Blogs

Farmonaut Overview: 2021-2022

Farmonaut was a vision seen in 2018 to bridge the technological gap between the farmers and their outputs. It also offers a satellite-based crop health monitoring system to agricultural companies to map more than thousands and lakhs of farmers’ fields. Farmanaut uses geotagging to map fields and provide smart data to farmers through its robust and advanced web app. 

As per data, organizations like Godrej Agrovet mapped several thousand field pieces initially using Farmonaut’s technology. The organization had already mapped over several thousand hectares last year.

Ankur Omar, CEO and founder of Farmonaut, said “Geotagging of farmer’s field has become easy, fast and more streamlined. There is an ecosystem of interconnected apps which enables our clients to perform multiple tasks like monitoring data, managing it without going to fields and accessing the data from anywhere across the world. During Covid 19, Farmonaut was a game-changer when it provided flawless data with interoperability that had become a game-changer for the organizations that have bigger pieces of land with huge numbers.” He added, “If flawless data is provided, it helps organizations perform farm-level actions in advance. Several organizations are using this technology. We welcome more to be on board across the world.” 

 

The challenge of agriculture in India is to get support and education for the right crop as per the weather conditions. Remote sensing technology provides data that mitigate challenges cost-effectively.

“We provide support and data to our clients cost-effectively. Indian farmers spend Rs. 42,000 per year on each hectare of land suitable for growing crops. If a farmer uses high processed remote sensing data results, 30% of the expense on chemicals, insecticides, pesticides and plant growth regulators can be saved.” said Aakash Omar, co-founder of Farmonaut.

Aakash Omar added, “We provide updated data, every 2 to 5 days with the resolution of 10m.

 

What data Farmonaut provides, let us figure out in detail.

Crop Health – To monitor crop health using geo sensing technology is a game-changer when it comes to production. Using geo sensing, a farmer can identify underperforming crops. Farmonout does provide support to farmers to take out underperforming crops from trouble. The organization provides farmers’ education for better crop yielding. Expert advice raises farmers’ education that helps them at every step to achieve profit. 

 

Evapotranspiration – Geo sensing technology also brings data to farmers when and where water is highly required for the crops. It also provides data for excess water in a field. 

Monitor evapotranspiration is an important task as it gives an estimate to the farmer that how much the soil contains water. A farmer can regulate the irrigation system as per the requirement of soil. It also identifies the location from where water is getting lost into the atmosphere at a higher rate. 

 

Radar data – A client can question what if there are clouds in the sky, how do we get data of our field then? 

To answer this question, Farmonaut also provides radar data that works even if it is extremely cloudy conditions. Organizations rely on Farmanout as it provides flawless data in harsh atmospheric conditions.

 

Digital Elevation Model – India is an agricultural country. Agriculture is a widespread occupation across the country. Monitoring data in Gangetic plans as well as hilly farms of Uttrakhand shows the commitment towards flawless data representation to our clients in India. Farmonaut uses technology that identifies tropical slopes at a micro-level.

During Covid 19 pandemic productivity and efficiency have been some important issues. In that case, useful information helps to improve farming. Digital Elevation Model is an important geospatial product that provides data on every slope and very useful tool for farming in hilly areas.

 

Colourblind visualization – Farmanout understands that a farmer’s productivity is our utmost priority. The organization treats all its farmers with respect. Farmanout also provides colour neutral satellite data specifically for colour blind users.  

 

Water Stress – Water stress is a key factor in the growth of crops. The app identifies the location where the water stress is low. It provides that data to the farmer by which water circulation can be managed effectively. 

 

Soil Moisture Identification – Breaking some barriers ahead, the app Farmanout also provides services in the identification of soil moisture. It provides data to the farmer where the moisture of the soil is low.

 

Soil Organic Carbon – If the organic carbon level of the soil is in check, fertilization can be done in a controlled way for better yielding. It also identifies the location where the soil organic carbon level is low and high. Experts suggest farmers rotate the fertilizers for a better outcome.

