Ashish Kumar
Crop health monitoring using technology
Ashish Kumar, Senior Product Manager at Farmonaut Technologies Pvt. Ltd., Bangalore, Karnataka, describes how the use of satellites in crop health monitoring has transformed traditional farming methods. Remote sensing data allows for early detection of crop stress, disease, and water deficiency. This enables farmers to respond promptly and minimise potential losses. The technology also supports informed planning and sustainable agricultural development.
What exactly does crop health monitoring using satellites mean? Most people think of satellites primarily as a technology used for GPS tracking or location-based services. In fact, satellites are capable of doing far more than that. In modern agriculture, satellite technology has emerged as a powerful tool that helps farmers, organisations, startups, companies, and governments to monitor crops, soil, irrigation, and land conditions from space with remarkable accuracy.
We can harness the power of satellites to keep a vigilant and close watch on crops spread across hundreds or even thousands of acres. This advanced technology provides a wealth of data that offers detailed insights into crop health, irrigation requirements, soil organic carbon levels, terrain variations, weather conditions, and several other agricultural parameters. By leveraging this data, it becomes possible to boost crop yields, minimise crop losses, and optimise irrigation and fertilisation practices. Ultimately, satellite-based crop health monitoring supports better decision-making, higher productivity, reduced costs, and more sustainable farming systems.
This article explains the concept of satellite-based crop health monitoring in detail and explores how Farmonaut is developing this technology. It discusses the various parameters used on the platform, how they can be applied to different crop and plantation scenarios, and how the resulting insights can be converted into practical advisories for farmers, agribusinesses, financial institutions, and policymakers. The explanation avoids unnecessary technical complexity while providing sufficient depth and clarity.
On the Farmonaut platform, there are multiple parameters used to provide insights to farmers, organisations, startups, and companies. But what exactly does the term “parameters” mean in this context? Parameters refer to the satellite-derived images or indices that are used to analyse and interpret agricultural conditions. These parameters are mathematical combinations of data captured by satellite sensors operating across different wavelengths of light and, in some cases, microwaves. Each parameter highlights a specific aspect of crop or soil behaviour, such as vegetation vigour, moisture availability in the soil, water stress, terrain variation, or organic matter content in the soil.
These parameters nowadays form the backbone of health advisories, irrigation advisories, soil assessments, and decision-support systems. Different crops, soil types, planting patterns, and climatic conditions behave differently, which is why a single parameter cannot be applied universally. Farmonaut uses a combination of parameters to ensure that insights remain accurate, highly reliable, and relevant even across diverse agricultural environments. By combining multiple parameters rather than relying on a single index, the platform delivers a holistic view of farm health. This approach enables early detection of problems and supports informed, timely interventions.
For crops such as maize or soybean, which typically have a dense canopy and significant height, the NDRE parameter is used. NDRE stands for Normalised Difference Red Edge Index. This index is particularly effective when crops have thick foliage and high biomass because it uses the red-edge portion of the electromagnetic spectrum, which is highly sensitive to changes in plant health. The NDRE parameter provides detailed crop health data for a specific field and identifies the exact locations where something may be going wrong. It highlights stress zones, growth irregularities, nutrient deficiencies, or early signs of disease that may not yet be visible to the naked eye.
Optimising crop and water health
As the saying goes, prevention is better than a cure. If farmers are informed well in advance that a particular patch of their farm is underperforming, they can take corrective measures immediately. This may include adjusting irrigation, applying fertilisers, or inspecting the crop for attack of pests or diseases. Early intervention prevents problems from spreading and helps protect the overall yield. In such situations, crop health parameters like NDRE become highly invaluable tools for proactive farm management.
For crops such as wheat and paddy, where the canopy is less dense and the crop structure differs from that of maize or soybean, the NDVI parameter is used. NDVI stands for Normalised Difference Vegetation Index, and it is one of the most widely used vegetation indices in agriculture nowadays. NDVI provides insights into vegetation vigour by comparing reflected light in the red and near-infrared bands. It effectively indicates how healthy and active the crop is across different locations within a field. NDVI helps to identify areas where crop health is declining and highlights zones that require closer inspection and action. This allows farmers to focus their efforts on specific problem areas rather than treating the entire field uniformly. Targeted intervention saves time, reduces input costs, and improves overall farm efficiency.
