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- W4200434668 abstract "The tradition of depending on plant derived products for medicinal purposes evolved several years ago as a part of human culture. Recent advancements in technology have a positive impact on medicinal crop production monitoring and mapping. In this study an innovative approach of ‘individual sample as mean’ based fuzzy modified possibilistic c-means classification technique was experimented for mapping Isabgol (Psyllium husk) medicinal plant. Five indices- CBSI-NDVI, NDVI (NIR-Red), NDVINRE1 (NIR-Red edge 1), NDVINRE2 (NIR-Red edge 2) and NDVINRE3 (NIR-Red edge 3) as input to fuzzy machine learning algorithm were tested using Sentinel-2 data. While optimum temporal data were selected using separability analysis on indices data. Handling of heterogeneity within class has been directly observed in the form of variance parameter, which was reduced from 0.022 to 0.020 for CBSI-NDVI when ‘Individual sample as mean’ based approach is used. Test fields of Isabgol were found to be close to training fields, while monitoring MMD values with training and testing Isabgol fields, which were too low (0.044 for CBSI-NDVI). Whereas, MMD between the training field of Isabgol and other crops were too high (0.688 for Cumin, 0.644 for Mustard, 0.307 for wheat, 0.278 for Castor and 0.115 for Fenugreek) indicating other crops were well separated." @default.
- W4200434668 created "2021-12-31" @default.
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- W4200434668 date "2022-01-01" @default.
- W4200434668 modified "2023-10-16" @default.
- W4200434668 title "Handling heterogeneity through 'individual sample as mean' approach – A case study of Isabgol(Psyllium husk)Medicinal crop" @default.
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- W4200434668 doi "https://doi.org/10.1016/j.rsase.2021.100671" @default.
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