153 Creation of a feed composition database: Machine learning techniques for automated classification of corn grain products, preliminary results

Abstract

Abstract Feed composition tables are a commonly used to develop research projects and to develop animal diets. Currently, the National Animal Nutrition Program aims to create a living database containing feed composition information using large datasets provided by commercial laboratories. Using large datasets should ensure representative nutritional values for feeds included in the database; however, managing large datasets requires computer codes to manage and classify feeds correctly. Thus, the objective of this project was to develop 2 models based on supervised machine learning techniques for automated classification of corn grain product samples. The database used in the study contained 88,057 samples of corn grain products resulting from the screening procedure previously described by Tran et al (2016). Two types of supervised machine learning models were developed: decision tree and random forest. Parameters included for feed classification were: dry matter, crude protein, neutral detergent fiber, ash, fat, and starch. Models were trained and validated using 70 and 30% of the dataset, respectively. The decision tree and random forest correctly classified 98.3 and 98.8% of validation dataset, respectively. For each corn grain product the performance of the decision tree and random forest were: corn germ = 91 and 91%; corn germ meal = 97 and 95%; corn gluten feed, dry = 99 and 100%; corn gluten feed, wet = 100 and 100%; corn gluten meal = 99 and 100%; corn grain, dry = 99 and 99%; corn grain, high moisture = 100 and 100%; corn grain, steam-flaked = 34 and 53%; corn hominy feed = 83 and 88%; and corn screenings = 44 and 60%, respectively. In conclusion, the random forest was superior to the decision tree approach for classifying corn grain products. Further development is required to improve the performance of models for classifying corn grain steam-flaked and corn screenings