Increasing human populations are leading to increasing and sometimes conflicting demands on land and resources. Agriculture, particularly the livestock industry will need to increase productivity using existing or fewer resources to meet demands. A resilient livestock industry will require cost-effective feed rations as feed accounts for between 60 to 80% of production costs. Techniques in computational intelligence were used in coding and fine-tuning algorithms for feed formulation. Simulations were done in MATLAB to evaluate the performance of the algorithms. Hyperspectral imaging and near infrared spectroscopy were used to acquire spectral data from mixed by-products and packaged feed. Preprocessing methods such as second derivative and normalization were applied to the data to remove noise. Data was acquired from a spectral range of 900 to 2100 nm. Prediction tools (Partial least square regression and Unscrambler) were used to build amino acids, sugars, organic acids and moisture content models. This presents rapid, non-destructive measurements with no sample preparation and high sensitivity. All the models had satisfactory R2 values. Subsequently, the developed systems were combined with existing systems to propose models for precision livestock feed production.