Tree plantations such as mango orchards present some unique challenges for satellite-based monitoring. Trees are usually planted with specific spacing between them, resulting in exposed soil between the canopies. In such cases, using only NDVI or NDRE may produce inaccurate results because soil reflectance interferes with vegetation signals. To address this issue, the Soil Adjusted Vegetation Index, or SAVI, is used. SAVI is designed to minimise the impact of exposed soil by incorporating necessary soil adjustment factors into the calculation. This makes it more suitable for plantations and orchards where canopy cover is mostly discontinuous. SAVI provides a clearer picture of plantation health and helps identify stressed or underperforming trees. In addition to SAVI, other indices such as EVI and VARI are also used in similar scenarios. These indices further refine the analysis and improve the accuracy in complex plantation environments. Put together, these parameters enable effective monitoring of orchards, helping plantation owners to maintain tree health, manage productivity, and plan timely interventions.
Irrigation is one of the most critical aspects of farming, and yet it is often managed using traditional practices rather than basing it on precise data. Many farmers irrigate their entire fields uniformly without knowing which areas actually require more frequent irrigation and which do not. Both under-irrigation and over-irrigation can be harmful. Under-irrigation deprives crops of essential moisture, leading to stress, reduced growth, and lower yields. Over-irrigation, on the other hand, can create favourable conditions for pest and disease development, increase nutrient leaching, and degrade the soil structure further. Excess water also results in unnecessary expenditure and wastage of valuable resources. Understanding irrigation requirements at fields at a micro-level is therefore essential for modern agriculture.
To support precise irrigation management, Farmonaut uses NDMI, NDWI, and evapotranspiration parameters. NDMI provides information about soil moisture levels in a particular field. It indicates whether the soil is adequately moist or experiencing dryness. NDWI reflects the amount of water present within the vegetation itself, providing deeper insights into plant water content and hydration status. Evapotranspiration measures how quickly water is evaporating from the soil and transpired by the plants. By analysing these three parameters together, farmers can easily identify exactly which patches of land require more irrigation and how often. For example, a patch with high evapotranspiration and low soil moisture clearly requires more frequent irrigation. Conversely, areas with adequate moisture and lower evaporation rates may not need immediate watering. This targeted irrigation approach ensures optimal water use, prevents wastage, reduces stress on crops, and supports sustainable water management practices.
Soil organic carbon insights
Soil organic carbon is another critical parameter captured through satellite-based monitoring, which is used before sowing and after harvesting. Soil organic carbon represents the organic matter content available in the soil and plays a vital role in maintaining soil fertility, structure, and biological activity. High levels of soil organic carbon indicate healthy soil with good structure, improved water-holding capacity, and better nutrient retention. Such a soil supports strong root development in plants and consistent crop growth. Soil organic carbon also provides indirect insights into the availability of other nutrients such as nitrogen, phosphorus, potassium, zinc, and sulphur. In many cases, areas with declining soil organic carbon also show deficiencies in these nutrients. Traditionally, farmers collect random soil samples for laboratory testing. However, this approach can miss the problematic patches, especially in large or uneven fields. Farmonaut recommends prioritising soil sampling in areas where satellite data indicates lower soil organic carbon levels. By focusing on these areas first, farmers can conduct more effective soil testing and apply fertilisers or make amendments precisely where they are needed. The
Managing terrain variations
Terrain variation also plays a significant role in irrigation efficiency and crop performance. The Digital Elevation Model, or DEM, provides detailed information about land surface elevation across a farm. In fields that have uneven terrain, water tends to accumulate in lower areas, leading to waterlogging, while higher areas may remain dry. Waterlogging can damage crops, reduce oxygen availability to roots, and increase the risk of disease. DEM-based insights help farmers understand these variations in advance. With this information, they can plan on land levelling, drainage systems, or controlled irrigation strategies. This proactive approach prevents water-related damage and ensures more uniform crop growth.
