Hyper Spectral Measurements as a Method for Potato Crop Characterization
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Hyper Spectral Measurements as a Method for Potato Crop Characterization |
| 2. | Creator | Author's name, affiliation, country | Mohamed Aboelghar; Agriculture Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS) El-Nozha El-Gedida, Cairo; Egypt |
| 2. | Creator | Author's name, affiliation, country | Sayed Arafat; Agriculture Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS) El-Nozha El-Gedida, Cairo; Egypt |
| 2. | Creator | Author's name, affiliation, country | Eslam Farag; Agriculture Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS) El-Nozha El-Gedida, Cairo; Egypt |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | Hyper Spectral Data, Potato Discrimination |
| 4. | Description | Abstract |
The main objectives of this research is to determine the optimal hyperspectral range and waveband/s in the spectral range of (400–2500 nm) to discriminate between four different varieties of Potato crop (Diamond, Everest, Mondial and Rosetta) that are cultivated in old and newly cultivated lands of Egypt and to propose detailed spectral reflectance characterization for these four varieties which will enable more accurate surveying of these varieties through satellite imagery. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the crop. An average of thirty measurements for each variety was considered in the process. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400–2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD analysis was used to choose the optimal spectral zone that could be used to differentiate between the four varieties. Then, linear discrimination analysis (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each variety could be spectrally identified. The results of Tukey’s HSD showed that NIR is the best spectral zone for the discrimination between the four varieties. The other five spectral zones showed close spectral characterizations between at least two varieties. The results of (LDA) showed the optimal waveband to identify each variety. These results will be used in machine learning process to improve the performance of the existing remote sensing software's to estimate potato crop acreage. The study was carried out in AlBuhayrah governorate of Egypt. |
| 5. | Publisher | Organizing agency, location | |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2013-06-04 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | http://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-97 |
| 11. | Source | Journal/conference title; vol., no. (year) | International Journal of Advanced Remote Sensing and GIS; Volume 2 (Year 2013) |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
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