|SHAMSHIRI, REDMOND - Leibniz Institute|
|HAMEED, IBRAHIM - Norwegian University|
|BALASUNDRAM, SIVA - Universiti Putra Malaysia|
|SHAFIAN, SANAZ - Virginia Polytechnic Institution & State University|
|FATEMIEH, MOHAMMAD - Adaptive Agrotech|
|SULTAN, MUHAMMAD - Bahauddin Zakariya University|
|MAHNS, BENJAMIN - Leibniz Institute|
|SAMIEI, SABA - Auckland University Of Technology|
Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 4/13/2021
Publication Date: 5/10/2021
Citation: Shamshiri, R.R., Hameed, I.A., Thorp, K.R., Balasundram, S.K., Shafian, S., Fatemieh, M., Sultan, M., Mahns, B., Samiei, S. 2021. Greenhouse automation using wireless sensors and IoT instruments integrated with artificial intelligence. Book Chapter. https://doi.org/10.5772/intechopen.97714.
Interpretive Summary: Sustainable production of fruits and vegetables in greenhouse environments with reduced energy inputs entails proper integration of existing climate control systems with IoT automation to incorporate real-time data transfer from sensors to algorithms and crop growth models using cloud-based streaming. This book chapter provides an overview of such an automation workflow in a greenhouse environment. The information benefits researchers and practitioners in controlled environment agriculture.
Technical Abstract: Automation of greenhouse environment using simple timer-based actuators or by means of conventional control algorithms that require feedbacks from offline sensors for switching devices are not efficient solutions in large-scale modern greenhouses. Wireless instruments that are integrated with artificial intelligence (AI) algorithms and knowledge-based decision support systems have attracted growers’ attention due to their implementation flexibility, contribution to energy reduction, and yield predictability. Sustainable production of fruits and vegetables under greenhouse environments with reduced energy inputs entails proper integration of the existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems. This chapter provides an overview of such an automation workflow in greenhouse environments by means of distributed wireless nodes that are custom-designed based on the powerful dualcore 32-bit microcontroller with LoRa modulation at 868 MHz. Sample results from commercial and research greenhouse experiments with the IoT hardware and software have been provided to show connection stability, robustness, and reliability. The presented setup allows deployment of AI on embedded hardware units such as CPUs and GPUs, or on cloud-based streaming systems that collect precise measurements from multiple sensors in different locations inside greenhouse environments.