Leaf classification in sunflower crops by computer vision and neural networks

TitleLeaf classification in sunflower crops by computer vision and neural networks
Publication TypeJournal Article
Year of Publication2011
AuthorsArribas, J. I., G. V. Sanchez-Ferrero, G. Ruiz-Ruiz, and J. Gomez-Gil
JournalComputers and Electronics in Agriculture
Keywordsaccuracy assessment, agricultural technology, algorithm, artificial neural network, automation, Classification rates, Computer vision, Crops, dicotyledon, Discriminative features, experimental study, Generalized softmax perceptron, Helianthus, herbicide, Herbicide application, Herbicides, Image classification, Image classification systems, Leaf classification, Learning machines, Model selection, Network architecture, Neural networks, Posterior probability, RGB color space, segmentation, Segmented images, Sunflower, Test sets

In this article, we present an automatic leaves image classification system for sunflower crops using neural networks, which could be used in selective herbicide applications. The system is comprised of four main stages. First, a segmentation based on rgb color space is performed. Second, many different features are detected and then extracted from the segmented image. Third, the most discriminable set of features are selected. Finally, the Generalized Softmax Perceptron (GSP) neural network architecture is used in conjunction with the recently proposed Posterior Probability Model Selection (PPMS) algorithm for complexity selection in order to select the leaves in an image and then classify them either as sunflower or non-sunflower. The experimental results show that the proposed system achieves a high level of accuracy with only five selected discriminative features obtaining an average Correct Classification Rate of 85% and an area under the receiver operation curve over 90%, for the test set. © 2011 Elsevier B.V.