Nowadays, one of the most popular applications is cloud computing for storing data and information through World Wide Web. Since cloud computing has become available, users are rapidly increasing. Cloud computing enables users to obtain a better and more effective application at a lower cost in a more satisfactory way. Health services data must therefore be kept as safe and secure as possible because the release of this data could have serious consequences for patients. A framework for security and privacy must be employed to store and manage extremely sensitive data. Patients’ confidential health records have been encrypted and saved in the cloud using cypher text so far. To ensure privacy and security in a cloud computing environment is a big issue. The medical system has been designed as a standard, access of records, and effective use by medical practitioners as required. In this paper, we propose a novel algorithm along with implementation details as an effective and secure E-health cloud model using identity-based cryptography. The comparison of the proposed and existing techniques has been carried out in terms of time taken for encryption and decryption, energy, and power. Decryption time has been decreased up to 50% with the proposed method of cryptography. As it will take less time for decryption, less power is consumed for doing the cryptography operations.

Numerous instances of dragon fruit diseases over the years have caused an estimated 14% annual crop loss globally, affecting untold millions of people. The study of dragon fruit illnesses, or pathology, aims to increase plants’ chances of surviving in the presence of pathogenic parasite microbes and poor climatic conditions. Environment-related elements that influence the development of dragon fruit diseases include temperature, pH, humidity, and moisture. Misdiagnosis can result in chemical overuse that costs money, environmental imbalance and pollution, and the establishment of pathogen strains that are resistant to treatment. The process of diagnosing diseases nowadays is time-consuming, expensive, and dependent on human scouting. Compared to the current method, automatic disease segmentation and diagnosis from dragon fruit photos can be somewhat useful. This study uses Convolutional Neural Network (CNN) models, to accurately identify four kinds of apple plant diseases from photographs of apple plant leaves. “Healthy,” “scab,” “rust,” and “many illnesses” are among the classifications.

Powdery mildew is a fungal disease that affects kiwi fruit plants and leads to a reduction in yield and quality. Early detection and classification of the disease can help farmers to take necessary measures to curb the transmission of the infection. In this research, we conducted binary and multi-classification of kiwi powdery mildew disease (KPMD) using 12000 images of kiwi fruit. The binary classification was done using two classes, healthy and inf, while multi-classification was done using four different classes of powdery mildew. An integrated CNN and LSTM model was developed for multi-classification, which resulted in an accuracy of 92.14% in binary classification and 95.91% in multi-classification. The results were analyzed based on various performance parameters, and the proposed model demonstrated encouraging results in terms of accuracy for both detection and classification. The analysis also revealed that the proposed model had the highest accuracy for detecting powdery mildew in its terminal severity level. This research provides a useful tool for the early detection and classification of kiwi powdery mildew disease, which can assist farmers in preventing the spread of the disease and improving crop yield and quality.

One of the main crops in agricultural fields is considered to be rice, which is produced and used extensively over the world. Due to this very reason, there is always a high risk of rice crops getting prone to disease. Rice leaf blight (RLB) disease detection system has been developed using 3000 real-time image dataset which is trained and validated on algorithm on deep learning (DL) convolutional neural networks (CNN) approach. The whole experiment is conducted in two phases, first is the binary classification of fine and RLB infected crop has been experimented, and secondly, multi-classification of RLB disease crop based on RLB disease severity is conducted. The accuracy of 94.33 % and 95.3 % have been achieved in binary and multi-classification infection severity. In addition to this the proposed study will contribute in enhancement of quality of life, technological progress, research development and industry innovation.

The fungal disease known as cucumber leaf spot (CLS) is capable of causing substantial damage to cucumber crops, leading to a decrease in production and quality. Early detection and management of the disease are critical for minimizing its impact on crop productivity. In this study, the authors developed a Residual Next-50 (ResNext-50) deep learning (DL) model based on 5 different severity levels for the multi-classification of CLS disease. The work collected a dataset of 50,000 digital images of cucumber leaves from multiple sources, including local farmers and agricultural research institutions. The dataset was segmented into training, validation, and testing groups, each of which contained 7,500 pictures. The training set contained 35,000 photos. The proposed model achieved an overall accuracy of 97.81% on the testing set, outperforming several baseline models, including U-Net, YOLO v5, and KNN. The confusion matrix revealed that the model was most accurate in identifying cucumber leaves with very low severity of the disease (Level 1) and very high severity of the disease (Level 5) while having lower precision values for cucumber leaves with moderate severity of the disease (Level 3). The suggested study demonstrates the potential of DL models for the accurate and efficient classification of CLS disease based on severity levels, providing valuable insights for the early detection and management of the disease. The insight that was gleaned from this research could also contribute to the development of methods that are more efficient in the prevention and management of CLS illness, ultimately improving the sustainability and productivity of cucumber cultivation.