final project report

Final_evaluation.md

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 # Final Evaluation
 
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 ## Introduction
+PotatoPlan is an Inteligent Tractor AI Project and is written in C# using Monogame framework.
+NuGet Packages used and requeired for the project to work ar as follows:
+	C5
+	Microsoft.ML
+	Microsoft.ML.LightGBM
+	System.Drawing.Common
+	
+In our project agent (tractor) moves on resizable grid, which starting size is dependant on primary screen resolution.
+The task of the agent is to go through all soil tiles and plant different types of crops, use proper fertilizer and collect crops when fully grown.
+Apart from Machine Learning Algorithms used in project there are also many different features implemented like:
+	Using A* algorithm to find an optimal path to previously selected target.
+	Target is found by scoring system which assign score to a tile based on few factors like production rate or distance.
+	Dynamically allocated cargo space for each fertilizer based on how often each fertilizer is used.
+	Day and night cycle and season system.
+	Using noise map generated for rainfall calculations to draw moving clouds.
+	... and few other.
+
+## Machine Learning Algorithms
+Project in its current state uses Machine Learning Algorithms to solve 2 problems. Light Gradient-Boosted Trees are used for both problems:
+	Choosing a proper fertilizer which should be applied to current tile, based on few variables like nutrients in soil. Applying proper fertilizer boosts production rate of a crop (rate of growth of a crop). This part was done by Oskar Nastały.
+	Calculating production rate of a tile based on rainfall and few other variabels. Noise map is generated and used to simulate dynamically changing rainfall. Then once a day AI is used to calculate base production rate multiplier. This part was done by Joel Städe.
+	
+## Examples
+![Clouds]()
+
+![UI]()
 
 Final evaluation doc.
 WIP