Pest Detection in Cropping Systems of Indo Gangetic Plains Through Remote Sensing - Lecture Note - Lecture Material
Pest Detection in Cropping Systems of Indo Gangetic Plains Through Remote Sensing
V. K. Bansal, Avadhesh Kumar Koshal
Sr. Research Fellow
PDCSR, Modipuram, Meerut

The major cropping systems are rice-wheat, rice-mustard-rice, cotton-wheat, pearl millet-wheat, rice-sugarcane and maize-wheat in Indo- Gangetic Plains of India. The Indo Gangetic plains regions are divide in four parts viz. Trans Gangetic Plain, Upper Gangetic Plain region, Middle Gangetic Plain and Lower Gangetic Plain region. The crops are rice, wheat, maize, cotton, sugarcane and pearl millet. These crops are grown in large areas of IGP in rabi and kharif seasons.

The cropping systems are largely affected due to pest. The major pests viz. aphid, stem fly, seed fly, Thrips, Shoot fly (Atherigona soccata), pod borer, moth and ball worms are affected seasonal crops. Due to affect of pest, adverse affected on the crop production and yield. Remote sensing technology plays a great role to prevent and control and management of pest. The insect affected crop and normal crops are give different tonal variation in imageries. The LISS III and LISS IV imageries are very helpful for detection of affected area. The affected areas are identified with the help of GPS (LEICA Differential GPS). The GPS give exact location and position on the imagery. The image interpretation keys shape, size, tone, texture and association help to detection to visual on the imagery and confirmation during ground survey. After image analysis found the great variability in tonal variation on the imagery. Normal crops give red, bright red and dark red with smooth texture but pest affected area give pink, yellow and yellow pinkish red colour with irregular shape and rough texture. Remote sensing technology help to season wise analysis of pest in affected area and help to control through crop rotation, if possible and through chemically.

1.0 Introduction
The different types of information on natural resources such as soil survey information, land use, vegetation information etc. can be extracted from the same satellite data. Remote sensing as a tool will give information regularly and at a low cost to enable timely reclamation. In India, production forecasting of certain crops, crop yield modeling and crop stress detection are done using remote sensing data. The first satellite remote sensed data became available in 1972. Therefore, we had the opportunity to get an overall synoptic view only after 1972. Currently a number of spacecraft imaging systems are operating using remote sensing sensors. Some of the current image systems from spacecraft platform include Indian remote sensing satellite (IRS), SPOT and IKONOS etc.

The variation between signatures, when the bands are assigned the primarily colours of red, blue and green, one can visualize the image as a colour one. Vegetation appears red in such a colour composite where NIR band is assigned as red, due to high reflectivity of vegetation in this band. This is typically termed as False Colour Composite (FCC).

The temporal IRS data were screened to select cloud free data during winter and summer season. Care was taken to select data pertaining to the period when crop coverage was with full foliage. The villages appear bright while water bodies had low backscatter and appeared in dark black colour. This was used to map the villages and water bodies using a decision rule classifier. All these two classes: villages and water bodies were combined and made a mask. The rest of the area was considered as potential agricultural area.

Detection and identification of plant diseases and planning effective control measures are important to sustain crop production. One of the potential applications of remote sensing technique in agriculture is the detection of plant disease an extensive areas before the symptoms clearly appear on the plant leaves

This has been clearly demonstrated for the first time in remote sensing studies on root wilt disease on coconut plans in Kerala under a NASA-ISRO-IARI collaborative programme (Dakshinamurti, 1969 and Dakshinamurti et al., 1970). Healthy plants give a higher reflectance in the near-infrared region and a lower one in the visible region. Diseased plants show a higher reflectance in the visible spectrum and a lower one in infrared region. This principle can be used in distinguishing healthy and diseased vegetation and assessing vegetation damage due to diseases.

Crop intensification is a viable proposition as far as India is concerned as it is endowed with favorable climatic condition for growing a diverse range of crops practically through out the year (Panigrahy and Manjunath, 2003). This concept of looking to the agricultural land as a system is known as the cropping systems approach that seeks to sustain the increase in the food production by judicious combination of crop intensification and yield improvement. Cropping pattern is defined as the yearly sequence and spatial arrangement of crops or of crops and fallow on a given area. The cropping pattern is categorized on the basis of crop season as Kharif, rabi and summer season cropping pattern. Growing two, three and multiple cropping patterns. The productive base of cropping system is plant growth, which is influenced by management and environment. Management here includes all those components associated with crop production. These include, crop area, crop biomass, crop rotation crop calendar, crop biomass, time and spread of sowing and harvest. Environment is the invariant resource of soil, physiography and climate.

