Agriculture is one of the economic activities that more require the presence human being in the decision taking. Innumerable are the processes that require some type of human being interference in the conclusion of the processes. Fruit Sorting depends on human or artificial pattern recognition according to some pre defined categories. Once a fruit pattern is under classification, this one must be compared to some other ones stored. After that comparison it can be classified. Most sorting fruits jobs are human basis classification. This paper shows that using neural networks is possible to develop capable models of storing fruit pattern vectors. Given any fruit pattern vector to the model it can classify to the closest fruit pattern vector stored. The number of patterns were incremented and presented to the neural networks, classifying the presented fruits and proved the scalability of number of vector components used in fruit pattern vectors stored in the model. This work was developed in the Agricultural Machinery Department of the Agricultural Faculty in State University of Campinas, the neural networks stored fruit pattern vectors such as Weight, Diameter. These vectors components associated itself interacted determining an output pattern vector classifying according to the stored fruit vector patterns. A Multi Layer Perceptron Network with Backpropagation algorithm was used, storing the relationship between input fruit pattern vectors and output classification class vectors. The neural network was trained and tested presenting the desired results, it can be used as a tool for future fruit classification processes.
Artificial Neural Networks; back-propagation; classifiers; sorting
