{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:40:11Z","timestamp":1785577211274,"version":"3.56.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11042-022-12200-y","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T19:02:57Z","timestamp":1644865377000},"page":"10313-10336","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["Convolutional Neural Networks based classifications of soil images"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9670-3020","authenticated-orcid":false,"given":"M. G.","family":"Lanjewar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5764-223X","authenticated-orcid":false,"given":"O. L.","family":"Gurav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"12200_CR1","unstructured":"Alemi A (2016) Improving Inception and Image Classification in TensorFlow. https:\/\/ai.googleblog.com\/2016\/08\/improving-inception-and-image.html"},{"key":"12200_CR2","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s00138-020-01157-3","volume":"32","author":"MZ Alom","year":"2021","unstructured":"Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2021) Inception recurrent convolutional neural network for object recognition. Mach Vis Appl 32:28. https:\/\/doi.org\/10.1007\/s00138-020-01157-3","journal-title":"Mach Vis Appl"},{"key":"12200_CR3","doi-asserted-by":"publisher","unstructured":"Anguraj DK, Mandhala VN, Bhattacharyya D, Kim TH (2021) Hybrid neural network classification for irrigation control in WSN based precision agriculture. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-020-02704-6","DOI":"10.1007\/s12652-020-02704-6"},{"key":"12200_CR4","doi-asserted-by":"publisher","unstructured":"Athanasios V, Nikolaos D, Anastasios D, Eftychios P (2018) Deep learning for computer vision: a brief review. Computational Intelligence and Neuroscience Article ID 7068349. https:\/\/doi.org\/10.1155\/2018\/7068349","DOI":"10.1155\/2018\/7068349"},{"key":"12200_CR5","doi-asserted-by":"publisher","first-page":"104586","DOI":"10.1016\/j.still.2020.104586","volume":"199","author":"A Azizi","year":"2020","unstructured":"Azizi A, Gilandeh YA, Tarahom MG, Saleh-Bigdeli AA, Moghaddam HA (2020) Classification of soil aggregates: a novel approach based on deep learning. Soil Tillage Res 199:104586","journal-title":"Soil Tillage Res"},{"key":"12200_CR6","first-page":"584","volume-title":"European conference on computer vision","author":"A Babenko","year":"2014","unstructured":"Babenko A, Slesarev A, Chigorin A, Lempitsky V (2014) Neural codes for image retrieval. In: European conference on computer vision. Springer, Cham, pp 584\u2013599"},{"key":"12200_CR7","doi-asserted-by":"publisher","first-page":"15244","DOI":"10.1038\/s41598-018-33516-6","volume":"8","author":"T Behrens","year":"2018","unstructured":"Behrens T, Schmidt K, MacMillan RA et al (2018)Multi-scale digital soil mapping with deep learning. Sci Rep 8:15244. https:\/\/doi.org\/10.1038\/s41598-018-33516-6","journal-title":"Sci Rep"},{"issue":"2","key":"12200_CR8","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.neunet.2006.01.005","volume":"19","author":"B Bhattacharya","year":"2006","unstructured":"Bhattacharya B, Solomatine DP (2006) Machine learning in soil classification. Neural Netw 19(2):186\u2013195","journal-title":"Neural Netw"},{"key":"12200_CR9","doi-asserted-by":"crossref","unstructured":"Cavallaro G, Riedel M, Bodenstein C et al (2015) Scalable developments for big data analytics in remote sensing. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) pp 2015:1366\u20131369","DOI":"10.1109\/IGARSS.2015.7326030"},{"key":"12200_CR10","first-page":"2250","volume":"33","author":"RT Chandan","year":"2018","unstructured":"Chandan RT (2018) An intelligent model for Indian soil classification using various machine learning techniques. International Journal of Computational Engineering Research (IJCER) 33:2250\u20133005","journal-title":"International Journal of Computational Engineering Research (IJCER)"},{"issue":"2","key":"12200_CR11","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.inpa.2019.08.001","volume":"7","author":"RD Choudhury","year":"2020","unstructured":"Choudhury RD, Barman U (2020) Soil texture classification using multi class support vector machine. Information Processing in Agriculture 