 

Weather Forecast – Conducive weather conditions are an essential part of the growth of crops. Weather is an uncontrollable phenomenon. However, damage to crops can be mitigated if the user knows the forthcoming situation. Farmonaut provides upcoming weather conditions so that farmers can prepare themselves.

 

All the above-mentioned services are being used by individual farmers as well as agricultural organizations.  Several agribusinesses have partnered with Farmonaut to avail satellite-based data for their farmers.

Some big names, who are the prominent clients of Farmonaut, are Godrej Agrovet, LINXAg, ZR3i.com, Crop United, Ubin Core, and Fashion for biodiversity. 

 

In India, very few organizations are putting efforts to change the face of agriculture using advanced technology. Farmonaut is one of them by taking baby steps and empowering farmers with technology.

 

All the above-mentioned services are being used by individual farmers as well as agricultural organizations.  Several agribusinesses have partnered with Farmonaut to avail satellite-based data for their farmers.

Some big names, who are the prominent clients of Farmonaut, are Godrej Agrovet, LINXAg, ZR3i.com, Crop United, Ubin Core, and Fashion for biodiversity. 

 

In India, very few organizations are putting efforts to change the face of agriculture using advanced technology. Farmonaut is one of them by taking baby steps and empowering farmers with technology.

 

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

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AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Wheat Yield Calculation Based on Remote Sensing in Saharsa (Bihar) - 2022

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, pests, weeds 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 20-30 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-1. 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.

Figure-1              Comparison of peak NDVI in 2022 Vs 2021 wheat crop

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 8-March-2022 compared to 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 08-March-2022 making the data as best date for calculating yield. The NDVI started falling after 08-March-2022 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 2.87 Tons/ha to 2.98 Tons/ha compared to 3.5 Tons/ha to 3.7 Tons/ha last year (2021).  

 

Figure-2

 

Area (sq. m.)

26-Feb-22

03-Mar-22

08-Mar-22

13-Mar-22

Maximum LAI

Estimated Yield  Kg/ha

Field 1

3600

0.4

0.5

0.5

0.44

1.827376043

2981

Field 2

7700

0.4

0.5

0.5

0.43

1.827376043

2981

Field 3

1500

0.4

0.48

0.5

0.45

1.827376043

2981

Field 4

3100

0.4

0.49

0.5

0.43

1.827376043

2981

Field 5

6600

0.4

0.44

0.49

0.4

1.785290384

2944

Field 6

2700

0.39

0.43

0.47

0.4

1.704004521

2871

Field 7

2000

0.4

0.48

0.49

0.42

1.785290384

2944

Field 8

4300

0.4

0.44

0.48

0.41

1.744173984

2908

Field 9

1700

0.4

0.46

0.5

0.44

1.827376043

2981

Field 10

3700

0.4

0.49

0.5

0.48

1.827376043

2981

 

Figure-3

Observations

The delay in Max NDVI occurred due to late planting of wheat crop as rains in during the planting due to cyclones led to delay in planting. Moreover, rains in vegetative stage of crop led to worsening of condition of the crop.

Also, the average max temperature in the crop season was below corresponding period last year and humidity and cloud cover was significantly higher in 2021-22 leading to less sunlight on the crop thereby affecting the quality of crop and lower yields. Higher humidity leads to frost and less sunlight in winter. Also, more cloud cover leads to lower temperature and less sunlight which directly affects the crop. Even wind speed was higher in the season.

In the Figure 1, there is comparison of peak NDVI of 2021-22 wheat crop vs 2020-21 white crop showing that last year wheat crop was in much better condition that current year and the average yield was 3.7 Tonnes/Hectare in the above plot while this year the average yield in the above 10 fields are 3.0 Tonnes/Hectare which is approximately 20% below last year. The data was corroborated with discussion with Krishi Vigyan Kendra- Saharsa which said the peak crop yield will fall from 4.5 Tonnes/Hectare to 3.7 Tonnes/Hectare which is approximately 20% lower than last year. Late planting, rains before planting, after planting, higher cloud cover, lower temperature, higher humidity and higher wind speed led to lower yield in current year. The crop will be ready to harvest by mid-April-2022 compared to start of April last year.

