The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models

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dc.authoridHasnain, Muhammad Usama/0009-0006-5577-7652
dc.authoridIqbal, Rashid/0000-0003-0473-889X
dc.authoridS Elshikh, Mohamed/0000-0002-6710-0458
dc.authoridTariq, Aqil/0000-0003-1196-1248
dc.contributor.authorJamil, Mutiullah
dc.contributor.authorRehman, Hafeezur
dc.contributor.authorZaheer, Muhammad Saqlain
dc.contributor.authorTariq, Aqil
dc.contributor.authorIqbal, Rashid
dc.contributor.authorHasnain, Muhammad Usama
dc.contributor.authorMajeed, Asma
dc.date.accessioned2024-12-24T19:27:57Z
dc.date.available2024-12-24T19:27:57Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractSatellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-'2011', 'Miraj-'08', and 'Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.
dc.description.sponsorshipThe authors extend their appreciation to the Researchers supporting project number (RSP2023R306), King Saud University, Riyadh, Saudi Arabia. [RSP2023R306]; King Saud University, Riyadh, Saudi Arabia
dc.description.sponsorshipThe authors extend their appreciation to the Researchers supporting project number (RSP2023R306), King Saud University, Riyadh, Saudi Arabia.
dc.identifier.doi10.1038/s41598-023-46957-5
dc.identifier.issn2045-2322
dc.identifier.issue1
dc.identifier.pmid37963968
dc.identifier.scopus2-s2.0-85176462439
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1038/s41598-023-46957-5
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6839
dc.identifier.volume13
dc.identifier.wosWOS:001104793000042
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherNature Portfolio
dc.relation.ispartofScientific Reports
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.titleThe use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models
dc.typeArticle

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