@book {676, title = {Sunflowers: growth and development, environmental influences and pests/diseases.}, year = {2014}, pages = {323}, publisher = {Nova Science Publishers}, organization = {Nova Science Publishers}, address = {New York}, abstract = {

We are all well aware that the importance of the sunflower (Helianthus Annus) as a crop has increased significantly in recent years, not only in the food industry but also as a natural energy resource in oil production. I am, thus, very pleased to be able to present this comprehensive monograph on a wide range of important issues regarding sunflowers, with an emphasis on environmental influences, pests and diseases in order to maximise production whilst minimising costs.

Contributors where selected based on their proven experience in the field of sunflowers. Contributors submitted an extended abstract that was assessed for relevance. They were then invited to contribute draft chapters. Each chapter underwent a stringent and thorough peer review process by other experts in the field, with final approval by the editor who, thus, was able to balance the topics from all contributors.

The book contains important original results. Each chapter deals with a different topic, and draws, where appropriate, from studies and results previously published by the authors. Authors were encouraged to complement their writing with original and high quality graphs, charts, tables, figures, pictures and photographs.

It{\textquoteright}s my honour and pleasure to acknowledge the rigorous work carried out by all authors in this book, and at the same time I am very grateful to them for trusting me in leading this project in the role of the editor of their work. My thanks also go to the anonymous reviewers who contributed their time so generously to this book, and without whom it would not exist.

I am also very grateful to Nova Science Pubs. for inviting me to lead this book, and thank them for the help and coverage provided during the whole time that this project lasted.

I really do hope that you find this book of interest and wish you enjoy its reading as much as I have done through the whole editing process and as much I am sure all authors have done while writing it.

}, keywords = {Leaf classification, Sunflower, desease, environmental, pest}, isbn = {978-1-63117.348-6}, doi = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84948981604\&origin=resultslist\&sort=plf-f\&src=s\&sid=6fdffa7042d279955cdde5960c4dc452\&sot=autdocs\&sdt=autdocs\&sl=17\&s=AU-ID\%287103041133\%29\&relpos=4\&citeCnt=0\&searchTerm=}, url = {https://www.amazon.com/Sunflowers-Development-Environmental-Influences-Botanical/dp/1631173472}, author = {J I Arribas} } @article {424, title = {Leaf classification in sunflower crops by computer vision and neural networks}, journal = {Computers and Electronics in Agriculture}, volume = {78}, year = {2011}, pages = {9-18}, abstract = {

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. {\^A}{\textcopyright} 2011 Elsevier B.V.

}, keywords = {Classification rates, Computer vision, Crops, Discriminative features, Generalized softmax perceptron, Helianthus, Herbicide application, Herbicides, Image classification, Image classification systems, Leaf classification, Learning machines, Model selection, Network architecture, Neural networks, Posterior probability, RGB color space, Segmented images, Sunflower, Test sets, accuracy assessment, agricultural technology, algorithm, artificial neural network, automation, dicotyledon, experimental study, herbicide, segmentation}, issn = {01681699}, doi = {10.1016/j.compag.2011.05.007}, url = {https://www.sciencedirect.com/science/article/pii/S0168169911001220}, author = {J I Arribas and G V Sanchez-Ferrero and G Ruiz-Ruiz and Jaime Gomez-Gil} }