2.0 Study area
IGP region divided in four agro climatic regions TGP (Trans Gangetic Plain), UGP (Upper Gangetic Plain), MGP (Middle Gangetic Plain) and LGP (Lower Gangetic Plain). These four zones are divided in 16th sub zone (Table. 1 and Fig.1). The Indo Gangetic Plain region of India has total geographical area of about 47.4 million hectares (Pandey et al. 2006).

The rainfall ranges from high as 3000 to low 300 mm. Administratively, the states are divided out of 203 but only 173 districts are belong to IGP and encompasses the entire states of Punjab, Haryana, Uttar Pradesh, Bihar and West Bengal. The major rivers of IGP are Ganga, Yamuna, Satluj and Ghagra. Soils of this region are loamy to sandy loam. The percentage of the organic carbon is enough to other region. The study was confined to Indo Gangetic Plain Region. These four agro climatic regions as defined by MOA (1997) and Agro- climatic Zones of India Planning Commission Report (2005) were further classified into 16 different sub regions. Three cropping seasons namely viz. Kharif (June to September), Rabi (October to February) and Zaid (from March to May) were taken into consideration.

3.0 Material and methodology
  1. IRS IC /1D LISS III (FCC) digital data
  2. District maps and state maps
  3. Indian Agro climatic zone map
  4. Global Positioning system (TOPCON and GARMIN) and
  5. Performa for field data information
These data and instruments were used in the study.
The methodology (Fig.2) consists of the study and analysis of remote sensing data, collection of ground truth information, collection of cropped and pests of field data and creation of data base for all the required physical and cultural parameters and overlying, integration and analysis of all spatial, non spatial and attribute data bases.

3.1 Methodology
Analysis of remotely – sensed data in different forms and formats was analyzed after examining the suitability of various products. The study proceeded as follows:
  1. Preliminary study of the IRS IC /1D LISS III – FCC.
  2. Study of false colour composites on digital (onscreen) or paper prints
  3. Preparation of image interpretation keys
  4. Collection of ground truth with GPS and
  5. Preparation final output map
The digital remote sensing data were processed and analyzed using the ERDAS IMAGINE 8.6 software. Initially, the digital data of scenes were geo-referenced. The Kharif and rabi season images were considered as the base since it is very sharp and clear. This image is first geo referenced by taking various control points from the SOI toposhits. The projection type used is ‘polyconic’ with the spheroid and datum as ‘modified Everest’. Subsequently, image- to- image registration was performed in order to register the second image and so on. Some sharp and easily identified features like crossing of roads, railways, canals, bridges etc. were located on both images and were selected as ground control points. Some GCP’s with rms error beyond threshold were deleted and replaced by other points so as to maintain acceptable positional accuracy.

3.1.1 Preparation of questionnaires
The necessity for acquiring such as information was felt on the basis of secondary information and the type of information acquired by direct observation during the reconnaissance field visit.

3.1.2 Preparation of Base map
The base maps indicating permanent and artificial features such as canal, highway, roads, railway, irrigation network, and districts boundary were prepared in ARC GIS 8.3 software. For this, Survey of India sheets were used as reference materials. The permanent fixtures were transferred in digital format through digitization.

3.1.3 Survey of the area/Reconnaissance
Surveyed of the area season wise in a year viz. kharif (June), rabi (January) and zaid (April). Reconnaissance of the area of IGP’s states, collected normal and pest affected crop samples and related information along with GPS points in different fields. Global Positioning System burgeoning technology, which provides unequalled accuracy and flexibility of positioning for navigation surveying and GIS data capture. The GPS uses satellites and computers to compute position any where on the earth. GPS viz. TOPCON, GARMIN and Differential GPS are very good for agricultural purpose.

3.1.4 Collection of ground truth information
Field visits were made to establish the relationship between the image elements, namely colour, texture, pattern, shape, size, association, etc. and the existence of normal crop and pest affected crop. Initially, reconnaissance traverse of the area was made to assess the trafficability and to precisely located sample areas, the pockets of land which were interpreted as normal as well as pest affected crop were precisely located on the ground with the help of topographical sheet and observations with respect to terrain conditions. Observations were, however, also made outside the sample areas randomly, in order to validate the relationship already established between image elements, and normal and pest affected crop areas.

