Machine Learning-Based Basil Yield Prediction in IoT-Enabled Indoor Vertical Hydroponic Farms

TL;DR

This study integrates IoT sensors and machine learning models to predict basil yield in indoor hydroponic farms, optimizing water use and providing insights into model performance trade-offs for resource-constrained environments.

Contribution

It develops and compares ML models for basil yield prediction in IoT-enabled hydroponic farms, highlighting DNN's high accuracy and LR's speed for practical deployment.

Findings

LSTM achieved 99% accuracy but was slower and more resource-intensive.

DNN provided 98% accuracy with a good balance of speed and resource use.

LR was the fastest, suitable for low-resource applications.

Abstract

As agriculture faces increasing pressure from water scarcity, especially in regions like Tunisia, innovative, resource-efficient solutions are urgently needed. This work explores the integration of indoor vertical hydroponics with Machine Learning (ML) techniques to optimize basil yield while saving water. This research develops a prediction system that uses different ML models and assesses their performance. The models were systematically trained and tested using data collected from IoT sensors of various environmental parameters like CO2, light. The experimental setup features 21 basil crops and uses Raspberry Pi and Arduino. 10k data points were collected and used to train and evaluate three ML models: Linear Regression (LR), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN). The comparative analysis of the performance of each model revealed that, while LSTM showed high predictive capability and accuracy of 99%, its execution time was 10 times longer than LR and its RAM usage was about 3 times higher than DNN's when simulated on a standard CPU environment. Conversely, the DNN model had an accuracy rate of 98%. This proves an efficient balance between computational speed and prediction quality, which makes this model well-suited for real-life deployment. Moreover, LR excelled in fast processing of basic prediction with an execution time of 11 seconds. This makes the LR model more suitable for low-complexity or resource-limited applications. These performance trade-offs highlight the potential of DNN-based solutions for building responsive, high-accuracy decision-support systems tailored to agricultural environments, making it suitable for future edge-device deployment.