Dinesh Kar, Co-Founder and COO at Crop Intellix Pvt.Ltd in Hyderabad, Telangana, explains how geo-informatics and sensor-based technology are transforming modern agriculture. These tools help farmers collect and analyse data about soil, crops, and weather to make better decisions. By using geospatial information, farmers can manage their fields more accurately and increase productivity. This approach leads to smarter, more efficient, and sustainable farming practices.
Agriculture has been the base of human civilization, shaping societies and economies across the world. From the earliest days of hunting and gathering to the sophisticated practices of today, the evolution of agriculture reflects humanity’s increasing understanding of nature, technology, and productivity. Over time, agriculture has undergone several transformative revolutions, each one reshaping the way we cultivate food, manage resources, and ensure food security. Today, we are witnessing the fourth agricultural revolution, commonly referred to as Agriculture 4.0 or digital agriculture, which integrates data and technological innovation with traditional farming practices.
The earliest form of agriculture began with humans as hunters and gatherers. However, as communities grew and humans sought more stability, a major transformation occurred—the first agricultural revolution. During this period, humans transitioned to settled agriculture. People began cultivating crops in fixed locations. The development of permanent agriculture laid the foundation for complex societies, trade, and urbanisation. The second agricultural revolution, often referred to as the British Agricultural Revolution, introduced mechanisation and systematic land management. During this period, traditional methods of cultivation were supplemented with advanced tools, implements, and machines. These innovations increased productivity, improved efficiency, and created surplus production. The third revolution, the Green Revolution, emerged as a response to the growing global population and frequent food shortages. This revolution focused on developing high-yielding crop varieties, the use of chemical fertilisers, irrigation techniques, and comprehensive packages of practices from sowing to harvest. Farmers learnt about detailed schedules for land preparation, sowing, fertiliser application, pest control, and post-harvest management. The Green Revolution demonstrated how science and technology could directly improve food production and the livelihoods of people.
Today, we are experiencing the fourth agricultural revolution, Agriculture 4.0, which integrates digital technology and data-driven solutions into traditional farming. This revolution is defined by the Food and Agriculture Organization as the “marriage of data and technological innovation with farming,” aiming to increase productivity, efficiency, quality, and environmental sustainability. Agriculture 4.0 encompasses a wide array of modern practices, including soilless farming, vertical farming, controlled environment agriculture, precision agriculture, robotics, drones, satellite imaging, and the Internet of Things (IoT). This ensures that farming is not only productive but also environmentally responsible and sustainable in the face of climate change and resource scarcity. The need for such a revolution is highlighted by the increasing demands on farmers. With a growing population, farmers are expected to increase agricultural output.
Smart tools for smarter agriculture
Agriculture 4.0 relies on technologies such as big data analytics, artificial intelligence (AI), machine learning, cloud computing, blockchain, weather monitoring systems, satellite and drone imagery, robotics, and automated machinery. These technologies, when implemented correctly, provide actionable insights to farmers and stakeholders, enabling more precise and efficient agricultural practices. It is important to note that the adoption of digital agriculture is not limited to farmers alone. The agricultural value chain includes multiple stakeholders who benefit from technological innovations. Input suppliers, government agencies, non-profit organisations, financial institutions, processing industries, and mechanisation providers all utilise digital tools to improve decision-making and operational efficiency. For instance, crop insurance companies leverage satellite imagery and AI-driven models to assess crop health and damage, significantly reducing the time and cost involved in manual verification. Similarly, traders and millers can plan procurement, manage supply chains, and assess market trends. By integrating geospatial technologies and digital platforms, stakeholders across the value chain can collaborate more effectively to support farmers and enhance overall productivity. Digital agriculture can be defined as the use of advanced and innovative technologies integrated into a system or software platform to enhance crop yield and food production. These systems consolidate data from multiple sources, including satellite imagery, sensors, drones, and ground-based observations, converting raw information into actionable insights. By transforming data into knowledge and wisdom, these platforms enable informed decision-making, precise crop management, and resource optimisation. The digital agriculture market is expanding rapidly, reflecting the increasing demand for technologically enabled solutions. In 2019, the market was valued at approximately USD 11.5 billion and is projected to reach USD 20 billion by 2025, highlighting the growing opportunities in this sector.
