Crop Discrimination using Non-Imaging Hyperspectral Data
Pooja Vinod Janse1, Ratnadeep R. Deshmukh2
1Pooja Vinod Janse*, Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad(Maharashtra), India.
2Ratnadeep R. Deshmukh, Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad(Maharashtra), India.
Manuscript received on June 10, 2021. | Revised Manuscript received on June 14, 2021. | Manuscript published on June 30, 2021. | PP: 269-273 | Volume-10 Issue-5, June 2021. | Retrieval Number: 100.1/ijeat.E28020610521 | DOI: 10.35940/ijeat.E2802.0610521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Crop type discrimination is still very challenging task for researchers using non-imaging hyperspectral data. It is because of spectral reflectance similarity between crops. In this research work we have discriminated between four crops wheat, jowar, bajara and maize. We have tried to overcome the problems which have been faced my researchers. Initially by visual analysis we have selected 22 reflectance band which shows the absorption property of particular molecules and classification techniqueis applied, but it has given us very poor result of classification. We observed only 24% classification accuracy. So we considered nine vegetation indices along with spectral bands and achieved better classification accuracy. ASD FieldSpec 4 Spectroradiometer device is used for capturing spectral reflectance data. We calculated nine different vegetation indices and some selective reflectance bands are used for crop classification. We have used Support Vector Machine (SVM) for classification.
Keywords: Crop Discrimination, ASD FieldSpec 4 Spectroradiometer, Support Vector Machine, Vegetation Indices
Scope of the Article: Support Vector Machine (SVM)