دانلود گزارش کارورزی و کار آموزی

چکیده : 

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%.