The vegetable industry is a fundamental part of Malaysia's agricultural and economic landscape, providing essential food supplies, creating employment opportunities, and contributing to economic growth. However, recent challenges, such as labor shortages in the agricultural sector, have limited the capacity of Malaysian vegetable businesses to meet increasing market demand. This study proposes the Smart Vertical Farming System (SVFS), utilizing the Internet of Things (IoT) as part of a broader 'green revolution' focused on applying advanced Information and Communication Technologies (ICT) in agriculture. Using a waterfall development framework and a qualitative research methodology, the SVFS was designed to monitor critical agricultural parameters such as soil moisture, light exposure, and ambient temperature. Initial implementation involved cultivating two different vegetable varieties alongside traditional farming methods. Remarkably, after 19 days, the results showed that vegetables grown using the SVFS experienced faster growth rates and increased leaf production compared to those cultivated conventionally. These findings suggest that integrating innovative farming technologies can significantly improve vegetable quality and yield, while also stabilizing market supply to meet consumer needs at competitive prices. This research demonstrates the potential for technological advancements to address pressing issues within the agricultural sector and enhance overall food security in Malaysia.

Agriculture is one of the most significant industries in the world. It feeds over one billion individuals and produces over $1.3 trillion in food annually. Pastures and crops cover more than half of habitable land, supporting diverse animal life with food and habitat. Sustainable agricultural practices can help maintain and restore key ecosystems, promote sustainable growth, protect watersheds, and improve soil and water quality. Agriculture's deep connections to the global economy, human communities, and biodiversity make it one of the most critical conservation frontiers on the planet. This project aims to develop agricultural innovations using machine learning to perform quality inspections of fruit, enabling users to detect defects reliably and effectively. Machine learning can analyze vast amounts of data to identify trends and patterns that humans might overlook. In this project, a dataset containing images of both fresh and rotten fruits will be used to evaluate their quality. The assessment will focus on key visual attributes such as color, texture, size, shape, and the presence of defects. Consequently, the system is designed to deliver accurate fruit quality inspections with minimal reliance on human expertise or prior knowledge of fruit quality.