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PrecisionAg101 Post #3 : 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 Tafuma Fundira (Masvingo, Zimbabwe), (Field Area: 2 Hectares). The image displayed on the map is ETCI (Enhanced True Color Image) on the left and VARI (Visible Atmospherically Resistant Index) image on the right. ETCI stands for Enhanced True Color Image. It is basically a TCI image processed by our own systems to enhance the land features which were not so explicitly visible in the raw TCI Image. VARI stands for Visible Atmospherically Resistant Index. VARI is minimally resistant to atmospheric effects, allowing vegetation to be estimated in a wide variety of environment. Hence, it is ideally recommended to be used for farm level decision making if TCI and ETCI images show visible atmospheric distortion such as mild clouds or haze above the field. 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
In the posted image, as we can see, the ETCI image seems to be distorted due to haze and clouds. In such cases vegetation indices like NDVI will not give correct observations. Thus, VARI is used in such cases. As we can see through the VARI image, a majority of the field is growing pretty well, with some barren regions shown in red. 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 #vari #crophealth #fieldscouting #remotesensing

PrecisionAg101 Post #2 : 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 Gullapalli Sujatha (Viswamatha farms, Andhra Pradesh, one of the pioneers in Natural Farming in India), (Field Area: 26 Hectares). The image displayed on the map is NDWI (Normalized Difference Water Index) captured by the satellite. NDWI images are used to quantify water stress in the vegetation in a field. NDWI index can help us control irrigation, significantly improving agriculture, especially in areas where meeting the need for water is difficult. The colors used to quantify this information is very easy to interpret.

Dark Green/ Green: water stress is good
Yellow: irrigation may need attention
Red: Barren land/ no vegetation

In the posted image, as we can see, the top portion of the field shows pretty good water stress in the vegetation, whereas the remaining field is in the yellowish or red region. This indicates that the farmer needs to pay attention to irrigation in these highlighted regions. 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 #ndwi #waterstress #fieldscouting #remotesensing #consultant #agriconsultant #greentech #smartfarming #precisionagriculture #farm #agritech #agriculture #foodsecurity #technology #satellite #vegetation #india #gis #waterstress #advisor #cropyield

PrecisionAg101 Post #1 : Starting today, we will be releasing posts regularly 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 Harikrushn from Surendranagar, Gujarat (Field Area: 6 Hectares). The image displayed on the map is NDVI (Normalized Difference Vegetation Index) captured by the satellite on 15 Feb 2020. NDVI images are used to quantify crop health status in a field. 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

In the posted image, as we can see, the top left portion of the field is completely barren, whereas the remaining field is in the yellowish green or green region. This indicates that the crop health of the farmer’s field is pretty well. 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 #ndvi #fieldscouting #remotesensing #consultant #agriconsultant #greentech #smartfarming #precisionagriculture #farm #agritech #agriculture #foodsecurity #technology #satellite #vegetation #india #gis #waterstress #advisor #cropyield

Farmonaut has been at the forefront of providing satellite based crop health monitoring services to farmers. We keep upon adding useful features for farmers to make their farming practices much accurate. Today, we have integrated EVI results into our system which can be accessed through our android app.
Even though NDVI results are pretty accurate in estimating crop health status of a field, there are still a few shortcomings (atmospheric and ground level) which can influence the field results.
The Enhanced Vegetation Index (EVI) uses additional wavelengths of light to correct for the inaccuracies of NDVI. Variations in solar incidence angle, atmospheric conditions like distortions in the reflected light caused by the particles in the air, and signals from the ground cover below the vegetation are corrected for using EVI.
Select your field for satellite monitoring on the app and start receiving EVI results with the satellite data as well!
Happy Farming!
#evi #ndvi #ndwi #vegetation #soil #aerosol
#organicfarming #crops #urbanfarming #fieldscouting #remotesensing #consultant #agriconsultant #greentech #smartfarming #precisionagriculture #farm #agritech #vegetables #agriculture #foodsecurity #technology #satellite #india #gis #waterstress #advisor #cropyield

Managing multiple farmer portfolios and generating results (Remote sensing data, weather data, precision agriculture analysis, GEOTIFF images) for them can be time consuming. Not only it limits the number of farmers you can serve but also the amount of money you spend on generating these results through third-party services. Why not automate this entire process and receive all these results automatically whenever the new data is available. By automating this entire process through Farmonaut, you can save time, money as well as can increase your farming clientele multifolds. Download the app now and start monitoring fields of your farmers! For any queries contact us on: support@farmonaut.com

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

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

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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.