Addressing satellite limitations
A common concern among farmers about satellite-based monitoring is its effectiveness during cloudy or rainy conditions. Optical sensors used for parameters such as NDVI, NDMI, and SAVI will not be able to penetrate cloud cover, which limits data availability during monsoon seasons. To overcome this limitation, radar-based satellite data is used. Radar sensors operate using microwaves and can penetrate clouds and provide reliable data regardless of weather conditions. Parameters such as the Radar Vegetation Index (RVI) and Radar Soil Moisture (RSM) are derived from this data. RVI provides information about crop health, while RSM indicates soil moisture levels. This ensures that farmers continue to receive critical insights even during periods of heavy cloud cover.
Farmonaut has also addressed accessibility challenges faced by colour-blind users. Many satellite platforms rely on colour-coded maps, where green represents healthy conditions, and red represents problematic areas. For colour-blind farmers, distinguishing between these colours can be difficult. To address this issue, Farmonaut has introduced black-and-white visualisations specifically designed for such colour-blind users. These visualisations use contrast and pattern differences instead of colour, ensuring that all users can interpret the data accurately.
There are some limitations to satellite-based crop health monitoring. Crops grown under sheds cannot be monitored due to a lack of visibility. Affordable satellite resolutions may not directly detect pests or diseases. However, the ongoing advancements in satellite resolution, sensor technology, data frequency, and artificial intelligence continue to improve accuracy and affordability for farmers. As more satellites are launched and technology evolves, these limitations are expected to reduce.
Land use intelligence
Satellite data enables land use and land classification analysis, which shows how land in a particular geography is distributed across crops, forests, residential areas, factories, and other categories. This analysis is valuable for governments, policymakers, and organisations involved in land management and planning. It helps assess how much land is under cultivation, how much is covered by forests, and how land use patterns are changing over time. Such insights support informed policy decisions related to agriculture, conservation, urban expansion, and environmental sustainability.
Crop area estimation is another important application of satellite-based monitoring. It helps determine how much land is under cultivation for specific crops in a given region. When combined with yield estimation, crop area estimation provides valuable insights into total production levels. This information is particularly useful for governments and companies involved in procurement, food processing, and supply chain planning. For example, companies that rely heavily on agricultural raw materials can estimate whether sufficient production is available locally or whether imports will be required. Governments can assess food security levels and plan imports or exports accordingly. The accuracy of crop area and yield estimation typically ranges between 85% and 95%.
Benefits of satellite monitoring
Satellite-based crop health monitoring has demonstrated significant benefits in reducing crop losses, often ranging from 10% to 35%. Early detection of stress, disease risk, or water issues allows farmers to take timely corrective actions. By addressing problems before they escalate too much, farmers can protect their yields and maintain consistent productivity. This proactive approach reduces financial risk for them and improves farm resilience.
Another major benefit of satellite-based monitoring is the reduction in farm input costs. By identifying specific patches where problems exist, farmers can apply fertilisers, pesticides, or water only where needed. This targeted approach reduces expenditure on inputs, minimises environmental impact, and protects soil health. Over time, it also improves overall farm profitability.
Large organisations managing thousands of acres traditionally rely on extensive ground surveys to assess crop conditions. These surveys are labour-intensive, time-consuming, and expensive. Satellite-based monitoring allows organisations to identify the underperforming areas remotely. Ground teams are sent only to locations that require immediate attention. This targeted approach has resulted in survey time reductions of up to 95% for some organisations. Satellite technology also supports plantation management. It enables tree counting, age estimation, area measurement, and uprooting analysis across large plantations. Manual tree surveys are time-consuming and prone to errors, especially across multiple locations. Satellite-based analysis provides faster and more accurate insights, helping plantation owners maintain optimal tree density and productivity.
