Wheat leaf disease prevention and treatment requires a accurate and rapid classification of wheat leaf diseases and their extent. Using healthy wheat, leaf rust, crown and root rot, and wheat loose smut as research objects, this study proposes a deep learning-based technique for classifying the wheat leaf diseases. A collaborative generative adversarial network is used as an image imputation in the proposed methodology, allowing a generator and discriminator network to properly estimate the missing data in the dataset using the residual method. It is used to improve the feature extraction in wheat leaf images. The major contribution of this study is to use a pre-trained deep learning convolutional neural network architecture as a foundation to improve and construct an automated tool for wheat leaf disease image categorization. To classify wheat leaf diseases, a modification to ResNet50 is being suggested. The ′Conv′, ′Batch Normaliz′, and ′Activation Leaky Relu′ layers were added as part of this modification. These layers are inserted into the ResNet50 architecture for accurate feature extraction and discrimination. Extensive tests are carried out to evaluate the proposed model's performance on photos from a large wheat disease classification dataset. The suggested approach outperforms ResNet50, InceptionV3, and DenseNet, according to the experimental findings. The suggested method achieves the greatest identification accuracy of 98.44%. These discoveries might aid in the accurate detection and categorization of wheat leaf diseases.
As the population on the earth is growing, the long-ranging planning of health and medical facilities are affected. Especially with old-aged people, health issues are more compared with other aged people. The medication given by the doctors to old age people to those health issues is not rememberable. People need to take the pills with a specified dose at a recommended time and frequency especially in case of diabetes and high blood pressure. To overcome the problem an IoT device is designed to remember about their medication time to the old people and their caretakers. The IR sensor present in the system will be continuously monitoring whether medicines has been taken properly by the patient or not. By using the GSM, the caretakers have been notified to their smart phones and watches. So, we design a pillbox which acts as a safety net for patients. The main objective of the system is to inform the patients to take their medicines in time that is prescribed by the doctor and to inform their family members which reduces their work.
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