Apple leaf disease detection is of critical importance for enhancing productivity in agriculture while reducing losses caused by diseases. Conventional apple leaf disease detection techniques are often inefficient and inaccurate because of environmental factors and the similarity of symptoms in the early stages of diseases. In this paper, a novel intelligent multi-model-based apple leaf disease detection system is proposed for enhanced accuracy and robustness of the proposed method. In the proposed method, a wide range of preprocessing techniques are employed for background removal, segmentation of the apple leaves, and extraction of disease region information. Various models such as MobileNetV2, ResNet50, DenseNet121, EfficientNetB0, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) are implemented and compared for enhanced accuracy of the proposed method. An adaptive ensemble method is also proposed for combining the results of different models for enhanced generalization performance of the proposed method. From the experimental results, it is observed that the proposed method based on SVM with deep feature extraction using MobileNetV2 achieves a high accuracy of 99.1%. Furthermore, a novel disease severity estimation module is also proposed for quantification of the percentage of disease infection in the apple leaves using HSV color space segmentation techniques.
S. Sanjaay, V. S. Kirithic Adhithya, I. S et al.· 2026 International Conferenc...· 0 citations
Corn diseases are a major threat to the food security of the world. However, the existing deep learning models are "heavy" in terms of computation and are not feasible for mobile applications. This paper proposes a model by modifying the existing DenseNet and EfficientNet models for the classification of four different types of corn leaves, namely Common Rust, Cercospora Leaf Spot, Northern Leaf Blight, and Healthy. Using the PlantVillage dataset and the existing data augmentation strategy, the proposed model integrating EfficientNet and DenseNet models has shown a high accuracy in terms of validation. This model has shown the feasibility of the high-precision corn leaf disease detection with minimal memory usage. Experimental results demonstrate that the proposed hybrid EfficientNetB0–DenseNet121 model achieves a classification accuracy of 96.94% with reliable and consistent performance. The model provides an efficient solution for accurate corn leaf disease detection while maintaining low computational complexity. These results highlight its potential for practical deployment in intelligent and precision agriculture applications.
Yada Mani Sai, Chittaluru Sai Kowshika Reddy, I. S· 2026 International Conferenc...· 0 citations
Diabetes has become a health problem worldwide. It often goes unnoticed until it causes health issues. Finding diabetes early using a lot of health and personal data can help reduce the diseases impact and healthcare costs. This study proposes a machine learning system for diabetes prediction. This system uses techniques to prepare data select important features handle unequal class distributions and combine multiple models. It is designed to process types of data from Electronic Health Records (EHRs) lifestyle factors and clinical measurements efficiently. Multiple machine learning models, for example tree-based classifiers, simple linear models and combined models are. Tested. Cross-validation is used to ensure the models are reliable and can be scaled up. The prediction of diabetes mellitus is based on identifying factors, so the importance analysis of characteristics is used to find the most influential predictors of diabetes. Oversampling of medical data involves the use of oversampling to overcome the problem of class distributions. The findings indicate that the given approach is more accurate, precise, possesses higher recall and F1-score, as well as ROC-AUC, compared to other models. This developed system offers an understandable solution for assessing diabetes risk early. It can be used in healthcare screening systems and clinical decision-support platforms for diabetes mellitus.
Thatikonda Krishna Kalyan Gupta, Oruganti Yashwanth Reddy, I. S et al.· 2026 International Conferenc...· 0 citations
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