 

Date

Min. Temp (C)

Max. Temp (C)

Humidity (%)

Pressure (hPa)

Cloud Cover (%)

Wind Speed (m/s)

21-02-2021

21

27

27

1018

0

4.43

08-03-2021

21

31

39

1010

0

4.08

13-03-2021

23

32

49

1013

68

4.22

 

 

 

 

 

 

 

21-02-2022

17

24

38

1012

1

2.68

08-03-2022

18

33

32

1009

0

2.64

13-03-2022

23

28

26

1009

0

2.86

 

 

 

 

 

 

 

2020-21 (1 Dec-Mar 10)

18.80

22.98

41.99

1014.00

7.68

2.39

2021-22(1 Dec-Mar 10)

18.02

20.87

50.95

1014.54

25.88

2.85

 

 

 

 

 

 

 

2021 (1 Feb-10 March)

20.41

25.00

37.38

1013.03

6.14

2.47

2022 (1 Feb-10 March)

18.06

21.43

48.83

1012.43

16.86

3.14

 

 

 

 

 

 

 

2021 (Jan)

16.93

21.14

47.79

1014.21

14.00

2.58

2022 (Jan)

16.41

18.68

57.09

1015.64

31.41

2.76

Figure-4                                Weather parameters responsible for lower yield

 

 

Results:

Data from our observation show that the average yield is 3.0 Tonnes/Hectare compared to 3.7 Tonnes/Hectare in the same fields last year. The yield range from 2.87 Tonnes/Hectare to 2.94 Tonnes/Hectare compared to 3.49 Tonnes/Hectare to 3.71 Tonnes/Hectare.

The above mentioned forecasted yields can vary depending upon weather and other unavoidable factors 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.

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Impact Analysis (April - October 2021)

Last 6 Months have taught us so much at Farmonaut. We faced the dangerous 2nd wave of covid-19 which affected all of us on the team as well as family level. For a bootstrapped startup like Farmonaut® it was a very important battle to be fought. It feels great to see us achieve so many milestones even in these tough times.


From serving more than 20,000 users directly through our platforms, to onboarding so many API users globally (Zr3i.com زرعي دوت, Godrej Agrovet Ltd, Ubincore, Fashion For Biodiversity Solutions GmbH, Linx Spatial Systems) and having more than 250,000 API Hits, to mapping 22,000+ farms for Godrej Agrovet Ltd, to successfully completely a large scale soybean area estimation for The Soybean Processors Association of India (SOPA), to conducting several internal and corporate researches, and receiving multiple awards and recognitions (Ramaiah Evolute, CE Worldwide, The Startup Pill, GIS Resources, Global Launch Base), it surely was a splendid 6 months period.


These are just a few steps towards a greater goal we plan to achieve in the coming months. Stay tuned! A lot more announcements are on the way.


Happy Farming!

#farming #farm #harvest #agrotech #agriculture #satellite #crophealth #farmonaut #fruit #vegetables #crops #agritech #precisionagriculture #organicfarming #greentech #technology #tech #foodsecurity #farmerlife #urbanfarmer #urbanfarming #nasa #remotesensing #gis #earth

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

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.

 

Methodology:

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.

 Figure-1

Farmer

Area ( sq. m.)

24-09-2021

04-10-2021

09-10-2021

Estimated Yield (Tons/ha)

Field-1

32600

0.6

0.63

0.54

4.70

Field-2

9000

0.63

0.64

0.56

4.90

Field-3

12000

0.62

0.69

0.58

6.03

Field-4

3800

0.62

0.7

0.6

6.28

Field-5

22300

0.65

0.7

0.43

6.28

Field-6

5600

0.64

0.7

0.48

6.28

Field-7

5300

0.63

0.68

0.43

5.78

Field-8

4200

0.65

0.7

0.55

6.28

Field-9

6300

0.51

0.7

0.54

6.28

Field-10

12000

0.62

0.69

0.51

6.03

Results:

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.

