Rice, the world’s most extensively cultivated cereal crop, serves as a staple food and energy source for over half of the global population. A variety of abiotic and biotic factors such as weather conditions, soil quality, temperature, insects, pathogens, and viruses can greatly impact the quantity and quality of rice grains. Studies have established that plant infections have a significant impact on rice crops, resulting in substantial financial losses in agriculture. To accurately diagnose and manage the diseases affecting rice plants, plant pathologists are seeking efficient and reliable methods. Traditional disease detection techniques, employed by farmers, involve time-consuming visual inspections and result in inadequate farming practices. With advancements in agricultural technology, the identification of pathogenic organisms in rice plants has become significantly more manageable through techniques such as machine learning and deep learning, which are receiving significant attention in crop disease research. In this paper, we used the transfer learning approach on 15 pre-trained CNN models for the automatic identification of Rice leave diseases. Results showed that the InceptionV3 model is outperforming with an average accuracy of 99.64% with Precision, Recall, F1-Score, and Specificity as 98.23, 98.21, 98.20, and 99.80, and the AlexNet model resulted in poor performance with average accuracy of 97.35% among others.

With the speeding up of social activities, rapid changes in lifestyles, and an increase in the pressure in professional fields, people are suffering from several types of sleep‐related disorders. It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods. For the purpose of accurate diagnosis of different sleep disorders, we have considered the automated analysis of sleep epochs, which were collected from the subjects during sleep time. The complete process of an automated approach of sleep stages’ classification is majorly executed through four steps: pre‐processing the raw signals, feature extraction, feature selection, and classification. In this study, we have extracted 12 statistical properties from input signals. The proposed models are tested in three different combinations of features sets. In the first experiment, the feature set contained all the 12 features. The second and third experiments were conducted with the nine and five best features. The patient records come from the ISRUC‐Sleep database. The highest classification accuracy was achieved for sleep staging through combinations with the five feature set. From the categories of the subjects, the reported accuracy results were found to exceed above 90%. As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.

Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on performance metrics such as classification accuracy, precision and recall, they fail to assess whether the models are considering relevant features for decision-making. The main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models. Eight pre-trained deep learning models - ResNet50, InceptionResNetV2, DenseNet 201, InceptionV3, EfficientNetB0, Xception, VGG16 and AlexNet,were evaluated using a three-stage methodology. First, the models are assessed using traditional classification metrics. Second, Local Interpretable Model-agnostic Explanations (LIME) is employed to visualize and quantitatively evaluate feature selection using metrics such as Intersection over Union (IoU) and the Dice Similarity Coefficient (DSC). Third, a novel overfitting ratio metric is introduced to quantify the reliance of the models on insignificant features. In the experimental analysis, ResNet50 emerged as the most accurate model, achieving 99.13% classification accuracy as well as the most reliable model demonstrating superior feature selection capabilities (IoU: 0.432, overfitting ratio: 0.284). Despite the high classification accuracies, models such as InceptionV3 and EfficientNetB0 showed poor feature selection capabilities with low IoU scores (0.295 and 0.326) and high overfitting ratios (0.544 and 0.458), indicating potential reliability issues in real-world applications. This study introduces a novel quantitative methodology for evaluating deep learning models that goes beyond traditional accuracy metrics, enabling more reliable and trustworthy AI systems for agricultural applications. This methodology is generic and researchers can explore the possibilities of extending it to other domains that require transparent and interpretable AI systems.