During surveyed collected crops and pests information 200 points with geo coordinate information these information overlaid on the imagery and observed visual as well as digital.

4.0 GIS and Remote sensing analysis for cropping systems

4.1 GIS analysis for cropping systems
The districts polygon coverage were digitized for the IGP region in India covering the states of Punjab, Haryana, Uttar Pradesh, Bihar, West Bengal, and a part of Rajasthan in ARC GIS system. A database was developed for analysis of cropping systems in MS Excel, converted dBASE IV format then finally converted in .shp file to overlay analysis in ARC GIS as well as ERDAS IMAGINE. Agro climatic zone, sub zone, districts and states information were taken from planning commission report. The coverage has been generalized based on the yield in to two classes, i.e. Normal crop and Pest-affected crop depending upon the range of crop survey data.

4.2 Remote sensing analysis for cropping systems
The post fieldwork consisted of modification of preliminary interpreted mapping units on FCC of satellite data in the light of field information data. The tentative legends prepared also finalized. The normal crop and pest affected crop areas; training sets were finalized on the computer screen with the help of GPS (latitude/longitude) points.

4.2.1 Visual analysis of FCC’s
The tonal variation and with the help of image interpretation keys identified the normal and pest effected crop area. During the image interpretation different crops have different tonal variation so it is very essential to study very precise and have to keep mind pre information about the area. The pest-affected areas are given the different tonal variation in the normal crop area. The normal crop give red, bright red and dark red colour with smooth texture in regular form but pest affected areas give pale yellow, yellowish red and dull red or pink colour, these tonal variation mixed with other crops.

The tonal variation varied crop to crop in different season and in different zone. Our concentrates were detecting the pest-affected area identified after observation (Fig.4).

4.2.2Digital Image Enhancement:
Enhancement is the modifications of an image alter its impact on the viewer. This improves the visual interpretability of an image by increasing the apparent distinction between the features in the screen.

Histogram equalization is one of the techniques of non-linear contrast enhancement. Histogram equalization (uniform Distribution Stretch) provided maximum contrast for identifies normal and pest affected area. Filter algorithms for performs image enhancement, they suppresses certain frequencies (de-emphasize) and pass (emphasize) other frequencies. In the present study the following image enhancement were performed viz. linear enhancement and non-linear enhancement etc.

4.2.3Image analysis
The geo- referenced digital data of IRS LISS III sensor for the two different seasons (Kharif and Rabi) were used to prepare the land use/ land cover maps for IGP. The digital image processing techniques used for land use/ land cover classification includes (a) image enhancement (b) image classification. Digital analyses for land use/ land cover classification were carried out using the image processing software- ERDAS IMAGINE 8.6. Based on ground truth data, training sets are identified for various land use/ cover classes. Using these training sets, the land use/ land cover classes were delineated employing maximum likelihood classifier in ERDAS IMAGINE. The thematic maps of land use/cover for both the scenes as well as the pest-affected crops were merged to prepare the land use/cover maps of IGP. Various land use/ cover classes delineated are built-up area, villages, water bodies (ponds), road, forest, railway line, and river and sandy area.

The tonal variation and with the help of image interpretation keys identified the normal and pest effected crop area. During the image interpretation different crops have different tonal variation so it is very essential to study very precise and have to keep mind pre information about the area. After this process prepared the digital classifications, run the un- supervised (ISODATA) and supervised classification (Maximum likelihood classification).

After run the supervised classification two broad classes are observed (1) normal crop and (2) pest affected crop areas.

(1) Normal cropped area:
In Kharif sesason’s imageries identified major crops are rice, maize, bajra, jowar, Pigeonpea, green gram, black gram, groundnut, sesamum, lahi, and berseem. In rabi sesason’s imageries major crops are wheat, sunflower, barley, potato, chickpea, pea, mustard, oat and sesamum identified. The normal crop gives red, bright red and dark red tone with smooth texture in regular form
After analysis IRS imageries observed season wise like that in Kharif season rice give dark red colour, sugarcane give bright red colour, in rabi season wheat give red colour pulse give red pink colour so different types give red tonal variation on the imageries.

