Knowledge discovery and machinelearning for capacity optimizationof Automatic Milking RotarySystem

Sammanfattning: Dairy farming as one part of agriculture has thousands of year’s history. The increasingdemands of dairy products and the rapid development of technology bring dairyfarming tremendous changes. Started by first hand milking, dairy farming goes throughvacuum bucket milking, pipeline milking, and now parlors milking. The automatic andtechnical milking system provided farmer with high-efficiency milking, effective herdmanagement and above all booming income.DeLaval Automatic Milking Rotary (AMRTM) is the world’s leading automatic milkingrotary system. It presents an ultimate combination of technology and machinerywhich brings dairy farming with significant benefits. AMRTM technical milking capacityis 90 cows per hour. However, constrained by farm management, cow’s condition andsystem configuration, the actual capacity is lower than technical value. In this thesis, anoptimization system is designed to analyze and improve AMRTM performance. The researchis focusing on cow behavior and AMRTM robot timeout. Through applying knowledgediscover from database (KDD), building machine learning cow behavior predictionsystem and developing modeling methods for system simulation, the optimizing solutionsare proposed and validated.