7(2):318\u2013332","journal-title":"Information Processing in Agriculture"},{"key":"12200_CR12","first-page":"07261","volume":"1602","author":"S Christian","year":"2016","unstructured":"Christian S, Sergey I, Vincent V, Alex A (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. Computer Vision and Pattern Recognition arXiv 1602:07261","journal-title":"Computer Vision and Pattern Recognition arXiv"},{"key":"12200_CR13","doi-asserted-by":"publisher","first-page":"19959","DOI":"10.1109\/ACCESS.2018.2815149","volume":"6","author":"J Chu","year":"2018","unstructured":"Chu J, Guo Z, Leng L (2018) Object detection based on multi-layer convolution feature fusion and online hard example mining. IEEE Access 6:19959\u201319967","journal-title":"IEEE Access"},{"issue":"2","key":"12200_CR14","first-page":"393","volume":"57","author":"SO Chung","year":"2012","unstructured":"Chung SO, Cho KH, Cho JW, Jung KY, Yamakawa T (2012) Soil texture classification algorithm using rgb characteristics of soil images. J Fac Agr Kyushuuniv 57(2):393\u2013397","journal-title":"J Fac Agr Kyushuuniv"},{"key":"12200_CR15","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1109\/ICIP.2001.958211","volume-title":"Proceedings 2001 international conference on image processing (cat. No.01CH37205)","author":"A Doulamis","year":"2001","unstructured":"Doulamis A, Doulamis N, Maragos P (2001) Generalized multiscale connected operators with applications to granulometric image analysis. In: Proceedings 2001 international conference on image processing (cat. No.01CH37205), vol 3, pp 684\u2013687. https:\/\/doi.org\/10.1109\/ICIP.2001.958211"},{"key":"12200_CR16","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","volume":"145","author":"KP Ferentinos","year":"2018","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Computer Electronics Agriculture 145:311\u2013318. https:\/\/doi.org\/10.1016\/j.compag.2018.01.009","journal-title":"Computer Electronics Agriculture"},{"key":"12200_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.rse.2006.03.004","volume":"1","author":"GM Foody","year":"2006","unstructured":"Foody GM, Mathur A, Sanchez-Hernandez C, Boyd DS (2006) Training set size requirements for the classification of a specific class. Remote Sens Environ 1:1\u201314","journal-title":"Remote Sens Environ"},{"key":"12200_CR18","unstructured":"Franc O (2017) Xception: deep learning with depth wise separable convolutions. Computer vision foundation Google Inc IEEE Xplore"},{"key":"12200_CR19","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354\u2013377","journal-title":"Pattern Recogn"},{"key":"12200_CR20","doi-asserted-by":"publisher","first-page":"4876","DOI":"10.7150\/jca.28769","volume":"10","author":"Q Guan","year":"2019","unstructured":"Guan Q, Wang Y, Ping B, Li D, du J, Qin Y, Lu H, Wan X, Xiang J (2019) Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer 10:4876\u20134882","journal-title":"J Cancer"},{"issue":"8","key":"12200_CR21","first-page":"656","volume":"17","author":"Y Guang","year":"2015","unstructured":"Guang Y, Shujun Q, Pengfei C (2015) Rock and soil classification using PLS-DA and SVM combined with a laser-induced breakdown spectroscopy library. Plasma SciTechnol 17(8):656\u2013663","journal-title":"Plasma SciTechnol"},{"key":"12200_CR22","volume-title":"EuropeanConference on Computer Vision","author":"K He","year":"2016","unstructured":"He K et al (2016) Identity mappings in deep residual networks. In: EuropeanConference on Computer Vision. Springer"},{"key":"12200_CR23","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"Proceedingsof the IEEE Conference on Computer Vision and Pattern Recognition","author":"K He","year":"2016","unstructured":"He K et al (2016) Deep residual learning for image recognition. In: Proceedingsof the IEEE Conference on Computer Vision and Pattern Recognition, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90"},{"key":"12200_CR24","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas NV","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas NV, pp 770\u2013778"},{"key":"12200_CR25","unstructured":"Honawad SK, Chinchali SS, Pawar K, Deshpande P (2017) Soil classification and suitable crop prediction. 