Smart farming workflow
Using Farmonaut’s solution is straightforward. Farmers begin by geotagging their farms and defining field boundaries within the application. Once this is done, satellite data is processed automatically. Reports are generated within five to ten minutes and shared via WhatsApp, email, and mobile or web applications. Satellite visits typically occur every three to five days, depending on location, ensuring regular updates.
The platform integrates weather data from nearby weather stations rather than relying solely on satellite-based weather data. Farmers receive real-time weather information and forecasts, which are combined with satellite insights to generate actionable advisories. These advisories help farmers plan irrigation, fertilisation, and other farm activities more effectively.
By combining satellite data, weather data, location information, and historical records, Farmonaut has developed the JEEVN AI system. This system provides irrigation schedules, pest and disease predictions, nutrient recommendations, harvesting timelines, and yield forecasts. The AI analyses NDVI time series, weather patterns, and farm-specific data to generate predictive advisories. Yield estimation accuracy reported by clients exceeds 90%, making it a reliable decision-support tool. The farmers have just subscribed to the AI services. They will get consultancy services. The data will give a clear indication of when the field has to be irrigated, how much irrigation is needed, if there is any pest attack, and if so, the solutions. The farmers can see this UI feature on the mobile application and web application.
Satellite-based crop health monitoring is transforming agriculture by enabling data-driven decision-making. By monitoring crop health, irrigation, soil conditions, terrain, and weather from space, farmers and organisations can improve productivity, reduce losses, and adopt more sustainable practices. Platforms such as Farmonaut convert complex satellite data into clear, actionable insights that farmers can easily understand and apply. This technology empowers farmers, supports agribusinesses, and contributes to a more resilient and efficient agricultural ecosystem worldwide. The farmers do not get confused with the data; they know what they have to do with the data. Farmonaut’s mobile application on both Android and iOS will give the colour coded image to know about the location’s conditions. They need to check it every day to take action accordingly.
Contact details
Ashish Kumar
Senior Product Manager, Farmonaut Technologies Pvt. Ltd, Bangalore, Karnataka
Mobile: 78995 27307
Email: ashish@farmonaut.com
Crop health monitoring using technology
Ashish Kumar, Senior Product Manager at Farmonaut Technologies Pvt. Ltd., Bangalore, Karnataka, describes how the use of satellites in crop health monitoring has transformed traditional farming methods. Remote sensing data allows for early detection of crop stress, disease, and water deficiency. This enables farmers to respond promptly and minimise potential losses. The technology also supports informed planning and sustainable agricultural development.
What exactly does crop health monitoring using satellites mean? Most people think of satellites primarily as a technology used for GPS tracking or location-based services. In fact, satellites are capable of doing far more than that. In modern agriculture, satellite technology has emerged as a powerful tool that helps farmers, organisations, startups, companies, and governments to monitor crops, soil, irrigation, and land conditions from space with remarkable accuracy.
We can harness the power of satellites to keep a vigilant and close watch on crops spread across hundreds or even thousands of acres. This advanced technology provides a wealth of data that offers detailed insights into crop health, irrigation requirements, soil organic carbon levels, terrain variations, weather conditions, and several other agricultural parameters. By leveraging this data, it becomes possible to boost crop yields, minimise crop losses, and optimise irrigation and fertilisation practices. Ultimately, satellite-based crop health monitoring supports better decision-making, higher productivity, reduced costs, and more sustainable farming systems.
This article explains the concept of satellite-based crop health monitoring in detail and explores how Farmonaut is developing this technology. It discusses the various parameters used on the platform, how they can be applied to different crop and plantation scenarios, and how the resulting insights can be converted into practical advisories for farmers, agribusinesses, financial institutions, and policymakers. The explanation avoids unnecessary technical complexity while providing sufficient depth and clarity.
On the Farmonaut platform, there are multiple parameters used to provide insights to farmers, organisations, startups, and companies. But what exactly does the term “parameters” mean in this context? Parameters refer to the satellite-derived images or indices that are used to analyse and interpret agricultural conditions. These parameters are mathematical combinations of data captured by satellite sensors operating across different wavelengths of light and, in some cases, microwaves. Each parameter highlights a specific aspect of crop or soil behaviour, such as vegetation vigour, moisture availability in the soil, water stress, terrain variation, or organic matter content in the soil.
