A Computer vision-based model for crop yield prediction using remote sensing data/ (Record no. 317323)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02203nam a22001937a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220606b |||||||| |||| 00| 0 eng d |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.87.K573 2021 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kiragu, Daniel Mburu |
| 245 ## - TITLE STATEMENT | |
| Title | A Computer vision-based model for crop yield prediction using remote sensing data/ |
| Statement of responsibility, etc | Daniel Mburu Kiragu |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Nairobi: |
| Name of publisher, distributor, etc | Strathmore University; |
| Date of publication, distribution, etc | 2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xv, 76p. |
| Other physical details | ill. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Arguably, crop yield data forms the most important measure of crop productivity in agriculture. With adequate crop yield data, local and international bodies can develop effective agricultural policy leading up to sustainable food supplies and elevated food security. However, timely acquisition of crop yield data can be a cumbersome task as existing crop yield prediction approaches face numerous challenges. In this study, these challenges are identified as high cost and high dimensionality of data required for the prediction activities as well as limited scaling of the resultant prediction models. In efforts of overcoming these challenges, this study leveraged an alternative source of data to design and develop a cheap, accurate and scalable deep learning model using convolutional neural networks. Satellite imagery datasets were used as the primary and only source of data for training the model. This benefited the study in two major ways. Firstly, off, the approach automatically took care of the high dimensionality problem as demonstrated in the GEMS data. Second, satellite imagery data is readily available globally, a factor that greatly reduced the costs needed to collect real-time data for the study. Validation of the developed model was done using 10% of the overall dataset acquired. Reliability of the model in performing crop yield predictions was captured using an MSE loss function for each epoch trained. Cumulatively, the model achieved an MSE loss score of 3.6. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep Learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Convolutional Neural Networks (CNNs) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Crop Yield Prediction |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Butime, Julius |
| Titles and other words associated with a name | [Dr.] |
| Relator term | Supervisor |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://su-plus.strathmore.edu.ezproxy.library.strathmore.edu/handle/11071/12756 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Library of Congress Classification |
| Item type | Thesis |
| CIN (SU) | NI |
| Course Code | MSc Information Technology |
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