Crop plant diseases are a significant threat to productivity and sustainable development in agriculture. Early prediction of disease attacks is useful for the effective control of the disease by taking proactive actions against their attacks. Modern Information and Communication Technologies (ICTs) have a predominant role in Precision Agriculture (PA) applications to support sustainable developments. There is an immense need for solutions for the early prediction of the disease attack for proactive control against the plant disease attack. The present solution of disease detection using the computer vision approach can only detect the existence of the disease, once the disease has already appeared. This study aims to propose a Machine Learning (ML) approach for the early prediction of the probability of disease attack based on Internet of Things (IoT) directly sensed crop field environmental conditions. Plant disease life cycles are strongly correlated with environmental conditions. The crop field environmental conditions are used to predict the occurrence of plant diseases. The Multiple Linear Regression (MLR) is applied as the ML model, due to the existence of a linear relationship between disease attack and environmental conditions. Internet of Things (IoT) based crop field environmental conditions help to accurately predict the occurrence of plant diseases using the ML approach. The proposed model is implemented for the prediction of blister blight (Exobasidium vexans) for tea (Camellia sinensis) plant to check the effectiveness of the proposed solution. The implementation of the proposed model from 2015 to 2019 reveals that the accuracy of prediction of occurrence of the disease reaches up to 91% in 2019.

Issue tracking systems allow users to report bugs. Bug reports often contain product name, product component, description, and severity. Based on such information, triagers often manually prioritize the bug reports for investigation. However, manual prioritization is time-consuming and cumbersome. DRONE is an automated state-of-the-art approach that recommends the priority level information of the bug reports. However, its performance for all levels of priorities are not uniform and may be improved. To this end, in this paper we propose an emotion-based automatic approach to predict the priority for a report. First, we exploit natural language processing techniques to preprocess the bug report. Second, we identify the emotion-words that are involved in the description of the bug report and assign it an emotion-value. Third, we create a feature vector for the bug report and predict its priority with a machine learning classifier that is trained with history data collected from the Internet. We evaluate the proposed approach on Eclipse open-source projects and the results of the cross-project evaluation suggest that the proposed approach outperforms the state-of-the-art. On average it improves the F1-score by more than six percent.

Software systems often receive a large number of bug reports. Triagers read through such reports and assign different priorities to different reports so that important and urgent bugs could be fixed on time. However, manual prioritization is tedious and time-consuming. To this end, in this article, we propose a convolutional neural network (CNN) based automatic approach to predict the multiclass priority for bug reports. First, we apply natural language processing (NLP) techniques to preprocess textual information of bug reports and covert the textual information into vectors based on the syntactic and semantic relationship of words within each bug report. Second, we perform the software engineering domain specific emotion analysis on bug reports and compute the emotion value for each of them using a software engineering domain repository. Finally, we train a CNN-based classifier that generates a suggested priority based on its input, i.e., vectored textual information and emotion values. To the best of our knowledge, it is the first CNN-based approach to bug report prioritization. We evaluate the proposed approach on open-source projects. Results of our cross-project evaluation suggest that the proposed approach significantly outperforms the state-of-the-art approaches and improves the average F1-score by more than 24%.