Leff, 2004, Geographic distribution of major crops across the world, Global Biogeochem Cycl, 18, 1, 10.1029/2003GB002108
DiTomaso, 2000, Invasive weeds in rangelands: species, impacts, and management, Weed Sci, 48, 255, 10.1614/0043-1745(2000)048[0255:IWIRSI]2.0.CO;2
Perez, 2000, Colour and shape analysis techniques for weed detection in cereal fields, Comput Electron Agric, 25, 197, 10.1016/S0168-1699(99)00068-X
Søgaard, 2005, Weed classification by active shape models, Biosyst Eng, 91, 271, 10.1016/j.biosystemseng.2005.04.011
Woebbecke, 1995, Color indices for weed identification under various soil, residue, and lighting conditions, Trans ASAE, 38, 259, 10.13031/2013.27838
Ojala, 2002, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans Pattern Anal Mach Intell, 24, 971, 10.1109/TPAMI.2002.1017623
Liu, 2016, Median robust extended local binary pattern for texture classification, IEEE Trans Image Process, 25, 1368, 10.1109/TIP.2016.2522378
Tang, 1999, Texture-based weed classification using Gabor wavelets and neural network for real-time selective herbicide applications, Urbana, 51, 61801
Ishak, 2009, Weed image classification using Gabor wavelet and gradient field distribution, Comput Electron Agric, 66, 53, 10.1016/j.compag.2008.12.003
Bossu, 2009, Wavelet transform to discriminate between crop and weed in perspective agronomic images, Comput Electron Agric, 65, 133, 10.1016/j.compag.2008.08.004
Hansen, 2003, Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression, Remote Sens Environ, 86, 542, 10.1016/S0034-4257(03)00131-7
Thorp, 2004, A review on remote sensing of weeds in agriculture, Precis Agric, 5, 477, 10.1007/s11119-004-5321-1
Huete, 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens Environ, 83, 195, 10.1016/S0034-4257(02)00096-2
Ozdogan, 2010, Remote sensing of irrigated agriculture: opportunities and challenges, Remote Sens, 2, 2274, 10.3390/rs2092274
Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int J Comput Vision, 60, 91, 10.1023/B:VISI.0000029664.99615.94
Bay, 2008, Speeded-up robust features (SURF), Comput Vision Image Understand, 110, 346, 10.1016/j.cviu.2007.09.014
Dalal, 2005, Histograms of oriented gradients for human detection, 886
Liu, 2002, Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition, IEEE Trans Image Process., 11, 467, 10.1109/TIP.2002.999679
Happy, 2012, A real time facial expression classification system using local binary patterns, 1
Lahdenoja, 2013, Towards understanding the formation of uniform local binary patterns, ISRN Mach Vision, 2013
Ahonen, 2006, Face description with local binary patterns: application to face recognition, IEEE Trans Pattern Anal Mach Intell, 28, 2037, 10.1109/TPAMI.2006.244
Jin, 2004, Face detection using improved LBP under Bayesian framework, 306
Louis, 2010, Co-occurrence of local binary patterns features for frontal face detection in surveillance applications, EURASIP J Image Video Process, 2011, 745487
Zhao, 2007, Dynamic texture recognition using local binary patterns with an application to facial expressions, IEEE Trans Pattern Anal Mach Intell, 29, 6, 10.1109/TPAMI.2007.1110
Shan, 2009, Facial expression recognition based on local binary patterns: a comprehensive study, Image Vis Comput, 27, 803, 10.1016/j.imavis.2008.08.005
Guo, 2010, A completed modeling of local binary pattern operator for texture classification, IEEE Trans Image Process, 19, 1657, 10.1109/TIP.2010.2044957
Liao, 2009, Dominant local binary patterns for texture classification, IEEE Trans Image Process, 18, 1107, 10.1109/TIP.2009.2015682
Heikkila, 2006, A texture-based method for modeling the background and detecting moving objects, IEEE Trans Pattern Anal Mach Intell, 28, 657, 10.1109/TPAMI.2006.68
Kellokumpu, 2008, Human activity recognition using a dynamic texture based method, BMVC, 1, 2
Ahmed, 2011, A study on local binary pattern for automated weed classification using template matching and support vector machine, 329
Ahmed, 2014, Automated weed classification with local pattern-based texture descriptors, Int Arab J Inf Technol, 11, 87
Ojala, 2002, Outex-new framework for empirical evaluation of texture analysis algorithms, 701
Brodatz, 1966
Lazebnik, 2005, A sparse texture representation using local affine regions, IEEE Trans Pattern Anal Mach Intell, 27, 1265, 10.1109/TPAMI.2005.151
Xu, 2010, A new texture descriptor using multifractal analysis in multi-orientation wavelet pyramid, 161
Varma, 2009, A statistical approach to material classification using image patch exemplars, IEEE Trans Pattern Anal Mach Intell, 31, 2032, 10.1109/TPAMI.2008.182
