyme disease, caused by the Borrelia burgdorferi bacterium and transmitted through black-legged (deer) tick bites, is becoming increasingly prevalent globally. According to data from the Lyme Disease Association, the number of cases has surged by more than 357% over the past 15 y…
Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and e…
Infant cries serve as a primary indicator of distress and pain; however, distinguishing pain-related cries from those triggered by other needs remains a challenging task, even for trained professionals. Timely and accurate pain assessment is essential for appropriate medical inte…
Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognit…
Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder worldwide, affecting approximately 56.9 million people in 2021 and projected to reach 152 million by 2050. Its defining pathological features, amyloid-beta plaques and neurofibrillary tangles, accumulate fo…