(2)Pest affected cropped area:
Pests are affected in both seasons (rabi and Kharif) on the different crops (Table-2). The major pests are in crops (1) rice- hispa, gundhi bugs and jassids (2) cotton- boll worms, leaf roller and whitefly (3) maize- grass hopper and thrip (4) pearl millet- midge fly and termite (5) sugarcane- top borer, pyrila and mealy bug (6) chickpea- pod borer, bruchids and cutworm (6) jute- leaf beetle and caterpillar affected in Kharif season.

The major pests are in crops (1) mustard- aphid and painted bug (2) pigeon pea- pod bug and pod fly (3) barley- stem borer and grasshopper (4) wheat- termite, thrip, and gujhia weevil (6) sunflower- capitulum borer (7) berseem- black ants (8) potato- aphid, white grub and tuber moth affected in rabi season. The pest affected areas give pale yellow, yellowish red and dull red or pink colour, these tonal variation mixed with other crops.

(3) Major cropping systems in IGP
The cropping systems livelihood are based but pest affected the crops and diseased yield and production after survey analysis of data zone and sub zone wise the dominant cropping system observed.

The major cropping systems were observed after data analysis of zone wise information of the IGP (Table-1 and Fig.1). The zone wise cropping systems are in (1) TGP- rice-wheat, cotton- wheat, maize wheat and cotton berseem (2) UGP- rice-potato, maize-potato, maize-wheat and pigeon pea- wheat (3) MGP- rice-lentils, rice potato and jute –rice-wheat and (4) LGP- rice –rice-mustard, rice-potato and rice-wheat-jute.

In the IGP, the maximum area was covered by rice –wheat-cropping system. In the Lower Gangetic Plain, rice-wheat cropping systems have lesser area because of boro rice is major crop. The cropping system is also changing in TGP, the south-west Punjab areas (Bhatinda and Muktsar districts) cotton belt converting in to rice belt. These changing are also due to cotton bollworm problem. Haryana and to some extent in western Utter Pradesh owing to high water table during monsoon season, rice has become a major crops in such areas.

5.0 Conclusions
Remote sensing technique is very useful, less time consuming and cost effective in delineation of pest-affected areas. The GPS, DGPS, LAPTOP and other sophisticated instruments give the very precise information and remote sensing software ERDAS IMAGINE play a vital role and reliable information to making pest control program and identified the pest affected area of the IGP. Immediate attention needs to be paid for control the pest affected area and prevent affected areas through chemical. This would facilitate enhancing the area under rabi and Kharif cultivation and increasing agricultural production, for better livelihood of the people.

The pest largely affected crop area and control is essential before the effect so remote sensing technology play a great role to control pest affect the area. It gives very good information before if precisely identified the cyclic study the remote sensing data of the area. The interchange the cropping systems also help to control the pest problems.

The major pests are observed after data analysis, in kharif season-gundhi bug, hispa, jassids, bollworm, grass hopper, thrips, top borer, pyrilla, aphids and white grub; in rabi season –termite, cutworm, whitefly, tuber moth, hairy caterpillar, hooded hopper, hawk moth, painted bug and gall fly (Singh et al. 2003).

IRS 1C/D, LISS III images given the area information very accurately. After visual interpretation run different algorithm and digital classification these data given valuable information of this area. Most of the growing cropped area some field are badly affected due to pest. Interpretation keys play very important role in the visual interpretation of image for identification of normal and pest affected areas. Due to pests, crops have poor growth and poor yield.

6.0 References
  • Agro- climatic Zones of India Planning Commission Report (2005). National Bureau of Soil survey and land use planning, Nagpur.
  • Dakshinamurti, (1969). Remote sensing and earth resource survey. J. Post Graduate School, IARI, New Delhi. 7(1&2): 140-161.
  • Dakshinamurti, C., Krishnamurthy, B., Summanwar, A.S., Shanta, P. and Pisharoty, P.R. (1970). Preliminary investigations on the root wilt disease in coconut plants in Kerala state (India) by remote sensing technique (a NASA-ISRO-IARI-CCRS Project). XXIst International Astronautical Conference of the IAF. Germany.
  • MOA, 1997. Indian Livestock Census, Summary table, Vol. 1. Directorate of Economics and Statistics, Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India, 1997.
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