IAPRS & SIS, Vol.34, Part 7, *Resource and Environmental Monitoring", Hyderabad, India, 2002 
  
  
  
  
  
  
  
  
  
  
  
   
   
  
  
   
    
  
  
   
    
   
  
   
   
   
   
    
  
  
  
   
    
  
  
  
  
  
  
TABLE - 1 
A COMPRATIVE ASSESSMENT OF THE 
SUGAR MILLS IN DEORIA DISTRICT IN 2001- 
2002 
SI. |Sugarcane| Nature | Crushing | Payment |Pending 
No.| mills [of mills of made by [payment 
sugarcane | the mills 
(in lac to the 
met. tone [farmer (in 
: lacs) 
1. |Baitapur | Govt. 10.23 Rs. Rs. 
sector 328.92 | 601.06 
(35%) (65%) 
2. | Deoria | Govt. 2.99 B Rs. 
sector 15.60 1259.38 
(5%) (9590) 
3. | Bhatni | Govt. 2.37 Rs. Rs. 
sector 4.59 [218.56 
(2%) 1(98%) 
4. Gauri | Govt. | Closed since last five years 
bazar | sector 
5. |Pratappur | Private | 23.04 Rs. Rs. 
sector 1816.58 | 274.45 
(86.5% (13.590) 
  
  
  
  
  
  
  
  
(Source : District sugarcane office Deoria U.P.) 
The second most important factor is non-adaptation of improved 
varieties of sugarcane by the farmers, as recommended by officers 
of district sugarcane department and agriculture scientist. This 
may be attributed to lack of awareness and inability to purchase 
high yielding variety. 
On the other hand farmers in majority purchase low vigor non- 
descript local varieties which results in low sugar recovery despite 
rendering hard labour and engagement of field for more than 
eighteen month resulting in further deterioration of socio- 
economic condition of the farmers. Major details of high yielding 
varieties are given below. Table — 2 
  
  
TABLE -2 
SUGARCANE VARIETIES AT A GLANCE IN 
DEORIA DISTRICT 
Sl. | Time of | Early maturing | Mid & late 
No. | planting | varieties maturing 
varieties 
  
1. Jan-Feb [CoS 88230, CoSCoS 8432, CoS 
05255 CoS 8436, CoSj767 CoS 92423, 
98231 : CoS 91230 
2. | Sep-Oct | CoS 88216, CoJ D.P. 39, CoS 
64, CoS 87216 86218, Cos 52263, 
oS 90269 
For waterlogged situations 
3. | Sep-Oct | U.P. 9530, U.P. 
9529, CoS 96436 
(Source : District sugarcane office Deoria U.P.) 
  
  
  
  
  
  
  
  
  
STUDY AREA 
The district of Deoria is one of the district of Gorakhpur division, 
and occupies the extreme north-eastern corner of U.P., It is 
bounded by the district Gorakhpur on the West, Maharajganj and 
Padrauna districts in North, Mau & Ballia districts in South and 
Bihar state in the East. The dividing line being partly artificial and 
partly provided by the Gandak and little Gandak rivers, on its 
south Ghagra separates the district of Mau and Ballia from it. It 
lies between lat 26°.0° N to 26°.45° N and Long 83° 30 E 84° 15° 
E. Total area of the district is above 2573.5 sq. kms. and the 
district hqts. is located at Deoria. 
  
  
  
Fig - 1 LOCATION MAP OF STUDY AREA 
  
TOPOGRAPHY 
The district is a level plain, gentle undulation and a gentle slope 
from north-east to south-East. The Ghaghra and Gandak, rivers 
bordering the district on the south and northeast respectively. A 
major part of the district consisting of the alluvium, brought down 
by the Gandak is known as the bhat. The district is divided into 
three topographical units, the bhat, the banger and the kanchar. 
DATA USED 
Three types of data sets have been used for the study. 
- Remotely sensed data, i.e. IRS-1D LISS-III, P103.R53 satellite 
data dated 17-10-2001. 
- The survey of India topographical maps no. 63N/10, 63N/12, 14, 
15, 16, 72b/2, 3, 4, on 1:50,000 scale. 
- Field data, collection from District sugar cane office, district 
statistical office data and other field data. 
METHODOLOGY 
Satellite data was loaded and standard FCC was generated by 
assigning blue, green & red color to visible green, visible red and 
near IR bands respectively, linear contrast stretching was carried 
out to use the total gray range and to get maximum contrast. 
Topographical maps covering the area were scanned and 
georeferenced using GCP works for EASI/PACE image 
processing software. Image to map transformation model was 
generated in order to rectify satellite data using topographical 
maps as a reference by taking suitable GCP both on map and 
image. Second order transformation was carried out and sub scene 
of district area was extracted. 
Supervised classification algorithm was used to classify satellite 
data into various LU/LC classes using MLC. Sugarcane crop were 
clearly seen in reddish brown colour and hence were separated 
from paddy, which appears in bright red on FCC. Since area is 
    
    
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