4IOSR Journal of Computer Engineering (IOSR-JCE):25\u201329"},{"key":"12200_CR26","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition (CVPR) Honolulu HI USA 21\u201326, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"12200_CR27","unstructured":"Hussain M (2020) What is rectified linear unit (ReLU)?. Introduction to ReLU Activation Function. https:\/\/www.mygreatlearning.com\/blog\/relu-activation-function\/"},{"key":"12200_CR28","doi-asserted-by":"publisher","unstructured":"Javaheri SH, Teimourpour B (2014) Response modeling in direct marketing, in Data Mining Applications with R. https:\/\/doi.org\/10.1016\/B978-0-12-411511-8.00006-2","DOI":"10.1016\/B978-0-12-411511-8.00006-2"},{"key":"12200_CR29","first-page":"1082","volume":"LI","author":"RP Kestrilia","year":"2020","unstructured":"Kestrilia RP, Syaiful A, Tatas HPB, Agus S (2020)Real-time assessment of plant photosynthetic pigment contentswith an artificial intelligence approach in a mobile application. Journal of Agricultural Engineering LI:1082","journal-title":"Journal of Agricultural Engineering"},{"key":"12200_CR30","doi-asserted-by":"publisher","first-page":"105938","DOI":"10.1016\/j.compag.2020.105938","volume":"181","author":"S Kiattisin","year":"2021","unstructured":"Kiattisin S (2021) Machine learning techniques for classifying the sweetness of watermelon using acoustic signal and image processing. Comput Electron Agric 181:105938","journal-title":"Comput Electron Agric"},{"key":"12200_CR31","doi-asserted-by":"publisher","first-page":"8009","DOI":"10.1007\/s12652-020-02530-w","volume":"12","author":"V Kumar","year":"2021","unstructured":"Kumar V, Balakrishnan N (2021) Artificial intelligence-based agriculture automated monitoring systems using WSN. J Ambient Intell Human Comput. 12:8009\u20138016. https:\/\/doi.org\/10.1007\/s12652-020-02530-w","journal-title":"J Ambient Intell Human Comput."},{"key":"12200_CR32","volume-title":"2002 ASAE annual international meeting\/ CIGR XVth world congress","author":"O Lameck","year":"2002","unstructured":"Lameck O, Odhiambo RS, Freeland RE, Yoder J, Wesley H (2002) Application of fuzzy-neural network in classification of soils using ground-penetrating radar imagery. In: 2002 ASAE annual international meeting\/ CIGR XVth world congress"},{"key":"12200_CR33","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/UEMCON.2018.8796838","volume-title":"2018 9th IEEE annual ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)","author":"Y Lu","year":"2018","unstructured":"Lu Y, Perez D, Dao M, Kwan C, Li J (2018) Deep learning with synthetic hyperspectral images for improved soil detection in multispectral imagery. In: 2018 9th IEEE annual ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON), pp 666\u2013672. https:\/\/doi.org\/10.1109\/UEMCON.2018.8796838"},{"issue":"2","key":"12200_CR34","first-page":"989","volume":"8","author":"AD Mengistu","year":"2018","unstructured":"Mengistu AD, Alemayehu DM (2018) Soil characterization and classification: a hybrid approach of computer vision and sensor network. Int J Electr Comput Eng 8(2):989\u2013995","journal-title":"Int J Electr Comput Eng"},{"key":"12200_CR35","doi-asserted-by":"publisher","unstructured":"Mohapatra H, Rath AK (2021) IoE based framework for smart agriculture. J Ambient Intell Human Comput. https:\/\/doi.org\/10.1007\/s12652-021-02908-4","DOI":"10.1007\/s12652-021-02908-4"},{"key":"12200_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ISCAS.2018.8351550","volume-title":"2018 IEEE International Symposium on Circuits and Systems (ISCAS)","author":"L Nguyen","year":"2018","unstructured":"Nguyen L, Lin D, Lin Z, Cao J (2018) Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp 1\u20135. https:\/\/doi.org\/10.1109\/ISCAS.2018.8351550"},{"key":"12200_CR37","unstructured":"Online Article, Top 6 Indian AgriTech startups that are Revolutionising Agriculture (2018). https:\/\/analyticsindiamag.com\/top-6-indian-agritech-startups-that-are-revolutionising-agriculture\/"},{"key":"12200_CR38","unstructured":"Online Article (n.d.) Machine Learning In Agriculture: How Ai Helps Solve The Industry's Most Pressing Challenges. https:\/\/objectcomputing.com\/expertise\/machine-learning\/machine-learning-in-agriculture"},{"key":"12200_CR39","unstructured":"Online Article (n.d.) https:\/\/www.mathworks.com\/help\/physmod\/simscape\/ug\/estimate-computation-costs.html"},{"key":"12200_CR40","unstructured":"Online Article Salty Soils. (n.d.) http:\/\/www.fao.org\/3\/r4082e\/r4082e08.htm"},{"key":"12200_CR41","doi-asserted-by":"publisher","first-page":"10339","DOI":"10.1007\/s12652-020-02820-3","volume":"12","author":"Y Pan","year":"2021","unstructured":"Pan Y, Pi D, Khan I et al (2021) DenseNetFuse: a study of deep unsupervised DenseNet to infrared and visual image fusion. J Ambient Intell Human Comput. 