Yield monitoring is crucial for the agricultural sector, as it can be used to inform decisions on harvesting, storage, and transportation. Traditionally, several statistical methods and visual inspection techniques are employed to get an early estimate of the final yield of citrus, with the downside of being inaccurate, costly, and time-consuming. In recent years, there have been a lot of advancements in the fields of Artificial Intelligence (AI) and computer vision, providing opportunities to automate plenty of things in different domains, including agriculture. This research proposes a deep learning-based framework that leverages multiple Convolutional Neural Networks (CNN) to efficiently and effectively operate in real-world environments, using field data to provide accurate, improved yield estimates. A high-quality dataset, consisting of citrus tree images, is obtained from orchards at the university research farm Koont and the National Agriculture Research Center (NARC). Afterwards, the CNN-based models are trained thoroughly with various configurations and data augmentation techniques. All the models are rigorously tested and evaluated on the basis of a number of performance metrics. Experiments have shown that YOLOv8m performs with the highest mean average precision, reaching up to 90% with an inference time of a few milliseconds, making it worthy to be deployed for fruit detection, counting, and yield estimation tasks.

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification, which affect the testing process. Therefore, it is difficult to identify all faults in software. As requirement changes continuously, it increases the irrelevancy and redundancy during testing. Due to these challenges; fault detection capability decreases and there arises a need to improve the testing process, which is based on changes in requirements specification. In this research, we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment. The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis. Then compute the similarity of requirements through case-based reasoning, which predicted the requirements for reuse and restricted to error-based requirements. Afterward, the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate. Furthermore, the proposed model was evaluated by conducting experiments. The results showed that requirement redundancy and irrelevancy improved due to semantic analysis, which correctly predicted the requirements, increasing the fault detection rate and resulting in high user satisfaction. The predicted requirements are mapped into test cases, increasing the fault detection rate after changes to achieve higher user satisfaction. Therefore, the model improves the redundancy and irrelevancy of requirements by more than 90% compared to other clustering methods and the analytical hierarchical process, achieving an 80% fault detection rate at an earlier stage. Hence, it provides guidelines for practitioners and researchers in the modern era. In the future, we will provide the working prototype of this model for proof of concept.