The key technological trends driving Agriculture 4.0 include big data, digitisation, IoT, AI, machine learning, and blockchain. These technologies are applied across nine main sectors in agriculture, including crop management, farm management, automated irrigation, livestock management, drone applications, robotics, supply chain monitoring, sustainable agriculture practices, and decision support systems. In crop management, for example, remote sensing and satellite imagery allow stakeholders to monitor crop health, growth stages, soil conditions, and pest infestations. Farm management platforms integrate this data to optimise planting schedules, fertiliser application, and irrigation, reducing resource waste and maximising yield. Automated irrigation systems leverage soil moisture sensors and weather forecasts to deliver precise water amounts, conserving water and improving crop performance. Livestock management benefits from digital monitoring tools that track animal health, behaviour, and productivity. Drones support crop monitoring and spraying of inputs, while robotics addresses labour shortages by performing tasks such as harvesting, weeding, and planting with precision. Supply chain monitoring platforms track the movement, quality, and storage of agricultural commodities, ensuring that produce reaches markets efficiently and safely. Sustainable agriculture applications assess cropping systems, soil resources, environmental impact, and potential crop damage, enabling informed decisions that minimise ecological footprints. Decision support systems provide diagnostic, predictive, and prescriptive insights, allowing stakeholders to evaluate crop health, anticipate risks, and implement corrective measures.
Remote sensing, GIS, and GPS
Geospatial technology, also known as geoinformatics, plays a central role in modern agriculture. It combines remote sensing, Geographic Information Systems (GIS), and Global Positioning Systems (GPS) to collect, analyse, and visualise spatial data. Remote sensing involves capturing data, using satellites, aircraft, or drones, about crops, soil, and environmental conditions without physical contact. GIS attaches attributes to spatial data, allowing stakeholders to assess crop type, health, soil composition, and other relevant factors. GPS provides precise location information, enabling accurate mapping, monitoring, and management of agricultural land. The integration of these technologies constitutes geospatial technology 1.0, which has been in use for over five decades. GPS, or Global Positioning System, helps in telling us where the damage is exactly so that we can go there, do the investigation, and thus save time. It helps in reaching the right spot quickly.
Geospatial technology 2.0 represents an evolution of these methods, incorporating AI and machine learning to enhance speed, accuracy, and predictive capabilities. Satellite imagery covering large areas can now be analysed farm by farm, village by village, using AI models trained with historical and real-time data. This reduces the time required for manual data processing while improving the precision of insights. Raw data, such as images and sensor readings, is transformed into information by adding context, such as crop type, growth stage, or environmental conditions. Further analysis converts this information into knowledge through expert rules, allowing stakeholders to make informed decisions. Finally, the integration of experience and expertise produces actionable wisdom, which guides interventions, optimises resources, and ensures sustainable outcomes.
Digital agriculture systems, including decision support and expert systems, enable stakeholders to access actionable insights without extensive manual intervention. For instance, a satellite image can be automatically processed to identify crop type, health, and distribution. AI-powered expert systems use training data to detect patterns, classify crops, and predict yield or damage. This technology reduces the need for extensive fieldwork, accelerates decision-making, and increases operational efficiency. Applications include crop insurance assessment, precision agriculture, and farm management. Insurance companies can determine the extent and intensity of crop damage accurately, calculating compensation based on precise measurements rather than estimates. This ensures faster, fairer, and more efficient claim settlement for farmers.
Smarter ways to farm
Precision agriculture, a core component of digital agriculture, involves site-specific crop management to optimise inputs and maximise yields. While precision agriculture is often associated with large-scale farms in countries like the United States or Australia, it is increasingly relevant for smaller plots in India and Southeast Asia. By leveraging satellite imagery, drones, and sensors, farmers can monitor soil moisture, crop growth, pest infestation, and nutrient levels on a plot-by-plot basis. This allows targeted interventions such as selective irrigation, fertilisation, or pest control, reducing resource wastage and improving productivity. Precision farming also supports sustainable practices by minimising environmental impact. In the traditional farming method, we follow the package of practices, such as land preparation, seed sowing, adding fertilisers at regular intervals, etc. In precision farming, we optimize everything, doing things as per crop requirements and not as per the book. We thus avoid wastage. We optimize the resources, save the cost, time, and improve the quality of the commodity. This will give a better output with better nutrition content and better quality. It is a long-term benefit for the farmers. When doing precision farming, we can take satellite images, study each and every farm, and there are resolutions in these images. It means the quality of the image. In satellite imagery, if it is a 10 metre resolution, it means that we can get information on 10 m x 10 m. With such detailed information, we can get detailed information and manage as per the conditions without going to the field. We can prepare the data related to the crop, soil, plant condition, growth, density, canopy, etc, using the system, and we can decide to apply it in the field.
