IoT Data Analysis for Predicting Bacillus Levels by using Recurrent Neural Network

Abstract

This study explores IoT data analysis for predicting Bacillus levels in turmeric farms, utilizing Recurrent Neural Networks (RNNs) to enhance soil health and crop management. IoT sensors collect real-time soil parameters, including moisture, pH, temperature, and electrical conductivity, which serve as input features for predictive modelling. Initial regression models establish baseline relationships between these parameters and Bacillus levels. Subsequently, RNNs are employed to capture temporal patterns and dependencies in the data, improving prediction accuracy over time. The findings indicate that RNNs significantly outperform traditional machine learning approaches, particularly in scenarios with limited data. This research highlights the potential of IoT and advanced deep learning techniques in automating Bacillus monitoring and enhancing crop health management in turmeric farming.

Introduction

The adoption of Internet of Things (IoT) in agriculture is revolutionizing farming practices. This study focuses on using IoT sensors and Recurrent Neural Networks (RNNs) to predict Bacillus levels—beneficial microorganisms essential for soil health and turmeric crop success. Traditional monitoring methods are labor-intensive and slow; this system offers a real-time, data-driven solution to improve soil and crop management.

Goals

• Develop an IoT-powered system to monitor key soil parameters (moisture, pH, temperature, conductivity).

• Use RNNs to model and predict Bacillus levels based on temporal environmental data.

• Provide farmers with timely, actionable insights to improve sustainability and crop yields.

Literature Insights

• Bacillus species enhance nutrient availability and disease resistance.

• Prior work (e.g., Lin et al., 2024) demonstrates IoT's potential in soil monitoring.

• RNNs outperform traditional models by capturing temporal patterns critical for Bacillus prediction.

Software Requirements

• Languages: Python (main), R (statistical analysis)

• Libraries: TensorFlow/PyTorch (RNNs), Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn

• Tools: PyCharm, Jupyter, Flask/FastAPI (web server), Docker (deployment), Git (version control)

• Data Storage: SQLite, PostgreSQL, optional cloud services (AWS/GCP)

• Communication: MQTT/HTTP

• Visualization & Monitoring: Grafana, web UI (HTML/CSS/JS/React)

Key Features

? Advantages

• Real-time monitoring of soil health

• Accurate predictions of Bacillus levels

• Optimized crop management based on data

• Reduced resource waste and cost

?? Limitations

• Accuracy depends on data quality and volume

• High initial cost and need for technical training

• Connectivity issues in rural areas

Applications

• Soil health tracking to maintain ideal conditions for turmeric

• Informed fertilization and pest control

• Precision agriculture for better efficiency

• Sustainable farming with smarter resource use

Conclusion

A. Conclusion The integration of IoT technology and machine learning for predicting Bacillus levels in turmeric farms represents a significant advancement in agricultural practices. By leveraging real-time data from soil sensors and employing Recurrent Neural Networks for analysis, farmers can gain valuable insights into soil health. This approach not only enhances crop management but also promotes sustainable farming practices by optimizing resource use and reducing waste. The ability to make data-driven decisions empowers farmers to improve yields and maintain soil quality, ultimately contributing to the long-term viability of turmeric cultivation. B. Future Work Looking ahead, there are several avenues for further development. Expanding the dataset to include more diverse environmental conditions and geographical locations will enhance the model\'s robustness and accuracy. Additionally, integrating more advanced machine learning techniques, such as ensemble methods, could improve prediction capabilities. Another important area for future work is the development of a user-friendly mobile application that allows farmers to access real-time data and insights on-the-go. This would facilitate quicker decision-making and enhance user engagement with the technology. Finally, exploring the integration of other soil health indicators and pest monitoring systems could provide a more comprehensive approach to precision agriculture, further supporting sustainable practices in turmeric farming and beyond.

References

[1] G. Patrizi, A. Bartolini, L. Ciani, V. Gallo, P. Sommella and M. Carrat` u, ”A Virtual Soil Moisture Sensor for Smart Farming Using Deep Learning,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022, Art no. 2515411, doi: 10.1109/TIM.2022.3196446. [2] A. Reyana, S. Kautish, P. M. S. Karthik, I. A. Al-Baltah, M. B. Jasser and A. W. Mohamed, ”Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification,” IEEE Access, vol. 11, pp. 20795-20805, 2023, doi: 10.1109/ACCESS.2023.3249205. [3] W. Rahman et al., ”Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain,” IEEE Transactions on Artificial Intelligence, vol. 5, no. 9, pp. 4787-4798, Sept. 2024, doi: 10.1109/TAI.2024.3394799. [4] E. Elbasi et al., ”Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 171-202, 2023, doi: 10.1109/ACCESS.2022.3232485. [5] S. Dattatreya, A. N. Khan, K. Jena and G. Chatterjee, ”Conventional to Modern Methods of Soil NPK Sensing: A Review,” IEEE Sensors Journal, vol. 24, no. 3, pp. 2367-2380, 1 Feb. 2024, doi: 10.1109/JSEN.2023.3334243. [6] D. G. Pai, R. Kamath and M. Balachandra, ”Deep Learning Techniques for Weed Detection in Agricultural Environments: A Comprehensive Review,” IEEE Access, vol. 12, pp. 113193-113214, 2024, doi: 10.1109/ACCESS.2024.3418454.

Copyright

Copyright © 2025 Babarao Kalyankar, Chetan Pawar, Shubham Sawant, Ajinkya Kalange, Archana Kale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.