Visual features based boosted classification of weeds for real-time selective herbicide sprayer systems

Slaughter, 2008, Autonomous robotic weed control systems: a review, Comput. Electron. Agric., 61, 63, 10.1016/j.compag.2007.05.008 Green, 2014, Current state of herbicides in herbicide-resistant crops, Pest Manag. Sci., 70, 1351, 10.1002/ps.3727 Swain, 2011, Weed identification using an automated active shape matching (AASM) technique, Biosyst. Eng., 110, 450, 10.1016/j.biosystemseng.2011.09.011 Laursen, 1848, Dicotyledon weed quantification algorithm for selective herbicide application in maize crops, Sensors, 16, 2016 Chowdhury, 2015, A novel texture feature based multiple classifier technique for roadside vegetation classification, Expert Syst. Appl., 42, 5047, 10.1016/j.eswa.2015.02.047 Herrera, 2014, A novel approach for weed type classification based on shape descriptors and a fuzzy decision-Making method, Sensors, 14, 15304, 10.3390/s140815304 Naeem, 2007, Weed classification using angular cross sectional intensities for real-time selective herbicide applications, Computing: Theory and Applications, 2007. ICCTA'07. International Conference on, 731 Ahmad, 2011, Weed classification based on Haar wavelet transform via k-nearest neighbor (k-NN) for real-time automatic sprayer control system, Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, 17 Herrera, 2014, A new combined strategy for discrimination between types of weed, ROBOT2013: First Iberian Robotics Conference, 469 Jeon, 2011, Robust crop and weed segmentation under uncontrolled outdoor illumination, Sensors, 11, 6270, 10.3390/s110606270 Loghavi, 2008, Development of a target oriented weed control system, Comput. Electron. Agric., 63, 112, 10.1016/j.compag.2008.01.020 Song, 2015, Technology application of smart spray in agriculture: a review, Intell. Autom. Soft Comput., 1 Ishak, 2009, Weed image classification using Gabor wavelet and gradient field distribution, Comput. Electron. Agric., 66, 53, 10.1016/j.compag.2008.12.003 Arribas, 2011, Leaf classification in sunflower crops by computer vision and neural networks, Comput. Electron. Agric., 78, 9, 10.1016/j.compag.2011.05.007 Ahmad, 2016, Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems, Multimed. Tools Appl., 75, 12669, 10.1007/s11042-016-3436-9 Ahmed, 2014, Automated weed classification with local pattern-based texture descriptors, Int. Arab. J. Inf. Technol. (IAJIT), 11 Herrera, 2014, Application of the Dempster-Shafer theory to classify monocot and dicot weeds based on geometric shape descriptors, Second International Conference on Robotics and Associated High-Technologies and Equipment for Agriculture and Forestry, 149 Hamuda, 2016, A survey of image processing techniques for plant extraction and segmentation in the field, Comput. Electron. Agric., 125, 184, 10.1016/j.compag.2016.04.024 Ahmad, 2007, Statistical based real-Time selective herbicide weed classifier, Multitopic Conference, 2007. INMIC 2007. IEEE International, 1 Siddiqi, 2009, Edge link detector based weed classifier, Digital Image Processing, 2009 International Conference on, 255, 10.1109/ICDIP.2009.64 Siddiqi, 2009, A real time specific weed discrimination system using multi-level wavelet decomposition, Int. J. Agric. Biol. (Pak.) Giselsson, 2013, Seedling discrimination with shape features derived from a distance transform, Sensors, 13, 5585, 10.3390/s130505585 Siddiqi, 2014, Weed image classification using wavelet transform, stepwise linear discriminant analysis, and support vector machines for an automatic spray control system, J. Inf. Sci. Eng., 30, 1227 Wong, 2013, Modular-based classification system for weed classification using mixture of features, Int. J. Comput. Vis. Robot., 3, 261, 10.1504/IJCVR.2013.059101 Camps-Valls, 2005, Kernel-based methods for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 43, 1351, 10.1109/TGRS.2005.846154 Ting, 2003, A study of adaboost with naive bayesian classifiers: weakness and improvement, Comput. Intell., 19, 186, 10.1111/1467-8640.00219 Marchant, 2000, Shadow-invariant classification for scenes illuminated by daylight, J. Opt. Soc. Am. A, 17, 1952, 10.1364/JOSAA.17.001952 Ribeiro, 2005, Development of an image analysis system for estimation of weed pressure, 169 Burgos-Artizzu, 2010, Analysis of natural images processing for the extraction of agricultural elements, Image Vis. Comput., 28, 138, 10.1016/j.imavis.2009.05.009 Ahmed, 2008, A real-time specific weed recognition system by measuring weeds density through mask operation, 221 Ahmed, 2012, Classification of crops and weeds from digital images: a support vector machine approach, Crop Prot., 40, 98, 10.1016/j.cropro.2012.04.024 Eom, 2005, Fast extraction of edge histogram in DCT domain based on MPEG7, Proceedings of World Academy of Science, Engineering and Technology, 209 Kanopoulos, 1988, Design of an image edge detection filter using the Sobel operator, IEEE J. Solid-State Circ., 23, 358, 10.1109/4.996 Ahmad, 2014, Describing colors, textures and shapes for content based image retrieval –a survey, J. Platform Technol., 2, 34 Ahmad, 2014, A fusion of labeled-grid shape descriptors with weighted ranking algorithm for shapes recognition, World Appl. Sci. J., 31 Sauvola, 2000, Adaptive document image binarization, Pattern Recogn., 33, 225, 10.1016/S0031-3203(99)00055-2 Schapire, 1990, The strength of weak learnability, Mach. Learn., 5, 197, 10.1007/BF00116037 McCallum, 1998, A comparison of event models for naive bayes text classification, AAAI-98 Workshop on Learning for Text Categorization, 41 Friedman, 1997, Bayesian network classifiers, Mach. Learn., 29, 131, 10.1023/A:1007465528199 Hosmer, 2004 Magerman, 1995, Statistical decision-tree models for parsing, Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, 276, 10.3115/981658.981695 Vapnik, 1998, vol. 1