Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based on agriculture. Pests are one of the major challenges in crop production worldwide. To reduce the overall production and economic loss from pests, advancement in computer vision and artificial intelligence may lead to early and small pest detection with greater accuracy and speed. In this paper, an approach for early pest detection using deep learning and convolutional neural networks has been presented. Object detection is applied on a dataset with images of thistle caterpillars, red beetles, and citrus psylla. The input dataset contains 9875 images of all the pests under different illumination conditions. State-of-the-art Yolo v3, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 have been adopted in this study for detection. All of these models were selected based on their performance in object detection. The images were annotated in the Yolo format. Yolov8 achieved the highest mAP of 84.7% with an average loss of 0.7939, which is better than the results reported in other works when compared to small pest detection. The Yolov8 model was further integrated in an Android application for real time pest detection. This paper contributes the implementation of novel deep learning models, analytical methodology, and a workflow to detect pests in crops for effective pest management.

ADSD (Agile Distributed Software Development) is a growing trend for software development organizations to develop quality software with limited cost and time. However, it gives rise to additional situational challenges. Situational variations result in unstable agile architecture which gets highly affected. Although the present literature focuses on agile architecture but effect of situational variation on the agile architecture still needs consideration. One possible solution is development of conceptual model and incorporation of multiple situational factors. This research study aims to identify the most relevant situations and propose situational ADSD approach for the development of situation-based software architecture framework for agile distributed projects. The study focuses on agile methods, rendering identified situational variations. The approach is developed through analyzing data from literature and their associated work documents. Comprehensive survey helps to bridge the gaps from conceptual to architecture model. The experimental results are acquired through both practical and statistical analysis. The results support Conway’s law, e.g. correlation that maps architecture with the communication and coordination needs. Correlation results show that architecture has major contribution in ADSD and validate the relationship among conceptual model and architecture model. The results also suggest that stable architecture in ADSD can positively affect the product.

Department of Software Engineering, In the current application development strategies, families of productsare developed with personalized configurations to increase stakeholders’ satisfaction. Product lines have theability to address several requirements due to their reusability and configuration properties. The structuringand prioritizing of configuration requirements facilitate the development processes, whereas it increases theconflicts and inadequacies. This increases human effort, reducing user satisfaction, and failing to accommodatea continuous evolution in configuration requirements. To address these challenges, we propose a framework formanaging the prioritization process considering heterogeneous stakeholders priority semantically. Featuresare analyzed, and mined configuration priority using the data mining method based on frequently accessed andchanged configurations. Firstly, priority is identified based on heterogeneous stakeholder’s perspectives usingthree factors functional, experiential, and expressive values. Secondly, the mined configuration is based on frequentlyaccessed or changed configuration frequency to identify the new priority for reducing failures or errorsamong configuration interaction. We evaluated the performance of the proposed framework with the help ofan experimental study and by comparing it with analytical hierarchical prioritization (AHP) and Clustering.The results indicate a significant increase (more than 90 percent) in the precision and the recall value of theproposed framework, for all selected cases.