Abstract
Abstract. Assessment of horticultural crops under mixed cropping system has been a challenge, both for horticulturists and also to the remote sensing communities. But the recent developments in wide range of sensors onboard Unmanned Aerial Vehicles (UAVs) has opened up new possibilities in identification, mapping and monitoring of horticultural crops. This paper presents the results made from a pilot exercise on horticultural crop discrimination using Parrot Sequoia multi-spectral sensor onboard a UAV. This exercise was carried out in Nongkhrah village, Ri-Bhoi district of Meghalaya state located in the north eastern part of India having mixed horticultural crops. A two level hierarchical classification system was followed for identification and delineation of the major horticultural crops in the village. Parrot Sequoia multi-spectral sensor having four bands has been found to be effective in discrimination of horticultural crops based on variation in spectral response of six horticultural crops viz., pineapple, banana, orange, papaya, ginger and turmeric using three commonly used indices viz., Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI). NDVI and GNDVI showed nearly similar spectral response, whereas separability among the horticultural crops significantly improved with the use of NDRE. The first level of classification involving the five broad land cover classes has resulted an overall accuracy of about 91%, whereas the second level of classification for delineating the five selected horticultural crops has provided an overall accuracy of 79.8%.
Highlights
The horticulture sector has become one of the major drivers of growth in agricultural sector, and over last few years, India has witnessed a rise in horticulture production, which has even surpassed total production of food grains
Segmentation was followed by grouping of the homogenous segments to derive the broad landuse classes in the village (Aguilar et al, 2015 and Park et al, 2016). This is followed by digital classification of the horticulture class based on spectral response of the selected horticultural crops through generation of three important vegetation indices viz., Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRI) and Green Normalized Difference Vegetation Index (GNDVI) to observe the possibility for discrimination of the selected horticultural crops
When we considered the separability of the selected crops with the indices, it was observed that pineapple and the orange crops were closely placed in terms of NDVI and GNDVI
Summary
The horticulture sector has become one of the major drivers of growth in agricultural sector, and over last few years, India has witnessed a rise in horticulture production, which has even surpassed total production of food grains. UAVs enable users for many agricultural and horticultural applications such as crop acreage and production estimation (Stroppiana et al, 2015), growth and quality assessment (Thenkabail et al, 2002; Herwitz et al, 2004), generation of detailed map of vegetation assemblages at the species level (Schuster et al, 2012), crop stress detection (Carter, 1993 and Smith et al, 2004), damage assessment (Kim et al, 2002; Handique et al, 2016 and Zhang et al, 2002) etc. With the possibility of increased spatial and temporal resolution provided by UAVborne sensors, there has been a shift towards precision, or site specific, crop management activities with remote sensing inputs such as acreage estimation of multiple horticultural crops and to study within-field variability (Petrie & Walker, 2007 and Hunt et al, 2014). This paper presents the results and observations made from a pilot exercise on identification of horticultural crops in mixed cropping pattern using UAV-borne multi-spectral sensor
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