Potato crops are vital to global food security and economy, yet they are vulnerable to a wide range of leaf diseases that can significantly impact yield and quality. Rapid diagnosis and accurate identification of these disorders are critical for effective disease control and prevention. In this research, we offer an extensive evaluation and contrast of three state -of-art CNN models- VGG19, DenseNet121 and ResNet50-in order to identify and forecast potato leaf diseases. Our study employed a sizable dataset of potato leaf images, containing diverse healthy and afflicted specimens, to train and assess the performance of the chosen CNN models. Extensive data augmentation techniques were employed to enhance the dataset’s diversity and generalization capabilities. We evaluated the models considering their accuracy, precision, recall, F1-score and computational efficiency to determine the most fitting model for real-life applications. The results demonstrate that all three CNN models achieved high performance in identifying and predicting potato leaf diseases, with VGG19 emerging as the top performer followed closely by DenseNet121 and ResNet50.Our findings provide valuable insights into the efficacy of DL approaches for potato leaf ailment identification and offer a foundation for future research and deployment of these models in precision agriculture systems. Ultimately, this work aims to support the development of more robust and efficient tools for timely disease diagnosis, enabling farmers and agronomists to make better-informed decisions and safeguard the health and productivity of potato crops worldwide.

Agriculture is pivotal in Bangladesh, with maize being a central crop. However, leaf diseases significantly threaten its productivity. This study introduces deep learning models for enhanced disease detection in maize. We developed an unique datasets of 4800 maize leaf images, categorized into four health conditions: Healthy, Common Rust, Gray Leaf Spot, and Blight. These images underwent extensive Pre-processing and data augmentation to improve robustness. We explored various deep learning models, including ResNet50GAP, DenseNet121, VGG19, and a custom Sequential model. DenseNet121 and VGG19 showed exceptional performance, achieving accuracies of 99.22% and 99.44% respectively. Our research is novel due to the integration of transfer learning and image augmentation, enhancing the models’ generalization capabilities. A hybrid model combining ResNet50 and VGG16 features achieved a remarkable 99.65% accuracy, validating our approach. Our results indicate that deep learning can significantly impact agricultural diagnostics, offering new research directions and applications. This study highlights the potential artificial intelligence in advancing agricultural practices and food security in Bangladesh, emphasizing the need for model interpretability to build trust in machine learning solutions.

The precise identification of FLAIR abnormalities in brain MR images is essential for diagnosing and managing lower-grade gliomas, segmentation continues to be a difficult task. In this research, we develop an exhaustive strategy that integrates advanced deep learning models such as DeepLabv3, U-Net, DenseNet121-Unet, ResNet50, Attention U-Net and EfficientNet to effectively segment FLAIR abnormalities in a dataset comprising 110 lower-grade glioma patients. The cancer Imaging achieve (TCIA), includes genomic cluster data and patient-specific details. Our methodology tackles the multi-class data imbalanced by employing a customized loss function, which merges Categorical Cross Entropy (CCE) WCE and WMDL functions are used to calculate loss, allowing the network to accurately segment smaller tumor regions. By performing dense network training on 3D picture patches, the suggested technique improves detection of border region artifacts and efficiently manages storage and system limited resources. We evaluate our strategy’s effectiveness on the presented dataset, emphasizing its potential for assisting correct diagnosis and individualized treatment strategies for patients with lower-grade gliomas.

Bangladesh is heavily reliant on rice production, but a staggering annual decline of 37% in rice output due to insufficient knowledge in recognizing and managing rice plant diseases has raised concerns. As a result, there is a pressing need for a system that can accurately identify and control rice plant diseases automatically. CNNs have demonstrated their effectiveness in detecting plant diseases, thanks to their exceptional image classification capabilities. Nevertheless, research on rice plant disease identification remains scarce. This study offers a comprehensive overview of rice plant ailments and explores DL techniques used for their detection. By evaluating the advantages and disadvantages of various systems found in the literature, the study aims to identify the most accurate means of detecting and controlling rice plant diseases using DL techniques. We present a real-time detection and diagnostic system for rice lead diseases that utilizes ML methods. This system is designed to identify three prevalent rice plant diseases, specially leaf smut, bacterial leaf blight and brown spot diseases. Clear images of affected rice leaves against a white background serve as input data for the system. To train the dataset, several ML algorithms were employed including KNN, Naive Bayes, J48 and Logistic Regression. Following the pre-processing stage, the decision tree algorithm demonstrated an accurateness of over 97% when claimed to test dataset. In conclusion, implementing an automated system that leverages ML techniques is vital for reducing the time and labor required for detecting and managing rice plant diseases. Such a system would contribute significantly to ensuring the healthy growth of rice plants in Bangladesh, ultimately boosting the nation’s rice production.

INTRODUCTION: Deep Learning has significantly impacted various domains, including medical imaging and diagnostics, by enabling accurate classification tasks. This research focuses on leveraging deep learning models to automate the classification of different blood cell types, thus advancing hematology practices. OBJECTIVES: The primary objective of this study is to evaluate the performance of five deep learning models - ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 - in accurately discerning and classifying distinct blood cell categories: Eosinophils, Lymphocytes, Monocytes, and Neutrophils. The study aims to identify the most effective model for automating hematology processes. METHODS: A comprehensive dataset containing approximately 8,500 augmented images of the four blood cell types is utilized for training and evaluation. The deep learning models undergo extensive training using this dataset. Performance assessment is conducted using various metrics including accuracy, precision, recall, and F1-score. RESULTS: The VGG19 model emerges as the top performer, achieving an impressive accuracy of 99% with near-perfect precision and recall across all cell types. This indicates its robustness and effectiveness in automated blood cell classification tasks. Other models, while demonstrating competence, do not match the performance levels attained by VGG19. CONCLUSION: This research underscores the potential of deep learning in automating and enhancing the accuracy of blood cell classification, thereby addressing the labor-intensive and error-prone nature of traditional methods in hematology. The superiority of the VGG19 model highlights its suitability for practical implementation in real-world scenarios. However, further investigation is warranted to comprehend model performance variations and ensure generalization to unseen data. Overall, this study serves as a crucial step towards broader applications of artificial intelligence in medical diagnostics, particularly in the realm of automated hematology, fostering advancements in healthcare technology.