TY - GEN
T1 - ML Based Control in Precision Agriculture
T2 - 5th International Conference on Smart Applications and Data Analysis for Smart Cyber Physical Systems, SADASC 2024
AU - Bermeo, Pither Gabriel Tene
AU - Senouci, Benaoumeur
AU - Copeland, Jacob
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - This paper presents a new approach in controlling fundamental parameters (Light Intensity, Temperature, humidity ... etc.) related to precision agriculture using Machine Learning. We propose and design a new autonomous control system that applies data-driven approach. Several AI prediction models are developed using local data sets. Leveraging the power of Long Short-Term Memory (LSTM) models, the system aims to dynamically adjust light output in response to varying levels of carbon dioxide emissions. The model is deployed in a local server using a hardware architecture based on Raspberry Pi and a ESP32 microcontroller. Pi Server facilitates model deployment and data storage, while ESP32 provides wireless communication and peripherals interface to ensure efficient real-time data sensing and light control. Our results show that our multivariate model, which uses temperature, humidity, and CO2 emissions, provides better accuracy in terms of RMSE. Also, the embedded developed architecture facilitates the real time data sensing, collection and control.
AB - This paper presents a new approach in controlling fundamental parameters (Light Intensity, Temperature, humidity ... etc.) related to precision agriculture using Machine Learning. We propose and design a new autonomous control system that applies data-driven approach. Several AI prediction models are developed using local data sets. Leveraging the power of Long Short-Term Memory (LSTM) models, the system aims to dynamically adjust light output in response to varying levels of carbon dioxide emissions. The model is deployed in a local server using a hardware architecture based on Raspberry Pi and a ESP32 microcontroller. Pi Server facilitates model deployment and data storage, while ESP32 provides wireless communication and peripherals interface to ensure efficient real-time data sensing and light control. Our results show that our multivariate model, which uses temperature, humidity, and CO2 emissions, provides better accuracy in terms of RMSE. Also, the embedded developed architecture facilitates the real time data sensing, collection and control.
KW - Autonomous system
KW - Forecasting
KW - Light control
KW - Long Short-Term Memory
KW - Precision Agriculture
KW - Real-time data sensing
U2 - 10.1007/978-3-031-77043-2_1
DO - 10.1007/978-3-031-77043-2_1
M3 - Article in proceedings
AN - SCOPUS:85214376328
SN - 9783031770425
T3 - Communications in Computer and Information Science
SP - 3
EP - 15
BT - Smart Applications and Data Analysis
A2 - Hamlich, Mohamed
A2 - Moutachaouik, Hicham
A2 - Dornaika, Fadi
A2 - Ordonez, Carlos
A2 - Bellatreche, Ladjel
PB - Springer Science+Business Media
Y2 - 18 April 2024 through 20 April 2024
ER -