Simplified Fuzzy Logic Expert System for Prototype Vertical Farm Environment Control

Chapter 2 features a narrow focus on the scope of previous researches done in ES using fuzzy logic. This chapter is inclusive of terminology definition, previous work and identifies the gap between current research and vertical farm concept.
2.1 Terminologies Definition
Hydroponic system is simply growing plants in a liquid nutrient solution with or without the use of artificial media such as clay balls, perlite or coconut coir (Shrestha A. & Dunn B., n.d). Aeroponics is a type of hydroponic system where roots of the plant is fully exposed in air – without clinging on any artificial media and water is sprayed to these roots in an enclosed area (Oxford Dictionaries‎). Geoponic on the other hand, is the typical soil-based planting that is commonly used in traditional farming (Merriam-Webster). Table 3 shows the comparison between the three systems used in farming.
Soil vs Hydroponic vs Aeroponic Geoponic Hydroponic Aeroponic
Uses soil Yes No No
Uses other medium to hold plants (coconut husk, rock wool, gravels) No Yes No
Expose to soil disease (crown gall, take-all disease of wheat, etc) Yes No No
Crop Yield Fair High High
Weeding or cultivation Yes No No
Lifting crops from ground (Lettuce, Strawberries) Hard Easy Easy
Crop Rotation (Maintain fertility) Yes No No
Control in PH, nutrient and growing environment Difficult Easy Easy
Growth Rate of Plant Slow Fast Fastest
Recycling of nutrient X Possible Possible
Initial & Operational Cost Low High Highest
Skill and Expertise Low Moderate High
Table 3: Comparison of Hydroponic, Aeroponic and Geoponic
*Adapted from: http://osufacts.okstate.edu/docushare/dsweb/Get/Document-6839/HLA-6442web.pdf, http://jpkc.scau.edu.cn/soilless/papers/upl...

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... is required to further identified and tackle obstacles and issues that would surfaced.
8 (2004) Making a learning robot that can explore and identify new obstacle and overcoming them using multi-objective Self-Exploration process based Intelligent Control System—mSEICS (Chen, L.-H., & Chiang, C.-H., 2004) Not mentioned Fuzzy neural network, multi-objective genetic algorithm mSEICS allows robots to learn and improvise without the need to hard-code solution for them. Although the field are robotics appear irrelevant to the expert system field of agriculture, such learning capability is vital to help learn the optimal growing conditions for a plant without relying solely on experts where the system will improvise and optimizing growth condition better than before. Can extend mSEICS in expert system in the field of agriculture such as greenhouses and vertical farming.