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Farmonaut Wheat And Paddy Yield Estimation

1. Wheat Yield (Saharsa Region Rabi 2020-2021)

During the Rabi season 2020-21, Farmonaut performed yield estimation of wheat in Saharsa district of Bihar using its remote sensing data and dedicated algorithms with several vegetation indices in consideration to create meaningful data and also by analyzing previous years data sets. 

The data generated and yield calculated was around 3.7 tonnes/hectare. However, after collecting the ground data and correlating with our estimated yield, we have noticed a deviation of up to 15% at some location. The mean yield achieved in the area was 4.4 tonnes/hectare as compared to our estimated 3.7 tonnes/hectare. 

By conducting such study purpose program, we, at farmonaut will improve and will come up with much better results in coming seasons.

2. Paddy Yield (Uttar Pradesh Region Kharif 2020-2021)

We conducted yield estimation program in Uttar Pradesh for paddy in kharif season 2020, for our research purpose, using various vegetation indices for every stage of crop to depict its characteristic throughout the cycle. 

Yield estimation is dependent on vegetative indices values which indicates the optimum time to harvest the crop to get maximum output from farms by checking biomass content. After going through the data of previous years and creating correlations, we used those algorithms and implemented them on the monitored fields and farms and our findings were quite close to the ground data with accuracy of more than 90% in results. 

 

We will continue our research work further to other crops too and will keep on posting our findings as well.

 

We will keep posting about any such informative information on to our blogs, to help as many people as possible. Farmonaut is built upon a vision to bridge the technological gap between farmers and strives to bring state-of-the-art technologies in the hands of each and every farmer. For any queries/suggestions, please contact us at support@farmonaut.com.

We have some more interesting articles coming up soon. Stay tuned!

Wait!!

Before that…

Follow us at:

Facebook: https://facebook.com/farmonaut

Instagram: https://instagram.com/farmonaut

Twitter: https://twitter.com/farmonaut

LinkedIn: https://www.linkedin.com/company/farmonaut/

Pinterest: https://in.pinterest.com/farmonaut/

Tumblr: https://farmonaut.tumblr.com/

Youtube: https://www.youtube.com/channel/UCYWOOPPKATLgh4L6YRlYFOQ

AppLink: https://play.google.com/store/apps/details?id=com.farmonaut.android

Website: https://farmonaut.com

Satellite Imagery: https://farmonaut.com/satellite-imagery

Satellite Imagery Samples: https://farmonaut.com/satellite-imagery-samples

 

 

Every time the satellite crosses location of the selected field, Farmonaut’s Satellite Based Crop Health Monitoring System generates field results as well as user downloadable reports as well. These user downloadable reports can be used for offline purposes when the user is not connected to the internet or wishes to share the field data with the on-field team. These auto-generated reports consist of the following information:

1. Weather Statistics on the Imagery Capture Date (Weather Station, Temperature (minimum and maximum), Pressure, Humidity, Wind Speed, Wind Direction, Cloud Cover)
2. Weather Graphs
3. Weather Forecast (Next 7 Days)
4. Index Data for the satellite captured date
5. Satellite Captured Images
6. Time series trend of the indices
7. Scientific background of the indices
8. Field area in different health zone for each index

The app is available on Google Play Store, Apple App Store as well as a Web App on our website. Happy Farming!

#farming #organicfarming #crops #urbanfarming #fieldscouting #remotesensing #greentech #smartfarming #precisionagriculture #agro #agrotech #farm #agritech #vegetables #agriculture #foodsecurity #technology #satellite #vegetation #india #gis

Introducing ROBUST POLYGON MANAGEMENT SYSTEM with the Farmonaut Web App. While monitoring a big area for crop health and water stress, a user may have to submit hundreds and thousands of polygons within a same master field to facilitate better data insight. To ensure a better accessibility for these large number of polygons within a field, farmers can now see a full searchable list of polygons on the interface itself as well as can provide a customized name for each and every polygon as well. By doing this, users can easily identify any specific polygon and analyze data for their ground level decision making.

Happy Farming!

