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meant for nitrogen i.e. VI and GRI (Inoue et al., 2008). From the results, it is confirmed that the proposed indices VI1 and VI2 were effectively able to differentiate the level of nitrogen treatments for most of the varieties except for one or two rice varieties. Whereas, the existing indices were not able obtain the difference between the rice varieties except for two or three varieties. It can be concluded from quantitative analysis of rice genotypes, VI2 can be applied functionally for the assessment of nitrogen stress in rice agriculture system. Each level of nitrogen applications was found effective and different for different varieties in an Indian rice agriculture system by using the advanced vegetation indices rather than the existing indices.
Thus, in future study, the potential of presently developed vegetation indices will be helpful for mapping of nitrogen treatments in rice crop by employing hyperspectral imagery obtained from space platform.
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5 Discrimination of Paddy Crop Species using Advanced Clustering Technique
Introduction
Population of the world is growing rapidly day-by-day, so is the food demand. As a major portion of world population relies on rice, its consumption is also increasing very fast. In that respect, achieving greater productivity is of vital importance for which rice species information is progressively receiving keen attention towards real-time assessment of paddy crop yield. To improve rice productivity over large area of cultivation, enhanced soil-water- fertilizer-crop management practices at species level within the field is very crucial. Nitrogen is an essential element for plant growth. Plant intakes nitrogen fertilizer as per its requirement not in excess. Therefore, application of excessive nitrogen fertilizer will lead to ground water contamination (Jain et al., 2007; Jaynes et al., 2001). In this context, optimizing the application of fertilizer is very much essential in paddy cultivation so that fertilizer is not wasted resulting in reduced environmental pollution and water quality degradation. It can be possible only when one has a primary knowledge on crop species’ fertilizer requirement. Traditionally, the most common methodology of agricultural crop species detection is visual investigation, which has several limitations like it is time-consuming, inaccurate, site specific and labour- intensive. Remote sensing techniques have proven to be very cost-effective, non-destructive and efficient in the management and classification of different agricultural crops, weed species and other plants within a field. In recent years, several research studies (de Castro et al., 2012;
Jorgensen et al., 2006; Pena-Barragan et al., 2011) have implied that remote sensing, particularly hyperspectral remote sensing, is more suitable for agricultural research studies as
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it has the flexibility to identify minor changes in crop characteristics. Moreover, this technique is capable of guiding the farmers to apply agricultural inputs to the rice plants with a prior knowledge on the paddy crop species, which is the central idea behind precision rice agriculture.
In past few decades, a number of studies carried out by some researchers have focused on the relation between spectral reflectance and different crop species (Liu and Bo, 2015), classification of crops (Boitt et al., 2014; Wardlow et al., 2007), discrimination of weed species (Borregaard et al., 2000; Rumpf et al., 2012), separation of cultivars levels of rice, wheat and cotton (Blackmer and Schepers, 1995; Feng et al., 2008; Filella et al., 1995; Kong et al., 2013; Nguyen and Lee; 2006), irrigation levels of potato crop (Kashyap and Panda, 2003; Onder et al., 2005), identification of different vegetated species together (Bunting and Lucas, 2006; Cochrane, 2000) and also discrimination of rice panicle (Liu et al., 2010). In these studies, various methodologies have been adopted such as support vector machine analysis, principal components analysis, discriminant analysis, multiple least square regression method, neural network techniques. It is reported that visible, red-edge, NIR regions of the spectrum are responsible for crop discrimination, but they vary significantly with location and crop type. On the other hand, all these studies emphasized only on a specific field condition and growth stage. However, it is observed from the paddy crop field that with fertilizer application the phenology of crop species changed significantly throughout the growth stage.
Therefore, this study has increased attention in defining the most suitable narrow bands to discriminate rice genotypes subjected to different nitrogen treatments and no nitrogen treatment at two different stages during the growing season using field hyperspectral remote sensing. It is very difficult to distinguish the paddy crop species with naked eye in their early stages, as they look very similar. Here the differences are very small to be discerned with ordinary aerial or multispectral satellite imagery. They cannot be captured through high spatial multispectral aerial images because of their wider spectral widths. So, hyperspectral in-situ data is used to accurately discriminate between visually similar rice varieties using waveform classification technique. Identification of narrow spectral bands is the key feature in rice species discrimination. The aim of the study is to acquire the spectral response of paddy crop
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species at canopy scale, which is unique. This signature can be used to measure the differences in reflectivity for different rice varieties either for real-time monitoring or for rice map creation. Moreover, in-situ signatures should be free from noise so as to build pure spectral libraries for remote sensing. Here, an attempt has been made to get pure spectral library along with powerful discrimination through waveform classification technique. Besides these, the present study attempted to identify the most suitable narrow bands in the electromagnetic spectrum sensitive for rice species discrimination.
Data description
For this study, ground based hyperspectral measurements were taken from the experimental station of the Regional Rainfed Lowland Rice Research Station, Assam (Study site 1) with the utilization of an ASD hand held portable spectroradiometer having a spectral range of 350- 1050 nm. Two sets of in-situ hyperspectral measurements were taken from the same study site in two different years. One set was taken from 72 plots covering eight rice varieties prior to different nitrogen applications in 2009. The other set was taken from 24 plots comprising 24 rice varieties, which are most commonly cultivated Indian rice varieties with different crop developmental age, with no nitrogen treatment in 2014. The study area and data collection have been described in detail in Chapter 3. The details of presently used hyperspectral ground based data are given in Table 5.1.
Table 5.1 : Details of ground based hyperspectral measurements
Dates of Observation Crop Stage Rice Genotypes Plots 03/04/2009
10/04/2009 17/04/2009 24/04/2009
Fully vegetative stage 8 72
21/05/2014
31/05/2014 Pre-vegetative stage* 24 24
*Pre-vegetative stage covers tillering and panicle initiation phase.
Methodology
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Experimental measurements have been assessed to characterize the canopy reflectance properties in a rice agriculture system accompanied with various rice species and nitrogen treatments. Rice species discrimination with different nitrogen treatments and extraction of pure rice spectra for the development of paddy crop spectral library are presented.