| Type of Material: | Thesis |
| Title: | Design and development of novel data analysis techniques for crop yield mapping using remote sensing satellite imagery |
| Researcher: | Sarith Divakar, M |
| Guide: | Sudheep Elayidom, M and Rajesh, R |
| Department: | Department of Computer Science |
| Publisher: | Cochin University of Science & Technology, Cochin |
| Place: | Cochin |
| Year: | 2022 |
| Language: | English |
| Subject: | Agricultural Data Mining | Computer Science | Crop Yield Prediction | Datasets and Data processing | Engineering and Technology | Remote Sensing - Agriculture | Remote Sensing Satellite Imagery | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Department of Computer Science, Cochin University of Science & Technology, Cochin, Cochin; 2022 |
| Fulltext: | Shodhganga |
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| 001 | 456403 | |
| 003 | IN-AhILN | |
| 005 | 2024-10-09 16:34:57 | |
| 008 | __ | 241009t2022||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_456403 |
| 040 | __ | |aCUST_682022|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aSarith Divakar, M|eResearcher |
| 110 | __ | |aDepartment of Computer Science|bCochin University of Science & Technology, Cochin|dCochin|ein|0U-0253 |
| 245 | __ | |aDesign and development of novel data analysis techniques for crop yield mapping using remote sensing satellite imagery |
| 260 | __ | |aCochin|bCochin University of Science & Technology, Cochin|c2022 |
| 300 | __ | |axii,139|dDVD |
| 502 | __ | |cDepartment of Computer Science, Cochin University of Science & Technology, Cochin, Cochin|d2022|bPhD |
| 518 | __ | |d2023|oDate of Award |
| 518 | __ | |oDate of Registration|d2017 |
| 520 | __ | |aTimely information about crop yield at the regional level is essential to ensure food security for the country. A decrease in arable land and crop yield variability due to climate change influences agricultural production and is a concern for the government, highlighting the importance of early crop yield prediction to develop strategic decisions on imports and exports. Crop yield forecasted using approaches like sample surveys are not feasible on a large scale, and results are available after harvest only. Prediction techniques using crop growth modelling based on the crop s phenological stages and environmental conditions are challenging as they have intensive data requirements. Statistical models were also used to map crop yield with climatic indices, fertiliser and soil information. However, the statistical model requires sufficient and reliable data for accurate predictions, while collecting soil and fertiliser data is expensive and unavailable for all locations. Remote sensing is an effective techniqu |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aAgricultural Data Mining |
| 653 | __ | |aComputer Science |
| 653 | __ | |aCrop Yield Prediction |
| 653 | __ | |aDatasets and Data processing |
| 653 | __ | |aEngineering and Technology |
| 653 | __ | |aRemote Sensing - Agriculture |
| 653 | __ | |aRemote Sensing Satellite Imagery |
| 700 | __ | |eGuide|aSudheep Elayidom, M and Rajesh, R |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/512663|yShodhganga |
| 905 | __ | |afromsg |
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