In today’s fast-paced business environment, data-driven decision-making is no longer a luxury—it’s a necessity. However, businesses often face a critical challenge: how can a third party prove the value of their proprietary data and models without exposing sensitive information? This article explores a robust methodology that balances data privacy with transparency, enabling businesses to gain valuable insights while maintaining trust and confidence.

Overview:
Project Room4All NUOC is a transformative initiative at the intersection of AI, IoT, and vertical farming. It aims to showcase the potential of sovereign blockchain and web3 technologies in addressing real-world challenges in Southeast Asia, particularly through precision agriculture. With an emphasis on community-driven participation and open-source development, the project serves as both a proof of concept (POC) and a blueprint for sustainable innovation.

Hydroponic farming requires precise monitoring and control, often facilitated by IoT devices like Raspberry Pis. Combining data from multiple Pis into a cohesive dataset stored in a database like MongoDB is crucial for effective analysis and machine learning applications.

Measuring various features such as temperature, humidity, light intensity (E), pH levels, carbon dioxide concentration (CO2), plant height, and plant health at frequent intervals using supervised machine learning, particularly logistic regression, can indeed provide insights into predicting successful lettuce leafy green harvests. However, the accuracy of such predictions may vary depending on several factors, including the quality and quantity of data, feature selection, and model complexity.

To detect plant diseases efficiently, incorporating data collection, sensors, and machine learning can significantly enhance the detection process. Here's an outline of an optimal approach:

The pilot program consists of two critical phases: IoT/sensors/datalogging and AI/ML. To achieve seamless integration, both teams must work collaboratively. The key to success lies in proper design, accurate monitoring, and triggering, with comprehensive datalogging of all relevant data points.

A Data-Driven Approach to Vertical Farming
In the world of farming, new methods are constantly being explored to meet the increasing demand for food. One promising approach is the use of data-driven techniques in vertical farming, where crops are grown in controlled indoor environments.

We believe that the success of this project hinges on a multidisciplinary approach and welcome collaboration with domain experts and we welcome all to join us playing a vital role in bringing our innovative ideas to life and contributing to the growth of the start-up community in the region.

There are several excellent open-source project management tools available for managing project timelines, assigning tasks, and collaborating with team members. Here are a few popular options:

The scenario described and discussed in Part1 of this article, where machine learning models are used to predict the success of a harvest in vertical farming based on various environmental and plant-related data, falls under the broader umbrella of Artificial Intelligence (AI).

Our dedication lies in harnessing the possibilities offered by web3 and blockchain technologies within industries on the brink of transformation in this rapidly evolving Digital Age. Currently, our primary emphasis is on forming partnerships with key stakeholders to conceptualize and implement an innovative vertical farming initiative in Southeast Asia.