Surveillance through digital cameras is increasing exponentially. A majority of these cameras are not smart cameras; therefore, they send their video stream to a central server where it is processed and analyzed for any threats. Typically, human operators or machine learning algorithms at cloud, analyzed and processed the post-event videos to track and locate the perpetrator or victim. The centralized approach leads to two primary shortcomings: 1) the high cost of cloud infrastructure; 2) lack of instant tracking and detection of the threat. One solution is to replace these legacy cameras with the smart cameras so they can process information locally. Although the solution is costly, it could solve the real time threat detection issues. However, the need for a central server remains there, to construct the path of threat, when threat moves from one camera view to another. The existing distributed architectures for threat tracking, shifts the load of threat capturing and processing from central server to the edge nodes, which in turn reduces the computational power but do not remove the role of central server completely. These architectures doesn’t equip each camera of processing and communicating with each other. Further, in the existing distributed architectures the local cameras are not able to store the path of the threat individually, and transmits the captured trajectory to the central body. This research proposed a second alternative that makes use of legacy cameras through additional hardware and software components such that they can process information and collaborate locally. The research addresses the challenge by introducing a low cost distributed threat tracking framework that allows the single camera to identify the threat and communicate its information to other cameras without involving the central server. The framework stores the information in a lightweight architecture that is inspired by the blockchain storage algorithm. The system also allows querying the path traveled by the threat at any stage. To evaluate the system, we performed two simulated experiments: one with a central server and another with the proposed distributed system. The results of the experiments showed that the time to track the threat through the proposed system was lower than the existing centralized system. Moreover, the proposed system predicted the paths of threats with an accuracy of 85.49%. In the future, the technique may be improved with reinforcement learning and other machine learning techniques.

A variety of leaf diseases affecting maize plants led to a notable reduction in crop yield, both in terms of quantity and quality. Traditional visual detection of these diseases was prone to subjectivity and limited accuracy. Therefore, advanced machine learning (ML) and deep learning (DL) techniques were explored to improve classification and detection practices in agricultural research. This study focused on employing ML and DL methodologies for the automated classification of maize leaf diseases, utilizing the "Corn or Maize Leaf Disease Dataset," which includes images of leaves affected by diseases such as common rust, blight, and gray leaf spot, alongside healthy samples. Four algorithms were implemented and evaluated: Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN). The models were trained on the dataset to identify disease patterns and characteristics in maize leaves. The results indicated that the CNN model achieved an accuracy of 95%, demonstrating its superior capability in image classification tasks. while ANN 85%. The comparative analysis provided a comprehensive understanding of the strengths and limitations of each algorithm in the context of maize leaf disease detection. The study concluded that deep learning models, particularly CNNs, are highly effective in this domain, offering precise and efficient disease management with minimal manual intervention. These findings offer valuable insights for future agricultural research and highlight the potential of integrating advanced computational techniques to optimize agricultural diagnostics and enhance crop management.