A Machine Learning Approach for Peanut Classification in Python

Abstract:

The automation level of testing peanut kernels' quality is low because of workers fatigue, and most of the work is done by manpower leads costly. The peanuts are evaluated in many areas for sowing and oilseed processing; they must be identified quickly and accurately for selection of a correct variety and kernels' quality. The proposed testing method based on image processing and computer vision is a new one which is undamaged, speedy with high distinguishing rate, repeatability and low cost and fatigue. In this paper, machinelearning classifiers (Multilayer Perceptron, Simple Logistic, Support Vector Machines, and Sequential Minimal Optimization and Logistic classifiers) are investigated to obtain the best predictive model for peanuts classification. The training and test sets are used to tune the model parameters during the training epochs by varying the complexity of the predictive models with K-fold cross-validation. After obtaining optimized models for each level of complexity, a dedicated validation set is used to validate predictive models. The developed computer vision system provided an overall accuracy rate for the best predictive model in discriminating peanuts variety are Random Forest (82.27%), Multilayer Perceptron(84.9%), and libSVM (86.07%).