The North-Western Indian States and the North-Eastern Indian States of Indian Himalayan Region (IHR) are rich of various temperate horticulture fruits such as the Apple, Pear, Peach, Plum, Apricot, Sweet Cherry and Sour Cherry. These horticulture fruits are majorly grown in North-western region comprising of Jammu and Kashmir (J&K), Himachal Pradesh (H.P.) and Uttarakhand (U.K.). These states of IHR share the same type of geographical and climatic condition and having nearly common flora and fauna. Out of the various horticulture temperate fruit crops apple and apricot have the potential to make a positive impact on economy of these states. Hyper-spectral remote sensing due to its capability of identifying the small variations within a particular feature (or land cover) is an important tool for discriminating or mapping the specific land cover among the various existing classes. Contrary to multispectral remote sensing, it is not only capable of mapping the vegetation class among the various classes in the land but also has the potential to discriminate within the different classes of vegetation as well as diseases identification within a class. This specific class level discrimination of vegetation is an important tool for mapping. In hyper-spectral remote sensing this variation is observed through the possible discrimination of spectral signatures of various vegetation classes. Thus, due to its fine spectral bands this type of remote sensing data has the potential to map the horticulture crops. However, the processing of hyper-spectral data always require the in-situ measurements or existing spectral library. Such a type of spectral library is never generated for the horticulture crops of IHR. This can be further useful for identifying the disease affected crops and input for developing model for estimation of biophysical and biochemical parameters. Therefore, in this study, a need for the development of spectral library for temperate horticulture crop has been highlighted. Further, a methodology for the processing of hyperspectral data has also be proposed.
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