12:10339\u201310351. https:\/\/doi.org\/10.1007\/s12652-020-02820-3","journal-title":"J Ambient Intell Human Comput."},{"key":"12200_CR42","doi-asserted-by":"publisher","first-page":"21941","DOI":"10.1007\/s11042-020-08905-7","volume":"79","author":"HS Pannu","year":"2020","unstructured":"Pannu HS, Ahuja S, Dang N, Soni S, Malhi AK (2020) Deep learning based image classification for intestinal hemorrhage. Multimed Tools Appl 79:21941\u201321966. https:\/\/doi.org\/10.1007\/s11042-020-08905-7","journal-title":"Multimed Tools Appl"},{"key":"12200_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2021.3079714","volume":"9","author":"J Parab","year":"2021","unstructured":"Parab J, Sequeira M, Lanjewar M, Pinto C, Naik G (2021) Backpropagation neural network-based machine learning model for prediction of blood urea and glucose in CKD patients. IEEE Journal of Translational Engineering in Health and Medicine 9:1\u20138 Art no. 4900608. https:\/\/doi.org\/10.1109\/JTEHM.2021.3079714","journal-title":"IEEE Journal of Translational Engineering in Health and Medicine"},{"key":"12200_CR44","doi-asserted-by":"publisher","first-page":"29481","DOI":"10.1007\/s11042-021-11087-5","volume":"80","author":"N Patil","year":"2021","unstructured":"Patil N, Patil PN, Rao PV (2021) Convolution neural network and deep-belief network (DBN) based automatic detection and diagnosis of Glaucoma. Multimed Tools Appl 80:29481\u201329495. https:\/\/doi.org\/10.1007\/s11042-021-11087-5","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"12200_CR45","doi-asserted-by":"publisher","first-page":"368","DOI":"10.3390\/rs13030368","volume":"13","author":"CA Ramezan","year":"2021","unstructured":"Ramezan CA, Warner TA, Maxwell AE, Price BS (2021) Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data. Remote Sens 13(3):368. https:\/\/doi.org\/10.3390\/rs13030368","journal-title":"Remote Sens"},{"key":"12200_CR46","first-page":"792","volume":"4","author":"A Rao","year":"2016","unstructured":"Rao A, Abhishek JU, Manjunatha GNS, Beham R (2016) Machine learn soil classification. Crop Detect 4:792\u2013794","journal-title":"Crop Detect"},{"key":"12200_CR47","unstructured":"Sanjay M (2018) Why and how to Cross Validate a Model? Importance and types of Cross validation techniques. https:\/\/towardsdatascience.com\/why-and-how-to-cross-validate-a-model-d6424b45261f"},{"key":"12200_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-35990-4_12","volume-title":"Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in intelligent systems and computing, vol 1092","author":"L Santos","year":"2020","unstructured":"Santos L, Santos FN, Oliveira PM, Shinde P (2020) Deep Learning Applications in Agriculture: A Short Review. In: Silva M, Lu\u00eds LJ, Reis L, Sanfeliu A, Tardioli D (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in intelligent systems and computing, vol 1092. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-35990-4_12"},{"issue":"1","key":"12200_CR49","doi-asserted-by":"publisher","first-page":"15","DOI":"10.9756\/BIJAIP.1004","volume":"1","author":"R Shenbagavalli","year":"2011","unstructured":"Shenbagavalli R, Ramar K (2011) Classification of soil textures based on Law\u2019s features extracted from preprocessing images on sequential and random windows. Bonfring Int J Adv Image Process 1(1):15\u201315","journal-title":"Bonfring Int J Adv Image Process"},{"key":"12200_CR50","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"12200_CR51","doi-asserted-by":"publisher","first-page":"14887","DOI":"10.1007\/s11042-021-10544-5","volume":"80","author":"P Srivastava","year":"2021","unstructured":"Srivastava P, Shukla A, Bansal A (2021) A comprehensive review on soil classification using deep learning and computer vision techniques. Multimed Tools Appl 80:14887\u201314914. https:\/\/doi.org\/10.1007\/s11042-021-10544-5","journal-title":"Multimed Tools Appl"},{"key":"12200_CR52","first-page":"411","volume-title":"Int. Conf. on signal processing, communication, power and embedded system","author":"K Srunitha","year":"2016","unstructured":"Srunitha K, Padmavathi S (2016) Performance of SVM classifier for image based soil classification. In: Int. Conf. on signal processing, communication, power and embedded system. SCOPES, pp 411\u2013415"},{"key":"12200_CR53","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P et al (2015) Going deeper with convolutions. