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Final poster of sensoring real-world enviornment and predicting using Machine Learning.

No matter how developed technologies may become, humans need to consume food and convert into energy. Autotrophs, usually are plants, take inorganic compounds and convert into organics which then could be digested by animals. Growing a feeble seed into a mature plant has always been carefully manually processed which is time consuming for large quantities. A sole seed takes approximate 7 days to germination, and the germination rate is difficult to control. In addition, different species require distinct environment even an expert could not predict the germination rate. Fully automation could not only help to reduce the resources cost but increase the efficiency as well. First, it is much more precise on each environmental condition, thus making sensitive changes more quickly. Then, with precise adjustment, reducing chemical waste and energy lost which means the cost would be decreased. Among developing countries, starvation is still a daily problem that needs to be considered.

Paper: As for paper, I will be mainly concentrate on the development of software frame and the interaction between the agent and the environemnt. Since this project is heavily depend on using machine learning to make rational decisions, applying algorithms to analyze data is essential. In addition, precise sensors could collect data which then should be labbled and pass on the the agent.

Software: Althogh this is a project involving both software and hardware components, using certain algorithms (such as Bayesian networks) to process collected data and make rational decision is critical. Upon that, by analyzing data from the past, a future prediction could be made for productivity and cost.