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360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.

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