Omagri - Agriculture What We do

Case Study

we are identified fresh and ratoon sugarcane in gujarat with the guidance of ISRO-SAC and provide area and gives acreage estimation in advance.

Sugarcane is a most important cash crop of India.

It involves less risk and farmers are assured of some degree of return even in adverse conditions. The sugarcane crop provides the raw material for the second largest agro-based industry after textiles. The sugar industry plays an important role in generating a large number of direct and indirect employment in the rural sector and an estimated 50 million farmers and their dependents are involved in sugarcane cultivation. The sugar industry in India plays an important role in the socio-economic development of rural areas by mobilizing rural resources, creating employment and increasing farm income. Due to less risk in the crop, farmers put less effort than other crops and the same goes for inputs. If the condition of crop health is taken care of from time to time, better and almost double production can be obtained.

Year to year fluctuations in weather and acreage, new plantings and fluctuations in ratoon plantings cause a large variation in production. So both the farmer and the mill face problems at the time of harvest. If this information is available in advance, then planning can be done well. we are working on stage wise crop health detection in it so that we can help farmers better.

Estimating sugarcane production at micro-scale prior to harvest is required for fixing of Fair and Remunerative Price (FRP) payable by sugar factories, levy price of sugar and its supply for public distribution systems and regulating supply of free-sale sugar. This may also help the sugar mill owners to plan for crushing the expected cane biomass, estimate the production of sugar in each mill and look for opportunities to sell or buy from nearest sugar mills if expected production is more or less than factory’s crushable capacity. The study was carried out in

Baroda sugar mill of Gujarat State during 2022–2023 period. Multi-date multispectral data from Sentinel-2 and Sentinel-1, LISS IV of Resourcesat-2&2A, mobile enable GPS based ground truth data and Crop Cutting Experiment data (CCE) were used. Crop discrimination in the form of fresh and ratoon, field-scale crop health assessment, yield-model development and mill-level crop acreage and production estimation were carried out. The mill-level production was found to have less than 10% deviation from reported production. The field-scale assessment and enumeration could lead to mill-level crushable cane production forecast 2 months before harvest. Future efforts are needed to improve the production forecasting.