Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System
Many approaches for crop yield prediction were analyzed by countries using remote sensing data, but the information obtained was less successful due to insufficient data gathered due to climatic variables and poor image resolution. As a result, current crop yield estimation methods are obsolete and...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/9575423 |
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| _version_ | 1832551545897484288 |
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| author | Venkata Kanaka Srivani Maddala K. Jayarajan M. Braveen Ranjan Walia Patteti Krishna Sivakumar Ponnusamy Karthikeyan Kaliyaperumal |
| author_facet | Venkata Kanaka Srivani Maddala K. Jayarajan M. Braveen Ranjan Walia Patteti Krishna Sivakumar Ponnusamy Karthikeyan Kaliyaperumal |
| author_sort | Venkata Kanaka Srivani Maddala |
| collection | DOAJ |
| description | Many approaches for crop yield prediction were analyzed by countries using remote sensing data, but the information obtained was less successful due to insufficient data gathered due to climatic variables and poor image resolution. As a result, current crop yield estimation methods are obsolete and no longer useful. Several attempts have been made to overcome these difficulties by combining high precision remote sensing images. Furthermore, such remote sensing-based working models are better suited to extraterrestrial farmers and homogeneous agricultural areas. The development of this innovative framework was prompted by a scarcity of high-quality satellite imagery. This intelligent strategy is based on a new theoretical framework that employs the energy equation to improve crop yield predictions. This method was used to collect input from multiple farmers in order to validate the observation. The proposed technique’s excellent reliability on crop yield prediction is compared and contrasted between crop yield prediction and actual production in different areas, and meaningful observations are provided. |
| format | Article |
| id | doaj-art-7ae2085fec8249019846f515377df9d6 |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-7ae2085fec8249019846f515377df9d62025-02-03T06:01:17ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/9575423Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring SystemVenkata Kanaka Srivani Maddala0K. Jayarajan1M. Braveen2Ranjan Walia3Patteti Krishna4Sivakumar Ponnusamy5Karthikeyan Kaliyaperumal6Department of Science and HumanitiesDepartment of Information TechnologySchool of Computer Science and EngineeringDepartment of Electrical EngineeringDepartment of Electronics and Communication EngineeringDepartment of Computer Science and EngineeringIoT—HH CampusMany approaches for crop yield prediction were analyzed by countries using remote sensing data, but the information obtained was less successful due to insufficient data gathered due to climatic variables and poor image resolution. As a result, current crop yield estimation methods are obsolete and no longer useful. Several attempts have been made to overcome these difficulties by combining high precision remote sensing images. Furthermore, such remote sensing-based working models are better suited to extraterrestrial farmers and homogeneous agricultural areas. The development of this innovative framework was prompted by a scarcity of high-quality satellite imagery. This intelligent strategy is based on a new theoretical framework that employs the energy equation to improve crop yield predictions. This method was used to collect input from multiple farmers in order to validate the observation. The proposed technique’s excellent reliability on crop yield prediction is compared and contrasted between crop yield prediction and actual production in different areas, and meaningful observations are provided.http://dx.doi.org/10.1155/2022/9575423 |
| spellingShingle | Venkata Kanaka Srivani Maddala K. Jayarajan M. Braveen Ranjan Walia Patteti Krishna Sivakumar Ponnusamy Karthikeyan Kaliyaperumal Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System Journal of Food Quality |
| title | Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System |
| title_full | Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System |
| title_fullStr | Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System |
| title_full_unstemmed | Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System |
| title_short | Multisensor Data and Cross-Validation Technique for Merging Temporal Images for the Agricultural Performance Monitoring System |
| title_sort | multisensor data and cross validation technique for merging temporal images for the agricultural performance monitoring system |
| url | http://dx.doi.org/10.1155/2022/9575423 |
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