These parameters nowadays form the backbone of health advisories, irrigation advisories, soil assessments, and decision-support systems. Different crops, soil types, planting patterns, and climatic conditions behave differently, which is why a single parameter cannot be applied universally. Farmonaut uses a combination of parameters to ensure that insights remain accurate, highly reliable, and relevant even across diverse agricultural environments. By combining multiple parameters rather than relying on a single index, the platform delivers a holistic view of farm health. This approach enables early detection of problems and supports informed, timely interventions.
For crops such as maize or soybean, which typically have a dense canopy and significant height, the NDRE parameter is used. NDRE stands for Normalised Difference Red Edge Index. This index is particularly effective when crops have thick foliage and high biomass because it uses the red-edge portion of the electromagnetic spectrum, which is highly sensitive to changes in plant health. The NDRE parameter provides detailed crop health data for a specific field and identifies the exact locations where something may be going wrong. It highlights stress zones, growth irregularities, nutrient deficiencies, or early signs of disease that may not yet be visible to the naked eye.
Optimising crop and water health
As the saying goes, prevention is better than a cure. If farmers are informed well in advance that a particular patch of their farm is underperforming, they can take corrective measures immediately. This may include adjusting irrigation, applying fertilisers, or inspecting the crop for attack of pests or diseases. Early intervention prevents problems from spreading and helps protect the overall yield. In such situations, crop health parameters like NDRE become highly invaluable tools for proactive farm management.
For crops such as wheat and paddy, where the canopy is less dense and the crop structure differs from that of maize or soybean, the NDVI parameter is used. NDVI stands for Normalised Difference Vegetation Index, and it is one of the most widely used vegetation indices in agriculture nowadays. NDVI provides insights into vegetation vigour by comparing reflected light in the red and near-infrared bands. It effectively indicates how healthy and active the crop is across different locations within a field. NDVI helps to identify areas where crop health is declining and highlights zones that require closer inspection and action. This allows farmers to focus their efforts on specific problem areas rather than treating the entire field uniformly. Targeted intervention saves time, reduces input costs, and improves overall farm efficiency.
Tree plantations such as mango orchards present some unique challenges for satellite-based monitoring. Trees are usually planted with specific spacing between them, resulting in exposed soil between the canopies. In such cases, using only NDVI or NDRE may produce inaccurate results because soil reflectance interferes with vegetation signals. To address this issue, the Soil Adjusted Vegetation Index, or SAVI, is used. SAVI is designed to minimise the impact of exposed soil by incorporating necessary soil adjustment factors into the calculation. This makes it more suitable for plantations and orchards where canopy cover is mostly discontinuous. SAVI provides a clearer picture of plantation health and helps identify stressed or underperforming trees. In addition to SAVI, other indices such as EVI and VARI are also used in similar scenarios. These indices further refine the analysis and improve the accuracy in complex plantation environments. Put together, these parameters enable effective monitoring of orchards, helping plantation owners to maintain tree health, manage productivity, and plan timely interventions.
Irrigation is one of the most critical aspects of farming, and yet it is often managed using traditional practices rather than basing it on precise data. Many farmers irrigate their entire fields uniformly without knowing which areas actually require more frequent irrigation and which do not. Both under-irrigation and over-irrigation can be harmful. Under-irrigation deprives crops of essential moisture, leading to stress, reduced growth, and lower yields. Over-irrigation, on the other hand, can create favourable conditions for pest and disease development, increase nutrient leaching, and degrade the soil structure further. Excess water also results in unnecessary expenditure and wastage of valuable resources. Understanding irrigation requirements at fields at a micro-level is therefore essential for modern agriculture.