Geospatial technology success stories
Case studies from around the world illustrate the power of geospatial technology and digital agriculture. In Guatemala, high-resolution satellite imagery and AI models were used to map banana plantations, identify tree counts, and detect gaps in crop growth. NDVI (Normalized Difference Vegetation Index) analysis allowed stakeholders to assess crop health and plan targeted interventions. In West Bengal, drones were deployed over vegetable gardens to monitor crop performance, detect damaged plots, and guide precise fertiliser and pesticide applications. In Uganda, geospatial analysis supported the establishment of a sugarcane plantation, optimising land use, irrigation planning, and crop suitability assessments. In India, satellite imagery is used to monitor sowing patterns, soil moisture, and crop health, enabling timely interventions and better decision-making at the village and district levels. Another major client, which is one of the largest procurement companies for cashews globally. For them, we are doing satellite imagery and machine learning based cashew plantation crop acreage and satellite-derived index-based crop age classification in six countries of Africa and in Cambodia. We have different models like NDVI, NDWI, GCI, SAVI, and EVI that tell us about the crop condition, such as growth and condition. These indices help us in differentiating the crops into types, health, and the growth of the crops etc. We have also developed a cloud-based application to run the model to get the result at the farm level.
Agri-tech shaping the future.
Digital agriculture also contributes to environmental sustainability. Technologies such as remote sensing, drones, and AI facilitate carbon monitoring, soil health assessment, water management, and sustainable input use. Controlled environment agriculture, including net houses and polyhouses, enables the cultivation of non-seasonal crops with minimal resource use, contributing to year-round food production while conserving natural resources. These practices help farmers adapt to climate change, mitigate environmental impact, and ensure long-term productivity. The future of agriculture lies in the integration of advanced technologies with traditional knowledge, enabling data-driven, sustainable, and highly efficient farming systems. Students, professionals, and entrepreneurs entering the field have immense opportunities to develop skills in digital agriculture, contribute to innovative solutions, and participate in a rapidly expanding market. Companies leveraging geospatial technology, AI, machine learning, and precision farming have attracted significant investments, reflecting confidence in the sector’s growth potential.
Agriculture has evolved from simple subsistence practices to sophisticated, technology-driven systems capable of feeding billions while addressing environmental and economic challenges. Agriculture 4.0, powered by digital agriculture and geospatial technology, represents the next frontier, offering precise, data-informed solutions for crop management, supply chain optimisation, and sustainable production. By integrating remote sensing, GIS, GPS, AI, and IoT, stakeholders can enhance productivity, reduce costs, and make informed decisions that benefit farmers, consumers, and the environment. The opportunities for students, educators, professionals, and entrepreneurs in this sector are vast, making digital agriculture a transformative force that will shape the future of global food security and sustainable farming.
Advanced farming sensors
There are different sensors, such as a multispectral sensor, which has colour and infrared bands. The hyperspectral sensor has colour bands, shortwave, and thermal infrared bands. Yet another sensor called SAR (Synthetic Aperture Radar). When there is a cloud, the satellites with optical sensors cannot take pictures, and the microwave sensors take the data. IoT devices are also a kind of sensor that gets information from the ground on aspects like moisture, temperature, humidity, rainfall, etc. The drones can also be used to fetch the data. Soil moisture sensors are used in a smart irrigation system. Via the mobile linked to it, we can get information on whether the soil needs water and the moisture level in other places. Based on that, we can switch on the irrigation system and release water. Once it is done, we can switch off the supply. As the satellite is in space, during cloudy weather, it cannot take good imagery. In such a case, drones play a role as they can fly low and get us an image. Satellites can give medium to low resolution imagery, but a drone can give us very accurate data. It can also reach out to inaccessible areas to get the information. We can also use drones to apply pesticides or fertilizers. Some tools on the phone tell us about the damage to the plant. We just need to upload the photo and get the answer, such as nutritional deficiency, pest attack, or disease. We will also get the prescription to solve the issue.
