{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:36:04Z","timestamp":1741667764362,"version":"3.38.0"},"reference-count":36,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MGS"],"published-print":{"date-parts":[[2024,3,4]]},"abstract":"<jats:p>Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%.<\/jats:p>","DOI":"10.3233\/mgs-230055","type":"journal-article","created":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T16:00:12Z","timestamp":1709049612000},"page":"337-362","source":"Crossref","is-referenced-by-count":0,"title":["Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image"],"prefix":"10.1177","volume":"19","author":[{"given":"Devyani Jadhav","family":"Bhamare","sequence":"first","affiliation":[{"name":"SRES\u2019s Sanjivani College of Engineering, Kopargaon, India"}]},{"given":"Ramesh","family":"Pudi","sequence":"additional","affiliation":[{"name":"Aditya College of Engineering, Surampalem, India"}]},{"given":"Garigipati Rama","family":"Krishna","sequence":"additional","affiliation":[{"name":"Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, India"}]}],"member":"179","reference":[{"issue":"8","key":"10.3233\/MGS-230055_ref1","doi-asserted-by":"crossref","first-page":"4639","DOI":"10.3390\/rs14184639","article-title":"Unsupervised domain adaptation with adversarial self-training for crop classification using remote sensing images","volume":"14","author":"Kwak","year":"2022","journal-title":"Remote Sensing"},{"issue":"5","key":"10.3233\/MGS-230055_ref2","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1016\/j.cj.2022.07.005","article-title":"Temporal sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series","volume":"10","author":"Li","year":"2022","journal-title":"The Crop Journal"},{"key":"10.3233\/MGS-230055_ref3","doi-asserted-by":"crossref","first-page":"102762","DOI":"10.1016\/j.jag.2022.102762","article-title":"A joint learning Im-BiLSTM model for incomplete time-series Sentinel-2A data imputation and crop classification","volume":"108","author":"Chen","year":"2022","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"issue":"11","key":"10.3233\/MGS-230055_ref4","doi-asserted-by":"crossref","first-page":"2713","DOI":"10.3390\/rs14112713","article-title":"Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP","volume":"14","author":"Wu","year":"2022","journal-title":"Remote Sensing"},{"key":"10.3233\/MGS-230055_ref5","doi-asserted-by":"crossref","first-page":"102598","DOI":"10.1016\/j.jag.2021.102598","article-title":"DOCC: Deep one-class crop classification via positive and unlabeled learning for multi-modal satellite imagery","volume":"105","author":"Lei","year":"2021","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"issue":"4","key":"10.3233\/MGS-230055_ref6","doi-asserted-by":"crossref","first-page":"829","DOI":"10.3390\/rs14040829","article-title":"Cotton classification method at the county scale based on multi-features and Random Forest feature selection algorithm and classifier","volume":"14","author":"Fei","year":"2022","journal-title":"Remote Sensing"},{"key":"10.3233\/MGS-230055_ref7","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/JSTARS.2020.2971763","article-title":"A CNN-transformer hybrid approach for crop classification using multitemporal multisensor images","volume":"13","author":"Li","year":"2020","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"5","key":"10.3233\/MGS-230055_ref8","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.3390\/rs13152917","article-title":"Crops fine classification in airborne hyperspectral imagery based on multi-feature fusion and deep learning","volume":"13","author":"Wei","year":"2021","journal-title":"Remote Sensing"},{"key":"10.3233\/MGS-230055_ref9","doi-asserted-by":"crossref","unstructured":"A. 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Pal, SAR and Optical data fusion based on anisotropic Diffusion with PCA and Classification using Patch-based with LBP, in: The Proceeding of IEEE India Geoscience and Remote Sensing Symposium (InGARSS), IEEE, Ahmedabad, India, 2021.","DOI":"10.1109\/InGARSS48198.2020.9358949"},{"issue":"12","key":"10.3233\/MGS-230055_ref10","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/MGS-230055_ref11","doi-asserted-by":"crossref","first-page":"107199","DOI":"10.1016\/j.compeleceng.2021.107199","article-title":"Hyperspectral imaging classification based on LBP feature extraction and multimodel ensemble learning","volume":"92","author":"Cheng","year":"2021","journal-title":"Computers & Electrical 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Yuan and G. He, Application of an anisotropic diffusion based preprocessing filtering algorithm for high resolution remote sensing image segmentation, in: Proceedings of 2008 Congress on Image and Signal Processing, IEEE, Vol. 3, 2008, pp. 629\u2013633.","DOI":"10.1109\/CISP.2008.318"},{"key":"10.3233\/MGS-230055_ref28","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/s41095-019-0139-y","article-title":"Automated brain tumor segmentation on multi-modal MR image using SegNet","volume":"5","author":"Alqazzaz","year":"2019","journal-title":"Computational Visual Media"},{"issue":"10","key":"10.3233\/MGS-230055_ref29","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/rs2102369","article-title":"Applicability of green-red vegetation index for remote sensing of vegetation phenology","volume":"2","author":"Motohka","year":"2010","journal-title":"Remote Sensing"},{"key":"10.3233\/MGS-230055_ref30","doi-asserted-by":"crossref","unstructured":"T.A. 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