Data Framework for LED Optimization in Greenhouses and Vertical Farming

Lumesmart Inc. seeks to enhance its LED lighting solutions by leveraging AI to simulate performance across diverse greenhouse and vertical farming environments. The goal of this project is to design a comprehensive data collection and analysis framework that will serve as the foundational training input for an AI tool. This AI tool will be capable of simulating how different LED lighting solutions perform under various agronomic and operational conditions, ultimately generating ROI-focused recommendations for growers. Students will be tasked with identifying key data points necessary for accurate and actionable AI simulations. These data points may include environmental factors, crop types, growth stages, and energy consumption metrics. By applying classroom knowledge in data analysis and AI, students will contribute to creating a robust framework that supports Lumesmart Inc.'s mission to optimize LED lighting solutions for sustainable agriculture. Key Responsibilities Identify and document key data inputs (e.g., crop type, growth stage, planting density, fixture height, light spectrum, electricity rates). Recommend additional agronomic and environmental variables to improve AI-driven simulations. Develop a grower-facing input form to standardize data collection. Propose a mapping framework linking each input to outputs (fixture selection, energy use, yield, ROI). Design a reporting structure for how results can be automated into grower-friendly ROI reports once AI is integrated.

UX/UI Design and User Experience Optimization

The objective of this project is to enhance the user interface and overall user experience of our platform. Students will analyze current design challenges, conduct user testing, and deliver wireframes, prototypes, and recommendations to improve accessibility, navigation, and visual consistency.

Cleantech Database Design and Implementation

The goal of this project is to design and implement a scalable database that can store, organize, and retrieve public cleantech company data. Students will practice skills in database design, coding (SQL/NoSQL), and query optimization. By the end, the project will deliver a functional database prototype with documentation, preparing the foundation for future integration with AI and CRM systems.

Sustainable Branding Strategy for Global Cleantech Directory

The primary objective of this project is to integrate sustainable branding and consumer engagement practices into the Global Cleantech Directory’s marketing strategy. The project aims to strengthen the platform’s identity as a trusted leader in cleantech by embedding environmental principles, fostering responsible consumption, and enhancing global visibility. Students will analyze consumer trends, explore innovative communication methods, and develop strategies that connect ecological responsibility with brand trust, while aligning with the Directory’s mission to harmonize technology, humanity, and nature.