Month: March 2021

Farmonaut matches the user location with satellite coordinates and shows unhealthy vegetation of the field and water stress condition in red and yellow. Upon identifying these regions of the fields, farmers can simply pay a visit to that part of the field and identify if the problem has already started. If it has not, the farmer can take preventive remedies by applying more fertilizers, plant growth regulators, improving irrigation etc. Farmonaut strives to bring state of the art technologies in the grasp of every farmer, and will continue adding more features in the coming days. ☺️ Happy Farming!! 🌱🌾👩‍🌾 #farming #organicfarming #crops #urbanfarming #fieldscouting #smartfarming #precisionagriculture #agro #agrotech #farm #agritech #vegetables #agriculture #foodsecurity #technology #greentech #satellite #vegetation #india #gis

At Farmonaut, we had been helping farmers identify unhealthy crop regions and water stress in their fields through satellite imagery. Now farmers will also receive comprehensive field reports of their agricultural land whenever the satellite crosses their location. This report will consist of:

A. Remote sensing data which will consist of continuous analysis of crop health (NDVI, NDRE, VARI) and water stress (NDWI) through tables, graphs, color maps etc.

B. Continuous weather data through which farmer can identify how is their field performing with different weather parameters.

These reports will be automatically sent to the email ID of the farmer as well as can be directly downloaded through the app.

Update your app now to access this new feature.
Happy Farming!

#precisionagriculture #organicfarming #fieldreport #analysis #research #greentech #technology #tech #foodsecurity #nasa #esa #spacex #remotesensing #gis #earth #farming #farm #harvest #agriculture #satellite #crophealth #fruit #vegetables #crops #agritech

GET FREE SATELLITE DATA!

Increase your Trust Rating On The Farmonaut Discussion Forum To 50+, and get satellite data for any field for free!

How To Increase Trust Rating?

1. Share Content On Farmonaut Discussion Forum
2. Provide Solutions To The Farmer Problems On The Discussion Forum

MORE THE LIKES ON YOUR POSTED CONTENT AND PROVIDED SOLUTIONS, THE MORE IS YOUR TRUST RATING!

Download The App From Google Play Store Now!

Crop Health, Water Stress, Evapotranspiration, Soil Organic Carbon, Radar Data, Weather Forecast, Farmers’ Social Network, Govt. Approved Database and so much more!

For partnerships/ large scale/ custom requirements contact us at: support@farmonaut.com

#farming #organicfarming #crops #urbanfarming #fieldscouting #remotesensing #agriconsultant #greentech #smartfarming #precisionagriculture #farm #agritech #vegetables #agriculture #foodsecurity #technology #satellite #vegetation #india #gis #waterstress #advisor #cropyield #socialnetwork

Introduction:

Yield calculation of the crop is very important for assessing the production and it depends on many variables like soil, weather, agricultural practices (date of planting, amount of irrigation and fertilizer 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 wheat based on data from Sentinel-2 satellite data. The study area was wheat crop in Saharsa (Bihar). The crop is in advanced stage and has vegetated fully and harvest is expected in next 20 days.

Methodology:

 Data from Sentinel-2 was used and random fields were selected from the mapped area and yield     was calculated using NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area 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 21 Feb 2021 which was used to calculate yield. From the maximum NDVI, LAI was calculated which was subsequently used to calculate yield. This approach is being used after going through a lot of available texts and final model was considered.

In this procedure the estimated LAI was used with WTGROWS model for yield mapping. This model suggests direct correlation between grain yields and LAI. This relationship was applied to all the fields to calculate yields.

NDVI started to rise after planting of crop in November continued to rise until 21-Feb-2021 making the data as best date for calculating yield. The NDVI started falling after 21-Feb-2021 and will fall until harvest. Best results of yield is obtained when the NDVI is maximum.  

 

NDVI and LAI showed the best correlation for the estimated yields. The yields ranged from 3.5 Tons/ha to 3.7 Tons/ha.  Figure-1

 

 

 

Area (sq. m.)

16-Feb-21

21-Feb-21

26-Feb-21

03-Mar-21

Maximum LAI

Estimated Yield  Kg/ha

Field 1

3600

0.6

0.7

0.7

0.7

2.912119253

3713

Field 2

7700

0.6

0.7

0.69

0.67

2.912119253

3713

Field 3

1500

0.59

0.7

0.7

0.69

2.912119253

3713

Field 4

3100

0.6

0.7

0.7

0.69

2.912119253

3713

Field 5

6600

0.59

0.7

0.68

0.67

2.912119253

3713

Field 6

2700

0.57

0.7

0.68

0.67

2.912119253

3713

Field 7

2000

0.52

0.64

0.64

0.64

2.532181241

3493

Field 8

4300

0.59

0.7

0.7

0.7

2.912119253

3713

Field 9

1700

0.52

0.65

0.66

0.64

2.591873781

3530

Field 10

3700

0.6

0.7

0.7

0.69

2.912119253

3713

 

Results:

Data from government institution show the wheat yield in Madhepura district of Bihar ta 3.8 Tons/ha which is just next to Saharsa district.

The above mentioned yields can vary depending upon weather before harvest. If normal condition prevail then the above yields can be achieved subject to error of 10%.

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.