Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection.

Food waste is a pressing global issue, particularly in urban settings, where substantial amounts of surplus food go unused. In corporate environments, this challenge is compounded by the lack of dedicated platforms to facilitate food sharing and reduce waste effectively. This paper examines the current landscape of food waste, existing solutions, and the need for a specialised platform aimed at corporate employees. The proposed solution is the creation of a user-friendly application that enables the sharing of untouched homemade meals. Suppliers can post their meal boxes with details such as location, type of food, and availability status, while consumers can search for and select meal boxes based on their preferences. This paper addresses the gap in solutions for reducing food waste within corporate environments. The meal-box-sharing app provides a practical and sustainable method for minimising food waste and promoting productivity, health, and safety in the workplace.

This paper introduces a deep learning approach for detecting plant leaf diseases. The objective is to develop robust models capable of accurately identifying plant diseases across various image backgrounds, thereby overcoming the limitations of existing methods that often rely on controlled laboratory conditions. To achieve this, a combination of the PlantDoc dataset and Web-sourced data of plant images from online platforms was used. This paper implemented and compared state-of-the-art *cnn architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, all fine-tuned specifically for leaf disease classification. A significant contribution is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. Results indicated varied performance across the datasets, with EfficientNet models generally outperforming others. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved the highest accuracy of 73.31%. In cross-dataset evaluation, EfficientNet-B3 reached 76.77% accuracy when trained on PlantDoc and tested on the Web-sourced dataset. The best performance occurred when training on the combined dataset and testing on the Web-sourced data, resulting in an accuracy of 80.19%. Class-wise F1-scores revealed consistently high performance (>0.90) for diseases such as apple rust leaf and grape leaf across models. This paper contributes to the comparative analysis of various datasets and model architectures for effective leaf disease detection.

In today's world, the escalating global insecurity poses significant challenges for both developed and undeveloped countries. The high crime rates and shortcomings in reporting crimes are pressing issues, emphasizing just how crucial it is to ensure the safety of citizens. While law enforcement agencies play a vital role, relying solely on them is insufficient. Embracing modern technology and fostering active citizen participation are essential to address these concerns effectively. This research adopts an agile methodology to tackle the above concerns. The result of this approach is the development of a user-friendly mobile application that leverages cutting-edge technology. The primary objective of this application is to effectively map incidents of crime and provide real-time alerts, thereby significantly enhancing citizen security. With this, users are empowered to report criminal incidents directly from their smartphones, eliminating the need to physically approach law enforcement authorities.