The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models
Abstract Satellite 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. Vario...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46957-5 |
| _version_ | 1797559759241478144 |
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| author | Mutiullah Jamil Hafeezur Rehman Muhammad Saqlain Zaheer Aqil Tariq Rashid Iqbal Muhammad Usama Hasnain Asma Majeed Awais Munir Ayman El Sabagh Muhammad Habib ur Rahman Ahsan Raza Mohammad Ajmal Ali Mohamed S. Elshikh |
| author_facet | Mutiullah Jamil Hafeezur Rehman Muhammad Saqlain Zaheer Aqil Tariq Rashid Iqbal Muhammad Usama Hasnain Asma Majeed Awais Munir Ayman El Sabagh Muhammad Habib ur Rahman Ahsan Raza Mohammad Ajmal Ali Mohamed S. Elshikh |
| author_sort | Mutiullah Jamil |
| collection | DOAJ |
| description | Abstract Satellite 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. |
| first_indexed | 2024-03-10T17:49:53Z |
| format | Article |
| id | doaj.art-64e7dd7c37244127964104c6649b94bc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:49:53Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-64e7dd7c37244127964104c6649b94bc2023-11-20T09:24:03ZengNature PortfolioScientific Reports2045-23222023-11-0113111510.1038/s41598-023-46957-5The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning modelsMutiullah Jamil0Hafeezur Rehman1Muhammad Saqlain Zaheer2Aqil Tariq3Rashid Iqbal4Muhammad Usama Hasnain5Asma Majeed6Awais Munir7Ayman El Sabagh8Muhammad Habib ur Rahman9Ahsan Raza10Mohammad Ajmal Ali11Mohamed S. Elshikh12Department of Computer Science, Khwaja Fareed University of Engineering and Information TechnologyDepartment of Computer Science, Khwaja Fareed University of Engineering and Information TechnologyDepartment of Agricultural Engineering, Khwaja Fareed University of Engineering and Information TechnologyDepartment of Wildlife, Fisheries and Aquaculture, Mississippi State UniversityDepartment of Agronomy, Faculty of Agriculture and Environment, The Islamia University of BahawalpurInstitute of Plant Breeding and Biotechnology, MNS-University of AgricultureInstitute of Agro-Industry & Environment, The Islamia University of BahawalpurInstitute of Agro-Industry & Environment, The Islamia University of BahawalpurDepartment of Agronomy, Faculty of Agriculture, Kafrelsheikh UniversityInstitute of Plant Breeding and Biotechnology, MNS-University of AgricultureCrop Science, Institute of Crop Science and Resource Conservation (INRES), University of BonnDepartment of Botany and Microbiology, College of Science, King Saud UniversityDepartment of Botany and Microbiology, College of Science, King Saud UniversityAbstract Satellite 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.https://doi.org/10.1038/s41598-023-46957-5 |
| spellingShingle | Mutiullah Jamil Hafeezur Rehman Muhammad Saqlain Zaheer Aqil Tariq Rashid Iqbal Muhammad Usama Hasnain Asma Majeed Awais Munir Ayman El Sabagh Muhammad Habib ur Rahman Ahsan Raza Mohammad Ajmal Ali Mohamed S. Elshikh The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models Scientific Reports |
| title | The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models |
| title_full | The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models |
| title_fullStr | The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models |
| title_full_unstemmed | The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models |
| title_short | The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models |
| title_sort | use of multispectral radio meter msr5 data for wheat crop genotypes identification using machine learning models |
| url | https://doi.org/10.1038/s41598-023-46957-5 |
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