To support precise irrigation management, Farmonaut uses NDMI, NDWI, and evapotranspiration parameters. NDMI provides information about soil moisture levels in a particular field. It indicates whether the soil is adequately moist or experiencing dryness. NDWI reflects the amount of water present within the vegetation itself, providing deeper insights into plant water content and hydration status. Evapotranspiration measures how quickly water is evaporating from the soil and transpired by the plants. By analysing these three parameters together, farmers can easily identify exactly which patches of land require more irrigation and how often. For example, a patch with high evapotranspiration and low soil moisture clearly requires more frequent irrigation. Conversely, areas with adequate moisture and lower evaporation rates may not need immediate watering. This targeted irrigation approach ensures optimal water use, prevents wastage, reduces stress on crops, and supports sustainable water management practices.
Soil organic carbon insights
Soil organic carbon is another critical parameter captured through satellite-based monitoring, which is used before sowing and after harvesting. Soil organic carbon represents the organic matter content available in the soil and plays a vital role in maintaining soil fertility, structure, and biological activity. High levels of soil organic carbon indicate healthy soil with good structure, improved water-holding capacity, and better nutrient retention. Such a soil supports strong root development in plants and consistent crop growth. Soil organic carbon also provides indirect insights into the availability of other nutrients such as nitrogen, phosphorus, potassium, zinc, and sulphur. In many cases, areas with declining soil organic carbon also show deficiencies in these nutrients. Traditionally, farmers collect random soil samples for laboratory testing. However, this approach can miss the problematic patches, especially in large or uneven fields. Farmonaut recommends prioritising soil sampling in areas where satellite data indicates lower soil organic carbon levels. By focusing on these areas first, farmers can conduct more effective soil testing and apply fertilisers or make amendments precisely where they are needed. The
Managing terrain variations
Terrain variation also plays a significant role in irrigation efficiency and crop performance. The Digital Elevation Model, or DEM, provides detailed information about land surface elevation across a farm. In fields that have uneven terrain, water tends to accumulate in lower areas, leading to waterlogging, while higher areas may remain dry. Waterlogging can damage crops, reduce oxygen availability to roots, and increase the risk of disease. DEM-based insights help farmers understand these variations in advance. With this information, they can plan on land levelling, drainage systems, or controlled irrigation strategies. This proactive approach prevents water-related damage and ensures more uniform crop growth.
Addressing satellite limitations
A common concern among farmers about satellite-based monitoring is its effectiveness during cloudy or rainy conditions. Optical sensors used for parameters such as NDVI, NDMI, and SAVI will not be able to penetrate cloud cover, which limits data availability during monsoon seasons. To overcome this limitation, radar-based satellite data is used. Radar sensors operate using microwaves and can penetrate clouds and provide reliable data regardless of weather conditions. Parameters such as the Radar Vegetation Index (RVI) and Radar Soil Moisture (RSM) are derived from this data. RVI provides information about crop health, while RSM indicates soil moisture levels. This ensures that farmers continue to receive critical insights even during periods of heavy cloud cover.
Farmonaut has also addressed accessibility challenges faced by colour-blind users. Many satellite platforms rely on colour-coded maps, where green represents healthy conditions, and red represents problematic areas. For colour-blind farmers, distinguishing between these colours can be difficult. To address this issue, Farmonaut has introduced black-and-white visualisations specifically designed for such colour-blind users. These visualisations use contrast and pattern differences instead of colour, ensuring that all users can interpret the data accurately.
There are some limitations to satellite-based crop health monitoring. Crops grown under sheds cannot be monitored due to a lack of visibility. Affordable satellite resolutions may not directly detect pests or diseases. However, the ongoing advancements in satellite resolution, sensor technology, data frequency, and artificial intelligence continue to improve accuracy and affordability for farmers. As more satellites are launched and technology evolves, these limitations are expected to reduce.
Land use intelligence
Satellite data enables land use and land classification analysis, which shows how land in a particular geography is distributed across crops, forests, residential areas, factories, and other categories. This analysis is valuable for governments, policymakers, and organisations involved in land management and planning. It helps assess how much land is under cultivation, how much is covered by forests, and how land use patterns are changing over time. Such insights support informed policy decisions related to agriculture, conservation, urban expansion, and environmental sustainability.