ESTIMATION OF THE YIELD OF POTATOES

INTRODUCTION

India is country which has an economy dependent on the agriculture. The production and distribution of agriculture is very much associated with the economy of India. Crop growth and yield monitoring over agricultural fields is an essential procedure for food security and agricultural economic return prediction. The advances in remote sensing have enhanced the process of monitoring the development of agricultural crops and estimating their yields. Achieving the maximum crop yield at the lowest investment is an ultimate goal of farmers in their quest towards an economically efficient agricultural production. Early detection of problems associated with crop yield can greatly help in reducing the loss and reaching the targeted yield and profit. Potato is classified as being the fourth major staple around the globe, which is still quickly attaining importance. The growing interest in potato, along with the diminishing agricultural lands, introduces the need for germplasm yield enhancement, better crop protection and much more efficient and productive management systems. Prediction of potato crop yield prior to the harvest period can be very useful in pre-harvest and marketing decision making. Many studies showed that traditional methods of crop yield estimation could lead to poor crop yield assessment and inaccurate crop area appraisal. In addition, these methods normally depend on rigorous field data collection of crop and yield, which is a costly and time-consuming process. Remote sensing (RS) and Geographical Information System (GIS) technologies can be used to assess the temporal variation in crop dynamics, including crop yield and its spatial variability. Visible (blue, green and red) and near infrared (NIR) portions of the electromagnetic spectrum have already proven their effectiveness in accessing information on crop type, crop health, soil moisture, nitrogen stress and crop yield. Advancement in RS techniques enhanced the use of multispectral images as an effective tool in determining and monitoring vegetation conditions, crop stress and crop yield prediction. Liu and Kogan revealed that remote sensing data offered exceptional spatial and temporal land surface characteristics, including the environmental impacts on crop growth. Numerous studies have reported that there could be a good correlation between the vegetation indices provided by the RS techniques and the crop yield and biomass. A crop yield research that is conducted at a regional scale, which employs coarse or low-resolution satellite images, can provide a broader information on the crop canopy conditions and crop yield estimates. Hence, decisions in the quantitative export and import of the product within the region could be made in assured way.

What environmental factors affect the yield?

 

Plant growth and geographic distribution are greatly affected by the environment. If any environmental factor is less than ideal, it limits a plant’s growth and/or distribution. Environmental factors that affect plant growth include light, temperature, water, humidity, and nutrition. It is important to understand how these factors affect plant growth and development. With a basic understanding of these factors, you may be able to manipulate plants to meet your needs, whether for increased leaf, flower, or fruit production. By recognizing the roles of these factors, you also will be better able to diagnose plant problems caused by environmental stress.

                        i.         Light: Three principal characteristics of light affect plant growth are quantity, quality, and duration. Light quantity refers to the intensity, or concentration, of sunlight. It varies with the seasons. The maximum amount of light is present in summer, and the minimum in winter. Light quality refers to the colour (wavelength) of light. Blue and red light, which plants absorb, have the greatest effect on plant growth. Blue light is responsible primarily for vegetative (leaf) growth. Red light, when combined with blue light, encourages flowering. Plants look green to us because they reflect, rather than absorb, green light. Duration refers to the amount of time a plant is exposed to light. Photoperiod controls flowering in many plants. Scientists initially thought the length of light period triggered flowering and other responses within plants. Thus, they describe plants as short-day or long-day, depending on what conditions they flower under. We now know that it is not the length of the light period, but rather the length of uninterrupted darkness, that is critical to floral development.

                       ii.         Temperature: Temperature influences most plant processes, including photosynthesis, transpiration, respiration, germination, and flowering. As temperature increases (up to a point), photosynthesis, transpiration, and respiration increase. When combined with day-length, temperature also affects the change from vegetative (leafy) to reproductive (flowering) growth. Depending on the situation and the specific plant, the effect of temperature can either speed up or slow down this transition. Low temperatures reduce energy use and increase sugar storage. Thus, leaving crops such as ripe winter squash on the vine during cool, fall nights increases their sweetness. Adverse temperatures, however, cause stunted growth and poor-quality vegetables.

                     iii.         Water and Humidity: Most growing plants contain about 90 percent water. Water plays many roles in plants. It is a primary component in photosynthesis and respiration. Responsible for turgor pressure in cells (Like air in an inflated balloon, water is responsible for the fullness and firmness of plant tissue. Turgor is needed to maintain cell shape and ensure cell growth.) A solvent for minerals and carbohydrates moving through the plant. Responsible for cooling leaves as it evaporates from leaf tissue during transpiration. A regulator of stomatal opening and closing, thus controlling transpiration and, to some degree, photosynthesis. The source of pressure to move roots through the soil. The medium in which most biochemical reactions take place.

