Predict indoor environment of greenhouses for automatic greenhouse environmental control using machine learning techniques

2020 ◽  
Author(s):  
Chia-Hui Hsu ◽  
Angela Huang ◽  
Fi-John Chang
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Crop Production ◽  
Indoor Environment ◽  
Environmental Control ◽  
Machine Learning Techniques ◽  
Control Mechanisms ◽  
Estimation Model ◽  
Food Energy ◽  
Greenhouse Environmental Control

<p>Maintaining stable crop production is the main benefit of greenhouses, which, however, would consume additional resources to control the indoor environment, as compared to open field cultivation. In consideration of Water-Food-Energy Nexus (WFE Nexus) management, it’s important to build an integrated methodology to estimate and optimize the crop production and resources consumption of greenhouses. Since the crop production of greenhouses is predictable if the indoor environment is well controlled, the main thing we should consider is how to reduce the water and energy consumption as much as possible during the environmental control process for greenhouses. For this purpose, we first build a machine learning-based model to predict indoor environment, including air temperature, relative humidity (RH), and soil water content, for a greenhouse that grows crops. Then according to the suitability criteria of the crop, the predicted values are utilized for environmental control if the values violate the criteria. Under such circumstance, an estimation model is established to determine which type and level of control mechanisms upon water and energy should be activated for meeting the suitability criteria to maintain stable crop production. The study area is a cherry tomato greenhouse located at the farm in Changhua County, Taiwan, where a total of 44,310 datasets were recorded by Internet of Things (IoT) from 2018 to 2019 at a 10-minute temporal resolution. This study also evaluates the efficiency of greenhouses under different scenarios of climatic conditions. The results are expected to contribute to the automatic greenhouse environmental control for stimulating the synergies of the WEF Nexus management toward sustainable development.</p><p>Keywords: Water-Food-Energy Nexus (WFE Nexus); Greenhouse; Machine learning; Internet of Things (IoT)</p>

scholarly journals Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-3 ◽  
Author(s):  
David Gil ◽  
Magnus Johnsson ◽  
Higinio Mora ◽  
Julian Szymanski
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1578 ◽  
Author(s):  
Shun-Nien Yang ◽  
Li-Chiu Chang
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Forecast Model ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Natural Environments ◽  
Dynamic Feature ◽  
Flood Inundation ◽  
Flood Depth ◽  
Inundation Depth

Natural disasters have tended to increase and become more severe over the last decades. A preparation measure to cope with future floods is flood forecasting in each particular area for warning involved persons and resulting in the reduction of damage. Machine learning (ML) techniques have a great capability to model the nonlinear dynamic feature in hydrological processes, such as flood forecasts. Internet of Things (IoT) sensors are useful for carrying out the monitoring of natural environments. This study proposes a machine learning-based flood forecast model to predict average regional flood inundation depth in the Erren River basin in south Taiwan and to input the IoT sensor data into the ML model as input factors so that the model can be continuously revised and the forecasts can be closer to the current situation. The results show that adding IoT sensor data as input factors can reduce the model error, especially for those of high-flood-depth conditions, where their underestimations are significantly mitigated. Thus, the ML model can be on-line adjusted, and its forecasts can be visually assessed by using the IoT sensors’ inundation levels, so that the model’s accuracy and applicability in multi-step-ahead flood inundation forecasts are promoted.

Machine Learning for Internet of Things

2019 ◽  
pp. 57-83 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

Sign in / Sign up

Export Citation Format

Share Document