In Rabi season 2020-21, FASAL SALAH team carry out a study of crops grown in Indore district of Madhya Pradesh. Wheat is the major crop of Indore district followed by Gram. Idea was to look at the crop on ground as well as from satellite to see if size the crop and yields could be determined.
FASAL SALAH App, Our Crop monitoring technology is unique using bottoms up approach with crop monitoring at field level and correlating ground realities with real-time crop vegetation index monitored via spectral analysis of high-resolution satellite images for different fields and crops. This enables to track positive and negative dynamics of crop development much more realistically.
This way it is more realistic way of crop yield estimation before actual harvest. Our technology has been field tested and awarded by Govt. of India (Winner of Great Agriculture Challenge 2019).
Satellite Image Processing and Vegetation Index-
The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices MOD13Q1 Version 6 (16-day composite) NDVI image product with spatial resolution of 250m, was used in this work. The satellite image has been pre-processed to rectify the geometrical errors.
The Soil Moisture Active Passive (SMAP) with 9 km resolution data used in this assessment, The SMAP spacecraft carries two instruments, a radar (active) and a radiometer (passive), that together make global measurements of land surface soil moisture and freeze/thaw state. It is useful for monitoring and predicting natural hazards such as floods and droughts, understanding the linkages between Earth’s water, energy and carbon cycles, and reducing uncertainties in predicting weather and climate.
During the rabi season sentinel- 2 (Optical) data was used for the crop classification. Sentinel-2 is the high-resolution optical satellite of ESA and the EU. The images have a resolution of 10 to 20 meters, higher than Landsat, and, as always with the Copernicus programme, the data are free and open. A single date Sentinel-2 imagery has been taken and individual bands of 10m spatial resolution NIR, R, and G are stacked together to create a multispectral image cube. Once the stacked image is generated subset the area on the basis of AOI (area of interest), then classified the stacked image using supervised classification and estimate the crop acreage of wheat crop during the rabi season.
Our main task is to conveniently combine all of the information about your fields in one place, to identify and draw the attention of farmers to problem areas. To achieve this, we collect data from different sources - satellite images, weather data, soil types, vegetation maps, among others - and supplement it with our own developments. After having analyzed the data obtained, the system notifies the user of possible risks and changes provides the ability to cluster fields and treat them accordingly, as well as predict yields. In the long run, the system allows for optimal resource utilization and effective decision-making.
- The district lies at the 22.71363 latitude and 75.7851 longitudes covering an area of 3,989 km2, according to the 2011 census Indore district has a population of 3.2 million.
- Indore falls under Malawa plateau Agro climatic Zone with an annual rainfall of 960.9 mm with the South West Monsoon, North East Monsoon, Winter and Summer supply.
- The net sown area is 264 thousand hectares and gross cropped area is 438 thousand hectares with cropping intensity of 165.1%.
· The district has a wide variety of crops grown where major Rabi crops in the region are Wheat and Gram.
- BKC’s FASAL SALAH App is in service of Indore farmers. A unique and AI based advanced technology where the farmers sends the geo-tagged crop pictures of mustard for better advice. Farmers get advisories based on variety sown, date of sowing and weather forecast of their village. This App uses artificial intelligence for image processing of farmers sent pictures and crop modelling analytics, which BKC has established for calculation of area, yield and health of the crop well before the harvest.
- Temperature condition in the month of November and December is optimal while in the last week of January maximum and minimum temperature sudden down, whereas from the first week of February its continuously upturn.
- No rainfall perceived in the month of November but it showing normal rainfall during the month of December and January, whereas there is no rainfall showing from the month of February to first fortnight of March.
NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI):
- NDVI values of the current year are showing better that of the previous year in all over the district except for Indore and Mhow block during the rabi season 2020-21.
- NDVI values of the current year are lagging behind than that of the previous year in all over the district during the rabi season 2020-21.
- Better crop condition is seen throughout the district with respect to the previous year except for Mhow block.
- All over the district displays lower moisture content with respect to the previous year during the month of Dec 2020.
- Soil moisture demonstrates low moisture content in all over the district when compared to the previous year in the month of January 2021.
- Deficit soil moisture content is depicted in the month of February in all over the district except for Sanwer block, in comparison to the year 2020.
Acreage estimates Indore (Madhya Pradesh):
- Wheat is the major crop of Indore district during the Rabi season 2020-21 with crop area of 114950 hectares. Area of wheat crop in current year showing slightly lower from 126387 ha of 2018-19 by 9% and from normal area down by 6%. On the basis of NDVI crop health condition of Indore district depicts normal condition with respect to the year 2018-19 in rabi season 2020-21 and same scenario can be seen in acreage estimation.
- Average crop yield in Indore district for wheat is approx. 59 quintals per hectare. The crop is calculated when the farmers gives their inputs during the crop registrations in FASAL SALAH App i.e. date of sowing, soil, variety and our crop modeler met-GIS runs in the backend and monitor the crop at every stages throughout the crop cycle and predicts the crop yield before harvesting.
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