A data-driven approach to vertical farming

15 January 2024 Written by 
Published in Project Room4All NUOC

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.

Stage 1: Automating farming with IoT
Our journey begins with a focus on automation through the Internet of Things (IoT). We use small computers called microcontrollers, along with sensors and actuators. These devices help us automate tasks like watering and controlling the farm's environment.

By connecting sensors that measure things like temperature, humidity, and light to microcontrollers, we can monitor and adjust conditions for the plants. Computer code makes this possible, and rigorous testing ensures that the automation works correctly.

Stage 2: Gathering and logging data
In the second stage, we concentrate on collecting and storing data from the farm's environment. The same sensors from Stage 1 play a critical role in this phase. They continuously collect data on temperature, humidity, and light levels, which we carefully record.

Data logging is essential because it helps us track changes and conditions over time. The logged data is formatted and organized for easy analysis. We store it in databases or files, ready to be used for decision-making.

Stage 3: Creating a data-driven model
Now we move to the final stage, Stage 3. Here, we employ computer programming and machine learning to make sense of the data we've collected. We use a programming language called Python and machine learning techniques to create a model.

This model learns from the data we've collected and can predict outcomes, helping us understand our farming processes better. We train, evaluate, and refine the model to improve its accuracy and usefulness over time.

Conclusion: Advancements in vertical farming
Our journey is poised for a new phase - the implementation of these data-driven techniques in a pilot program. As we look forward to proving the benefits of IoT automation, data logging, and machine learning in real-world vertical farming, we anticipate a future where technology enhances our ability to meet the growing demand for food more efficiently.

In this pilot program, we aim to demonstrate how technology can improve farming practices, leading to more sustainable and productive agriculture. We're excited to embark on this journey and validate the potential of data-driven methods in vertical farming.

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