Crop area estimation is another important application of satellite-based monitoring. It helps determine how much land is under cultivation for specific crops in a given region. When combined with yield estimation, crop area estimation provides valuable insights into total production levels. This information is particularly useful for governments and companies involved in procurement, food processing, and supply chain planning. For example, companies that rely heavily on agricultural raw materials can estimate whether sufficient production is available locally or whether imports will be required. Governments can assess food security levels and plan imports or exports accordingly. The accuracy of crop area and yield estimation typically ranges between 85% and 95%.
Benefits of satellite monitoring
Satellite-based crop health monitoring has demonstrated significant benefits in reducing crop losses, often ranging from 10% to 35%. Early detection of stress, disease risk, or water issues allows farmers to take timely corrective actions. By addressing problems before they escalate too much, farmers can protect their yields and maintain consistent productivity. This proactive approach reduces financial risk for them and improves farm resilience.
Another major benefit of satellite-based monitoring is the reduction in farm input costs. By identifying specific patches where problems exist, farmers can apply fertilisers, pesticides, or water only where needed. This targeted approach reduces expenditure on inputs, minimises environmental impact, and protects soil health. Over time, it also improves overall farm profitability.
Large organisations managing thousands of acres traditionally rely on extensive ground surveys to assess crop conditions. These surveys are labour-intensive, time-consuming, and expensive. Satellite-based monitoring allows organisations to identify the underperforming areas remotely. Ground teams are sent only to locations that require immediate attention. This targeted approach has resulted in survey time reductions of up to 95% for some organisations. Satellite technology also supports plantation management. It enables tree counting, age estimation, area measurement, and uprooting analysis across large plantations. Manual tree surveys are time-consuming and prone to errors, especially across multiple locations. Satellite-based analysis provides faster and more accurate insights, helping plantation owners maintain optimal tree density and productivity.
Smart farming workflow
Using Farmonaut’s solution is straightforward. Farmers begin by geotagging their farms and defining field boundaries within the application. Once this is done, satellite data is processed automatically. Reports are generated within five to ten minutes and shared via WhatsApp, email, and mobile or web applications. Satellite visits typically occur every three to five days, depending on location, ensuring regular updates.
The platform integrates weather data from nearby weather stations rather than relying solely on satellite-based weather data. Farmers receive real-time weather information and forecasts, which are combined with satellite insights to generate actionable advisories. These advisories help farmers plan irrigation, fertilisation, and other farm activities more effectively.
By combining satellite data, weather data, location information, and historical records, Farmonaut has developed the JEEVN AI system. This system provides irrigation schedules, pest and disease predictions, nutrient recommendations, harvesting timelines, and yield forecasts. The AI analyses NDVI time series, weather patterns, and farm-specific data to generate predictive advisories. Yield estimation accuracy reported by clients exceeds 90%, making it a reliable decision-support tool. The farmers have just subscribed to the AI services. They will get consultancy services. The data will give a clear indication of when the field has to be irrigated, how much irrigation is needed, if there is any pest attack, and if so, the solutions. The farmers can see this UI feature on the mobile application and web application.
Satellite-based crop health monitoring is transforming agriculture by enabling data-driven decision-making. By monitoring crop health, irrigation, soil conditions, terrain, and weather from space, farmers and organisations can improve productivity, reduce losses, and adopt more sustainable practices. Platforms such as Farmonaut convert complex satellite data into clear, actionable insights that farmers can easily understand and apply. This technology empowers farmers, supports agribusinesses, and contributes to a more resilient and efficient agricultural ecosystem worldwide. The farmers do not get confused with the data; they know what they have to do with the data. Farmonaut’s mobile application on both Android and iOS will give the colour coded image to know about the location’s conditions. They need to check it every day to take action accordingly.
Contact details
Ashish Kumar
Senior Product Manager, Farmonaut Technologies Pvt. Ltd, Bangalore, Karnataka
Mobile: 78995 27307
Email: ashish@farmonaut.com