Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedlings

• We evaluate feature selection efficacy for pot plant seedling classification.
• Performances of 4 feature selection techniques are compared using 8 classifiers.
• Best configuration for seedling classification had up to 7.4% accuracy gain.
• With feature selection, an average of 11 features were used (out of 26 features).
• Our process can be generalized to many other agricultural products.

Homogeneity plays an important role in ornamental plant and flower production. As assessing the quality of seedlings is an effective way of predicting plant growth performance, a vision system capable of performing this task is desirable. Yet, the optical sorting of agricultural products must find ways to incorporate knowledge from human experts into the computational solution. Our aim is evaluating feature selection techniques with respect to the performance of vision-based inspection and classification of pot plant seedlings. A large feature set was initially obtained from seedlings images and several subsets were generated with various features selection techniques. The performance of each subset was compared to some of the most popular classifiers in the literature: Naive Bayes, k-Nearest Neighbors, Logistic Regression, C4.5, Random Forest, Multilayer Perceptron as well as Partial Least Squares and Support Vector Machine Discriminant Analysis. The best classifier and subset configuration is presented; our results show that feature selection was indeed advantageous, generating accuracy gains of up to 7.4%.