Rice Yield Calculation Based on Remote Sensing in Saharsa (Bihar) - 2021
Yield calculation of the crop is very important for assessing the production and it depends on many variables like soil, location, weather, crop variety, agricultural practices (date of planting, amount of irrigation, fertilizer use, weedicide and pesticide use), seeds and biotic stress.
Remote sensing provides an effective and efficient way to forecast yield. Remote sensing has been widely used by many institutions across world to calculate area and yield of a crop.
In this paper, we focus on forecasting yield of paddy based on data from Sentinel-2 satellite data and Farmonaut’s Satellite Based Crop Monitoring System available on android, iOS, and web-app. The study area was rice crop in Saharsa (Bihar). The crop is in advanced stage and has vegetated fully and harvest is expected in next 10-30 days depending on crop variety of rice.
Data from Sentinel-2 was used and fields with highest vegetative index were selected from the mapped area and yield was calculated using NDVI (Normalized Difference Vegetation Index).
Farmonaut platform was used to map fields to create bounded regions (fields) as shown in Figure-2. On the dates of observations the sample fields were free from clouds and normal data was observed. Crop classification was done manually by ground truthing to ascertain the crop is standing in the field.
Maximum NDVI was used to calculate yields for the given fields which means peak vegetative growth rate. Peak vegetative growth helps to forecast yields more accurately.
Below are the observations of NDVI of the various fields. The maximum NDVI was reached on 4-Oct-2021 which was used to calculate yield. This approach is being used after going through a lot of available texts and final model was considered.
This model suggests direct correlation between grain yields and NDVI. This relationship was applied to all the fields to calculate yields.
NDVI started to rise after planting of crop in June continued to rise until 4-Oct-2021 making the data as best date for calculating yield. The NDVI started falling after 4-Oct-2021 and will fall until harvest. Best results of yield is obtained when the NDVI is maximum. In Figure 1 NDVI figures of three dates were taken and the date when the NDVI was maximum was considered.
NDVI showed the best correlation for the estimated yields. The yields ranged from 4.7 Tons/ha to 6.28 Tons/ha as shown in Figure 1 implying that most of the fields are having of long duration rice crop thereby generating higher NDVI. Also, the highest NDVI fields were selected meaning that most of the fields assessed is having long duration rice variety.
Area ( sq. m.)
Estimated Yield (Tons/ha)
Our analysis of prediction of average yield was reported at 5.88 tons/ha which is in line with yield reported by Krishi Vigyan Kendra- Saharsa(ICAR) of 5-6 tons/ha for long duration crops. Our analysis show that the rice field used for yield prediction is mostly of long duration. Also, the prime reason of higher yield prediction was due to selection of fields which are producing highest NDVI skewing the data towards higher average yield. Which means the fields selected in our study is in upper quartile of yield implying long duration rice variety.
Data from Krishi Vigyan Kendra (KVK-Saharsa) showed that yields range between 5-6 tons/ha for long duration crop of 150-155 days (MTU 7029, Rajendra Masuri, Sabaur Shree, Sabaur Sampann) which will be harvested in November. For short duration rice crop of 90-100 days (Prabhat, Rajendra Bhagwati, Sabaur Surabhi etc) show mean yield of 3 tons/ha and it is harvested from mid-October. Also, yields in Saharsa is lower as it is situated on flood belts and around 80% of field is effected by water logging leading to lower yields compared to other districts. The above mentioned yields can vary depending upon weather before harvest as the crop is still standing while making the report. If conducive condition prevail then the above yields can be achieved.
The researches done in the area of yield forecasting of field crops by remote sensing has demonstrated good results. With the help of new sensors and indexes, researchers can calculate yields with less errors in future. Also, data by ground truthing will benefit the yield forecasting.
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