Liu, 2017, Local binary features for texture classification: taxonomy and experimental study, Pattern Recogn, 62, 135, 10.1016/j.patcog.2016.08.032
Burks, 2005, Evaluation of neural-network classifiers for weed species discrimination, Biosyst Eng, 91, 293, 10.1016/j.biosystemseng.2004.12.012
Tang, 2003, Classification of broadleaf and grass weeds using Gabor wavelets and an artificial neural network, Trans ASAE, 46, 1247, 10.13031/2013.13944
Onyango, 2003, Segmentation of row crop plants from weeds using colour and morphology, Comput Electron Agric, 39, 141, 10.1016/S0168-1699(03)00023-1
De Rainville, 2014, Bayesian classification and unsupervised learning for isolating weeds in row crops, Pattern Anal Appl, 17, 401, 10.1007/s10044-012-0307-5
Ahmed, 2011, Performance analysis of support vector machine and Bayesian classifier for crop and weed classification from digital images, World Appl Sci J, 12, 432
Ahmad, 2011, Weed classification based on Haar wavelet transform via k-nearest neighbor (k-NN) for real-time automatic sprayer control system, 17
Burks, 2000, Classification of weed species using color texture features and discriminant analysis, Trans ASAE, 43, 441, 10.13031/2013.2723
Pydipati, 2006, Identification of citrus disease using color texture features and discriminant analysis, Comput Electron Agric, 52, 49, 10.1016/j.compag.2006.01.004
Pulido, 2017, Weed recognition by SVM texture feature classification in outdoor vegetable crop images, IngenIería e InvestIgacIón., 37, 68, 10.15446/ing.investig.v37n1.54703
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
Liu, 2009, A method of plant classification based on wavelet transforms and support vector machines, 253
Guerrero, 2012, Support vector machines for crop/weeds identification in maize fields, Expert Syst Appl, 39, 11149, 10.1016/j.eswa.2012.03.040
Arivazhagan, 2013, Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features, Agric Eng Int: CIGR J, 15, 211
Camargo, 2009, Image pattern classification for the identification of disease causing agents in plants, Comput Electron Agric, 66, 121, 10.1016/j.compag.2009.01.003
Rumpf, 2010, Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance, Comput Electron Agric, 74, 91, 10.1016/j.compag.2010.06.009
Meyer, 2008, Verification of color vegetation indices for automated crop imaging applications, Comput Electron Agric, 63, 282, 10.1016/j.compag.2008.03.009
Neto J Camargo. A combined statistical-soft computing approach for classification and mapping weed species in minimum-tillage systems; 2004.
Ojala, 1996, A comparative study of texture measures with classification based on featured distributions, Pattern Recogn, 29, 51, 10.1016/0031-3203(95)00067-4
Wu, 2009, Weed/corn seedling recognition by support vector machine using texture features, Afr J Agric Res, 4, 840
Boser, 1992, A training algorithm for optimal margin classifiers, 144
Mathur, 2008, Crop classification by support vector machine with intelligently selected training data for an operational application, Int J Remote Sens, 29, 2227, 10.1080/01431160701395203
Lin, 2009
Forman, 2010, Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement, ACM SIGKDD Explor Newsl, 12, 49, 10.1145/1882471.1882479
Akbarzadeh, 2018, Plant discrimination by support vector machine classifier based on spectral reflectance, Comput Electron Agric, 148, 250, 10.1016/j.compag.2018.03.026
Gao, 2018, Recognising weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery, Biosyst Eng, 170, 39, 10.1016/j.biosystemseng.2018.03.006
Sokolova, 2006, Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation, 1015
Sokolova, 2009, A systematic analysis of performance measures for classification tasks, Inf Process Manage, 45, 427, 10.1016/j.ipm.2009.03.002
Xilinx. Xilinx Zynq-7000 all programmable SoC ZC702 evaluation kit. link: https://www.xilinx.com/products/boards-and-kits/ek-z7-zc702-g.html.
Abdi, 2010, Principal component analysis, Wiley Interdiscip Rev Comput Stat, 2, 433, 10.1002/wics.101
Wold, 1987, Principal component analysis, Chemometr Intell Lab Syst, 2, 37, 10.1016/0169-7439(87)80084-9
Hofmann, 2008, Kernel methods in machine learning, Ann Stat, 1171, 10.1214/009053607000000677
Milgram, 2006, “One against one” or “one against all”: Which one is better for handwriting recognition with SVMs?
Maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579
Cover, 1967, Nearest neighbor pattern classification, IEEE Trans Inf Theory, 13, 21, 10.1109/TIT.1967.1053964
Weinberger, 2009, Distance metric learning for large margin nearest neighbor classification, J Mach Learn Res, 10, 207
Yadav, 2015, Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species, Appl Soft Comput, 32, 101, 10.1016/j.asoc.2015.03.039