#farming #organicfarming #crops #urbanfarming #fieldscouting #remotesensing #greentech #smartfarming #precisionagriculture #farm #agritech #agriculture #foodsecurity #technology #satellite #vegetation #india #gis #webapp #newfeature #organicmatter

While Monitoring the farms, it is essential to observe how different parts of the field have shown growth changes through time to make better on-field decisions. This can be done through Farmonaut’s Web App by comparing two satellite images simultaneously. You can select any two dates of satellite visits and compare results side by side on the map of the Farmonaut web app itself. Try this out if you haven’t yet.
Happy Farming!
#webapp #newfeature #organicmatter #farming #organicfarming #crops #urbanfarming #fieldscouting #remotesensing #greentech #smartfarming #precisionagriculture #agro #agrotech #farm #agritech #vegetables #agriculture #foodsecurity #technology #greentech #satellite #vegetation #india #gis #soil #soilhealth #soc

 

In 2016, fertilizer consumption for India was 165.8 kilograms per hectare. Fertilizer consumption of India increased from 100.3 kilograms per hectare in 2002 to 165.8 kilograms per hectare in 2016 growing at an average annual rate of 3.81 %.
The chemical price can vary from 250 rupees to 2000 rupees per kg depending upon the specific chemical being used. So on an average an Indian farmer spends between 41500 to 332000 rupees per year on each hectare of arable land.
Farmonaut identifies those areas of land where the crop growth is not normal. Farmer can apply fertilizers, chemicals, insecticides, pesticides, plant growth regulators etc. only in
those areas where the crop growth is not normal. By using
remote sensing results provided by Farmonaut, a farmer can save at least 30% on chemical consumption per year.
So for every one hectare of land, farmer can save at least between 12000 (~ $160) and 95000 (~ $1300) rupees per year.

The app is available on Google Play Store, Apple App Store as well as a Web App! Happy Farming!

#farming #organicfarming #crops #urbanfarming #fieldscouting #remotesensing #greentech #smartfarming #precisionagriculture #agro #agrotech #farm #agritech #vegetables #agriculture #foodsecurity #technology #greentech #satellite #vegetation #india #gis

PrecisionAg101 Post #4 : These posts are meant to make farmers aware of the basics of precision agriculture and how to interpret various satellite data provided by us.

The field image attached below is of the farmer Paulo o (Ponta Pora – State of Mato Grosso do Sul, 79900-000, Brazil), (Field Area: 47 Hectares). The image displayed on the map is NDRE (Normalized Difference Red Edge) captured by the satellite. NDRE images are used to quantify crop health when the crop is in the later stage of growth. NDVI index is not ideal for the crops in their later stage of growth because in grasses, cereal crops, permanent crops and in certain row crops which are in their later growth stages, chlorophyll content reaches a point at which NDVI reaches a maximum value of 1.0 and hence saturates. Hence, any crop health issue is hard to detect with NDVI until any such problem becomes strong enough to reduce the NDVI value below 1.0. This may happen at a point at which damage has already occurred. By substituting NDVI’s red band with NDRE’s red edge band we can mitigate this issue of saturation discussed above. So, in conclusion, if the crops of observation are permanent or dense, you should use NDRE right away. The colors used to quantify this information is very easy to interpret.

Dark Green/ Green: Crop is very healthy
Yellow: Crop needs attention
Red: Barren land

To cross-verify these results farmers can simply open GPS on their smartphones and can navigate through the field using this image.

By using satellite data provided by us, farmers can:
1. Reduce Chemical/Fertilizer consumption by applying them only in the locations where crop
health is not good.
2. Reduce Labour costs by directing the labours only in those field areas where crop health is
critical.
3. Reduce irrigation water wastage by applying proper irrigation only in those locations where
plant water stress is low.
4. Increase overall yield.

The system is available for use on android, iOS as well as web.

Happy Farming!

#farming #organicfarming #crops #urbanfarming #ndre #rededge #crophealth #fieldscouting #remotesensing #consultant #agriconsultant #greentech #smartfarming #precisionagriculture #farm