Literature Review
1.4. Application of Remote Sensing in Tea Sector:
Remote sensing has been in used for quite a long time. Remotely sensed data helps in identifying the vegetation status and allows the users to take appropriate actions at the appropriate time. Lot of research work has also been carried out in the agricultural sector from crop monitoring to damage assessment. But it was observed that very less work has been done in the tea sector. As such few research works have been carried out in the tea sector using remote sensing and GIS.
As tea is a very important beverage, from both management and commercial point of view, an attempt has been made to predict tea yield using remote sensing and GIS and other key parameters in the GIS environment (Tripathy et.al).
Leaf area index (LAI) is one main key factor useful in crop growth models that may be derived from optical remote sensing data. LAI plays an important role in both the processes of crop growth and canopy reflectance (Clevers et. al., 1994). The normalized difference vegetation index (NDVI) is used as a measure of plant productivity (Sellers, 1985). It has also been considered as a measure of LAI for most of crops (Gong, et. al., 1995). So NDVI-LAI could be used to study the vegetation status of the crop.
There have been many attempts to estimate LAI using various types of remote sensing data since the early stage of space remote sensing (Badwhar et al., 1986; Peterson et al., 1987; Turner et al., 1999). Remote sensing estimation of LAI has been primarily based on the empirical relationship between the field-measured LAI and sensor observed spectral responses (Curran et al., 1992; Peddle et al., 1999). As a single value to represent the remotely sensed spectral responses of green leaves, spectral vegetation indices, such as normalized difference vegetation index (NDVI) or simple ratio, are frequently used to indirectly estimate LAI. Normalized difference vegetation index (NDVI) has been a popular index with which to estimate LAI across diverse ecosystems. Recent studies have shown that the NDVI may not be very sensitive to values of LAI in particular at the forest ecosystem having the close canopy condition that the LAI value is relatively high (Chen and Cihlar 1996, Turner et al. 1999). Since NDVI and are obtained from satellite data (Gardner et.al., 1988 and Baret et.al., 1991) the distinction of vegetation and quantitative assessment for it’s growth stage and the estimates of it’s carbon absorption and evapotranspiration (Nimani et.al., 1989 and Ebisu, 2000) become possible (Ogawa, et.al., 2000). Using optical and radar satellite data, crop specific LAI could be obtained for growth monitoring and modelling (Guissard et.al., 2005).
It has also been found that spectral characteristics of tea plants are very important for monitoring the tea plantations by remote sensing. A study was carried out in Sri Lanka where LAI of the tea canopy and spectral reflectance of different types of tea clones for different pruning ages were studied in fifty tea fields in the estate and an empirical model between NDVI and LAI of tea canopy was developed (Rajapakse et. al., 2001).
An attempt has also been made to develop a GIS anchored web enabled Decision Support System (DSS) for tea enterprises, introduction of precision farming, user friendly IT framework for collection of spatiotemporal data and efficient and smart Enterprise Resource and Planning (ERP) package for decision support at all levels for better management and profitability (Ghosh et.al., 2004).
Some work has already been carried out in West Bengal, India where remote sensing has been used for finding the ground water availability in the tea growing areas of Terai Region. The study has revealed that satellite remote sensing combined with other conventional data has great potential for ground water exploration (Duarah et.al. 1993).
It is observed that water management is an important factor for augmenting the productivity of perennial crops like tea. A study was carried out on the landuse and ground water potential through remote sensing technique in the Darrang and Sonitpur District of Assam (Bordoloi et.al., 1994).
Attempt has also been made to use porphyrin derivative, n-tetraphenyl porphine manganese (111) chloride, thin films for detection of tea aroma using optical fibre reflectance with three different colour LED’s i.e., red, yellow and green as the light sources (Akrajas et.al., 2000).
Researches have also been carried out on determining the tea quality by using neural network based electronic nose. Metal oxide sensor based electronic nose (EN) have been used to analyze five tea samples with different qualities in oven. The metal oxide used has electronic resistance with partial sensitivity to headspace of tea. The data were processed using Principal Components Analysis (PCA) and Fuzzy C Means algorithm (FCM). It was found that EN was able to discriminate between the flavours of tea manufactured under different processing conditions (Dutta et.al.)
The electronic nose (ER) was also applied for aroma characterization of orthodox black tea where orthodox samples were tested using Alpha MOS 2000 Electronic Nose and data obtained from the experimental setup have been classified using PCA and Black-propagation Multi Layer Perceptron model (Bhattacharyya et.al.).
Experiments have also been conducted for the measurement of plural tea information using fibre-optic sensor. Fibre reacts to various signals like temperature and pressure. The plural tea information using the multi-functional technique was measured with optical fibre (Zhu et.al. 2004).
Attempts have also been made to model the influence of irrigation on potential yield of tea where the CUPPA Tea model was validated against the yield data from irrigation experiments carried out on contrasting soil types at Siliguri and Tezpore regions of North-East India (Panda et. al., 2002).