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"12200_CR54","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning In AAAI, pp 4278\u20134284","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"12200_CR55","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","volume":"161","author":"EC Too","year":"2018","unstructured":"Too EC, Yujian L, Njuki S, Yingchun L (2018) A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture 161:272\u2013279. https:\/\/doi.org\/10.1016\/j.compag.2018.03.032","journal-title":"Computers and Electronics in Agriculture"},{"key":"12200_CR56","doi-asserted-by":"publisher","first-page":"105809","DOI":"10.1016\/j.compag.2020.105809","volume":"179","author":"F Var\u00e7\u0131n","year":"2020","unstructured":"Var\u00e7\u0131n F (2020) Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks. Comput Electron Agric 179:105809","journal-title":"Comput Electron Agric"},{"key":"12200_CR57","doi-asserted-by":"crossref","unstructured":"Vibhute AD, Kale KV, Dhumal RK, Mehrotra SC (2015) Soil type classification and mapping using hyperspectral remote sensing data. In: Conference on man and machine interfacing (MAMI), pp 1\u20134","DOI":"10.1109\/MAMI.2015.7456607"},{"key":"12200_CR58","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.compag.2017.11.037","volume":"144","author":"W Wu","year":"2018","unstructured":"Wu W, Li AD, He XH, Ma R, Liu HB, Lv JK (2018) A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in Southwest China. Comput Electron Agric 144:86\u201393","journal-title":"Comput Electron Agric"},{"key":"12200_CR59","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"G Yanming","year":"2016","unstructured":"Yanming G, Yu L, Ard O, Songyang L, Song W, Michael SL (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27\u201348","journal-title":"Neurocomputing"},{"key":"12200_CR60","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1109\/IGARSS.2003.1294621","volume-title":"2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse: Proceedings (IEEE Cat.No.03CH37477)","author":"X Zhang","year":"2003","unstructured":"Zhang X, Younan NH, King RL (2003) Soil texture classification, using wavelet transform and maximum likelihood approach. In: 2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse: Proceedings (IEEE Cat.No.03CH37477), pp 2888\u20132890"},{"issue":"4","key":"12200_CR61","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.3390\/s20041010","volume":"20","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Chu J, Leng L, Miao J (2020)Mask-refined R-CNN: a network for refining object details in instance segmentation. Sensors 20(4):1010","journal-title":"Sensors"},{"key":"12200_CR62","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.compag.2008.07.008","volume":"65","author":"Z Zhao","year":"2009","unstructured":"Zhao Z, Chow TL, Rees HW, Yang Q, Xing Z, Meng FR (2009) Predict soil texture distributions using an artificial neural network model. Comput Electron Agric 65:36\u201348","journal-title":"Comput Electron Agric"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12200-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-12200-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-12200-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T14:44:21Z","timestamp":1726670661000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-12200-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,14]]},"references-count":62,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["12200"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-12200-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,14]]},"assertion":[{"value":"26 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest\/competing interests"}},{"value":"NA.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}