 

                     iv.         Plant Nutrition: Plant nutrition often is confused with fertilization. Plant nutrition refers to a plant’s need for and use of basic chemical elements. Fertilization is the term used when these materials are added to the environment around a plant. A lot must happen before a chemical element in a fertilizer can be used by a plant. Plants need 17 elements for normal growth. Three of them carbon, hydrogen, and oxygen are found in air and water. The rest are found in the soil. Six soil elements are called macronutrients because they are used in relatively large amounts by plants. They are nitrogen, potassium, magnesium, calcium, phosphorus, and sulphur.

What soil factors affect the yield?

Potatoes grow best during cooler weather. Plant potatoes 2-4 weeks before the last frost in the spring, when the soil temperature is at least 40 degrees F. In warm climates, potatoes are planted from January to March and harvested between March and June. In cooler areas, potato-planting time is usually between April and June, with harvest between July and September. Plant potatoes where they will receive full sun, and choose and well-drained, acidic soil. Avoid planting in the same spot in which peppers, eggplants, or tomatoes were grown in the previous season, as potatoes are particularly susceptible to diseases carried by those plants. Cut seed potatoes into 1- to 2-inch squares with two to three eyes (bud sprouts) per piece, then allow them to dry for a couple of days before planting. Plant seed potatoes 12 to 18 inches apart and four inches deep. The eyes should be facing up and the cut side facing down. If planting in-ground, space rows 24 to 36 inches apart to leave room for hilling and walking between the plants.

Does harvesting method play a role in increased/decreased yield?

Harvest “new” (immature) potatoes for eating after the plant begins flowering. Gently scratch some soil under the plants aside and feel around for a few small tubers and pull them out, then replace the soil so the plants will keep growing. For full-sized potatoes, wait until after the tops of the plants have died. Carefully dig up the entire plant using a garden fork, doing your best not to bruise or pierce the potatoes. Bring them inside and keep them in a dark, cool, humid spot (such as an unfinished basement or garage) for two weeks to “cure” so the skins will thicken and dry for storing. Store in a covered, ventilated box or bin.

 

What indices are generally used for this kind of study?

Monitoring of crop growth and forecasting its yield well before harvest is very important for crop and food management. Remote sensing images are capable of identifying crop health, as well as predicting its yield. Vegetation indices (VIs), such as the normalized difference vegetation index (NDVI), leaf area index (LAI), Soil adjusted vegetative index (SAVI), normalised differential water index (NDWI) calculated from remotely sensed data have been widely used to monitor crop growth and to predict crop yield.

RESULTS

The fields of potato observed and the Normalised Differential Vegetation Index (NDVI), Normalised Differential Water Index (NDWI) Vegetative Condition Index (VCI) are calculated. The values of indices are given below.

The above image shows the NDVI values of the study area. The value of NDVI varies from -1 to +1, as the values increases the amount of vegetation increases. The study area is observed for two months from April to May. From the above image we can identify the changes in the level of vegetation for each period of time.

Normalized Difference Water Index (NDWI) is used to differentiate water from the dry land or rather most suitable for water body mapping. Water bodies have a low radiation and strong absorbability in the visible infrared wavelengths range. The value of NDWI varies from -1 to +1.

The Enhanced Vegetation Index (EVI) is an optimized vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences. The value range of EVI is between -1 to +1 and with healthy vegetation generally around 0.20 to 0.80.

CONCLUSION

The estimation of yield of potatoes using remote sensing is a very toughest process. The cultivation of potato in the study area was during March 2020 to May 2020 and the satellite data of that period is used for the study. Various indices like NDVI, NDWI, EVI are used to estimate the growth of plants and by that estimate the outcome. The values of NDVI, NDWI and EVI for beginning of April month is 0.33, 0.32, 0.31 respectively. The crops are in the beginning stages of the growth and so NDVI and EVI value are higher. The NDWI value shows some water stress in the soil. Similarly, the values of April last week are 0.34 for NDVI, 0.23 for NDWI and 0.31 for EVI. Also, for first week of May is 0.32 for NDVI, 0.21 for NDWI and 0.31 for EVI. The results for last week of May is 0.48 for NDVI, 0.41 for NDWI, 0.48 for EVI.

 

The values of May last week shows that the crop has attained a very good condition. The water stress in the field is good, and so the yield of the crop will be better. On analysing the values of the different considered indices, we can conclude that the crop shows a very good health conditions, by that we can expect a very good amount of yield. The potatoes in each field of our study area will give a moderately above amount of yield.

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.

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