@article {408,
	title = {Evaluation of the use of low-cost GPS receivers in the autonomous guidance of agricultural tractors},
	journal = {Spanish Journal of Agricultural Research},
	volume = {9},
	year = {2011},
	pages = {377-388},
	abstract = {<p>This paper evaluates the use of low-cost global positioning system (GPS) receivers in the autonomous guidance of agricultural tractors. An autonomous guidance system was installed in a 6400 John Deere agricultural tractor. A lowcost GPS receiver was used as positioning sensor. Three different control laws were implemented in order to evaluate the autonomous guidance of the tractor with the low-cost receiver. The guidance was experimentally tested with the tracking of straight trajectories and with the step response. The total guidance error was obtained from the receiver accuracy and from the guidance error. For the evaluation of the receiver{\textquoteright}s accuracy, positioning data from several lowcost receivers were recorded and analyzed. For the evaluation of the guidance error, tests were performed with each control law at three different speeds. The conclusions obtained were that relative accuracy of low-cost receivers decreases with the time; that for an interval lower than 15 min, the error usually remains below 1 m; that all the control laws have a similar behavior and it is conditioned by the control law adjustment; that automatic guidance with lowcost receivers is possible with speeds that went up to 9 km h -1; and finally, that the total error in the guidance is mainly determined by the receiver{\textquoteright}s accuracy.</p>
},
	issn = {1695971X},
	doi = {https://doi.org/10.5424/sjar/20110902-088-10},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79959669468\&partnerID=40\&md5=774d42717ec127c9a6c5e25864da9722},
	author = {Sergio Alonso-Garcia and Jaime Gomez-Gil and 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 = {<p>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.</p>
},
	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}
}
