Insur.Tech

Insur.Tech

Remote sensing is the science to obtain reliable information about an object without being in physical contact with it. Remote sensing provides a global perspective along with a vast range of data about Earth systems and it enables data-based decision making on the current and future state of the earth.

The remote sensing-based data-driven decision-making helps us in different fields such as mapping of forest fire, tracking clouds to predict the weather, or watching volcano eruption, dust storms, tracking down change in the agricultural farmland, predicting the yield of a certain crop as well as monitoring the crop health throughout different stages of its growth, tracking the growth of cities, change in urbanization, etc.

As an Agritech start-up Ingreens concentrates on the use of RST in the field of agriculture. India being a developing country and more than 65% of its population lives in village, Agriculture is of huge economic significance in India as it accounts for the involvement of the largest share of the labour force and significant contribution in Indian GDP.

Natural calamities such as floods, heavy rainfalls, cyclones, hailstorms, droughts etc. are major threats to this agriculture-based economy. Monitoring the loss occurred due to these events and predicting crop yields after such calamities are of utmost importance in ensuring food security of a nation. It also helps in providing the actual benefits of crop insurance to the affected farmers. Remote sensing not only helps in crop yield estimation or crop loss calculation, but it also helps in monitoring crop health, helps in estimation of area of coverage of a particular crop in a particular place to nullify the crop area discrepancy that arises in crop insurance claim, predicting crop yield, monitoring different aspects of precision agriculture etc.

Availability of real-time, reliable information on crop condition is very essential to manage all above mentioned Applications. Completely human based survey and monitoring system makes these real time data driven processes very cumbersome and erroneous. Remote Sensing Technology is one of the most effective and efficient ways in this regard as the required data can be collected without physically reaching the places. Using remotely sensed data along with ground truthing data we can develop model with the help of machine learning tools what makes it possible to manage different aspects of crop management and thus can help in improving quality and quantity of production ensuing food security and better earning of farmers.


Project 1.
Crop yield estimation and loss assessment

The remote sensing-based data-driven decision-making helps us in different fields such as mapping of forest fire, tracking clouds to predict the weather, or watching volcano eruption, dust storms, tracking down change in the agricultural farmland, predicting the yield of a certain crop as well as monitoring the crop health throughout different stages of its growth, tracking the growth of cities, change in urbanization, etc.

As an Agriculture based start-up, our main interest is the uses of RST in the field of agriculture and one of the most important uses is Crop yield estimation and loss assessment. For this purpose, real time monitoring of crop filed is the most important factor. We have used Microwave SAR as well Optical technique of RST to estimate yield and further assessment of the yield loss. The SAR and Optical data have been sourced from Sentinel-1 platform of Copernicus initiative of European Space Agency, ESA, and Landsat 8, the Landsat Data Continuity Mission, LDCM respectively. Using these data along with our own ground truth data, the RST team of Ingreens developed machine learning based model for crop yield estimation and subsequent crop loss assessment.


Project 2.
Crop production forecasting

The remote sensing-based data-driven decision-making helps us in different fields of Weather, Forestry, Agriculture, Surface changes, Biodiversity etc. and among different application of RST in agriculture forecasting of the crop production or yield is one of the most important aspects as it is essential for various agricultural planning purposes, including pricing, export/import, contingency measures, food security etc.

Using real time data, including remote sensing images along with soil and weather parameters data of the crop field, with historic and current ground truthing can be used to develop model to forecast the crop yield of a certain location for a particular crop. Using IOT and drones to capture better-quality real-time data can help in enhancing the accuracy of the whole process many a fold but the assessment of the financial viability of these technologies in Indian condition, where most of the farmers are small and marginal ones involved in conventional ways of farming, is the most important aspect.






Project 3.
Crop Identification and crop acreage estimation

Remote sensing is the science to obtain reliable information about an object without being in physical contact with it. Remote sensors collect data by detecting the energy that is reflected from Earth. These sensors can be on satellites or mounted on aircraft, drones.

Remote sensing provides a global perspective along with a vast range of data about Earth systems and it enables data-based decision making on the current and future state of the earth.

The remote sensing-based data-driven decision-making helps us in different fields of Weather, Forestry, Agriculture, Surface changes, Biodiversity etc. and among different application of RST in agriculture, identification of crop and estimation of area under cultivation for a particular crop.

As there are distinct change in backscatter profiles & Optical indices profiles with different growth stages, Comparison of the SAR backscatter profile as well as optical indices over the period of its different growth stages with other crops as well as other classes like build-up areas, barren land, water bodies etc., generates difference among different Classes.

Use the pre-processed satellite data of different classes and analysis with machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), provide effective tools to identify different land cover classes, develop land cover Map and subsequent estimation of the crop acreage.






Project 4.
Crop Health Monitoring

Remote sensing is the science to obtain reliable information about an object without being in physical contact with it. Remote sensors collect data by detecting the energy that is reflected from Earth. These sensors can be on satellites or mounted on aircraft, drones.

Remote sensing provides a global perspective along with a vast range of data about Earth systems and it enables data-based decision making on the current and future state of the earth.

The remote sensing-based data-driven decision-making helps us in different fields of Weather, Forestry, Agriculture, Surface changes, Biodiversity etc. and among different application of RST in agriculture, assessment of crop health and its monitoring is very important aspect.

Assessment of the health of a crop, as well as early detection of crop infestations, is critical in ensuring good agricultural productivity. Stress associated with, for example, moisture deficiencies, insects, fungal and weed infestations etc. must be detected early enough to provide an opportunity for the farmer to mitigate.

Using real time data, including remote sensing images along with soil and weather parameters data of the crop field and with historic and current ground truthing can be used to develop model to any such infestations as well as monitoring the crop health.








Project 5.
Precision farming

Remote sensing is the science to obtain reliable information about an object without being in physical contact with it. Remote sensors collect data by detecting the energy that is reflected from Earth. These sensors can be on satellites or mounted on aircraft, drones.

Remote sensing provides a global perspective along with a vast range of data about Earth systems and it enables data-based dsecision making on the current and future state of the earth.

The remote sensing-based data-driven decision-making helps us in different fields of Weather, Forestry, Agriculture, Surface changes, Biodiversity etc. and among different application of RST in agriculture, Precision farming is one of the most important and futuristic application of remote sensing.

Precision farming is nothing but the use of geographical information to determine field variability ensuring optimal use of inputs and maximize the output from a farm. Precision farming gained popularity after the realization that diverse fields of land hold different properties and demands different farm management. Large tracts of land usually have spatial variations of soil types, moisture content, nutrient availability and so on. Therefore, with the help of remote sensing, geographical information systems and global positioning systems, farmers can more precisely determine what inputs to put exactly where and in what quantities.

This data-driven decision-making system helps farmers to effectively use expensive resources such as fertilizers, pesticides, and herbicides, and more efficiently use water resources. In the end, farmers who use this method would not only maximize their yields but also reduce their operating expenses, thus increasing their profits and most importantly conserve the ever-depleting resources like ground water leading to sustainability.







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