Greenhouse Protection Against Frost Conditions in Smart Farming using IoT Enabled Artificial Neural Networks

An Artificial Intelligence and IoT incorporated frost forecasting is proposed in this novel work. The objects present inside a greenhouse are connected to each other through Internet of Things (IoT), using devices such as actuators, sensors and assisting aids. A smart system incorporating IoT is designed, developed and implemented using Fuzzy associative memory and Artificial Neural Networks (ANN) in order to manage any ill effects in irrigation caused due to frost conditions. The temperature inside the green house is monitored continuously on comparison with the outside temperature, thereby steps are taken to stabilize the temperature to make it suitable for plant growth. The temperature inside the greenhouses are forecasted by means of ANN and using fuzzy control, temperature of the crops are predicted and watered as per the required using 5 levels of water pump output. The output obtained is analyzed and compared with similar Fourierstatistical method and it is found that the proposed methodology provides a more effective prediction of temperature.

[1]  Manuel Toledano-Ayala,et al.  Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development , 2020, Applied Sciences.

[2]  D. Ruth Anita Shirley,et al.  Automatic Distributed Gardening System Using Object Recognition and Visual Servoing , 2020 .

[3]  T Vijayakumar,et al.  NEURAL NETWORK ANALYSIS FOR TUMOR INVESTIGATION AND CANCER PREDICTION , 2019, December 2019.

[4]  Jennifer S. Raj Dr,et al.  A COMPREHENSIVE SURVEY ON THE COMPUTATIONAL INTELLIGENCE TECHNIQUES AND ITS APPLICATIONS , 2019 .

[5]  V. Singh,et al.  PREDICTION OF GREENHOUSE MICRO-CLIMATE USING ARTIFICIAL NEURAL NETWORK , 2017 .

[6]  Abbas Rohani,et al.  Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse , 2016 .

[7]  Hamid Taghavifar,et al.  Prognostication of energy consumption and greenhouse gas (GHG) emissions analysis of apple production in West Azarbayjan of Iran using Artificial Neural Network , 2015 .

[8]  Mahmoud Omid,et al.  Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks , 2013 .

[9]  G. Mudd,et al.  Future Greenhouse Gas Emissions from Copper Mining: Assessing Clean Energy Scenarios , 2012 .

[10]  Chengwei Ma,et al.  Modeling greenhouse air humidity by means of artificial neural network and principal component analysis , 2010 .

[11]  J. Ríos-Moreno,et al.  Greenhouse energy consumption prediction using neural networks models , 2009 .

[12]  A. Rojano,et al.  A Neural Network Model to Control Greenhouse Environment , 2007, 2007 Sixth Mexican International Conference on Artificial Intelligence, Special Session (MICAI).

[13]  R. Zakaluk,et al.  Artificial Neural Network Modelling of Leaf Water Potential for Potatoes Using RGB Digital Images: A Greenhouse Study , 2007, Potato Research.