The speaker concludes by saying that he has a B.Sc. and M.Sc. in agriculture and has done his MBA in marketing. He has worked for about 27 years in remote sensing GIS in agricultural applications. His clients are mainly from the input industry, such as seeds, fertilizer, crop insurance, agri-commodity traders, the processing industry, and government departments. He is presently working in the nature-based carbon sector on how digital technology can be used in agricultural carbon sequestration.
Contact details
Dinesh Kar
Co-Founder and COO at Crop Intellix Pvt.Ltd in Hyderabad, Telangana
Email: kar.dinesh@gmail.com
Mob: 91777 79553
Agriculture has been the base of human civilization, shaping societies and economies across the world. From the earliest days of hunting and gathering to the sophisticated practices of today, the evolution of agriculture reflects humanity’s increasing understanding of nature, technology, and productivity. Over time, agriculture has undergone several transformative revolutions, each one reshaping the way we cultivate food, manage resources, and ensure food security. Today, we are witnessing the fourth agricultural revolution, commonly referred to as Agriculture 4.0 or digital agriculture, which integrates data and technological innovation with traditional farming practices.
The earliest form of agriculture began with humans as hunters and gatherers. However, as communities grew and humans sought more stability, a major transformation occurred—the first agricultural revolution. During this period, humans transitioned to settled agriculture. People began cultivating crops in fixed locations. The development of permanent agriculture laid the foundation for complex societies, trade, and urbanisation. The second agricultural revolution, often referred to as the British Agricultural Revolution, introduced mechanisation and systematic land management. During this period, traditional methods of cultivation were supplemented with advanced tools, implements, and machines. These innovations increased productivity, improved efficiency, and created surplus production. The third revolution, the Green Revolution, emerged as a response to the growing global population and frequent food shortages. This revolution focused on developing high-yielding crop varieties, the use of chemical fertilisers, irrigation techniques, and comprehensive packages of practices from sowing to harvest. Farmers learnt about detailed schedules for land preparation, sowing, fertiliser application, pest control, and post-harvest management. The Green Revolution demonstrated how science and technology could directly improve food production and the livelihoods of people.
Today, we are experiencing the fourth agricultural revolution, Agriculture 4.0, which integrates digital technology and data-driven solutions into traditional farming. This revolution is defined by the Food and Agriculture Organization as the “marriage of data and technological innovation with farming,” aiming to increase productivity, efficiency, quality, and environmental sustainability. Agriculture 4.0 encompasses a wide array of modern practices, including soilless farming, vertical farming, controlled environment agriculture, precision agriculture, robotics, drones, satellite imaging, and the Internet of Things (IoT). This ensures that farming is not only productive but also environmentally responsible and sustainable in the face of climate change and resource scarcity. The need for such a revolution is highlighted by the increasing demands on farmers. With a growing population, farmers are expected to increase agricultural output.
Smart tools for smarter agriculture
Agriculture 4.0 relies on technologies such as big data analytics, artificial intelligence (AI), machine learning, cloud computing, blockchain, weather monitoring systems, satellite and drone imagery, robotics, and automated machinery. These technologies, when implemented correctly, provide actionable insights to farmers and stakeholders, enabling more precise and efficient agricultural practices. It is important to note that the adoption of digital agriculture is not limited to farmers alone. The agricultural value chain includes multiple stakeholders who benefit from technological innovations. Input suppliers, government agencies, non-profit organisations, financial institutions, processing industries, and mechanisation providers all utilise digital tools to improve decision-making and operational efficiency. For instance, crop insurance companies leverage satellite imagery and AI-driven models to assess crop health and damage, significantly reducing the time and cost involved in manual verification. Similarly, traders and millers can plan procurement, manage supply chains, and assess market trends. By integrating geospatial technologies and digital platforms, stakeholders across the value chain can collaborate more effectively to support farmers and enhance overall productivity. Digital agriculture can be defined as the use of advanced and innovative technologies integrated into a system or software platform to enhance crop yield and food production. These systems consolidate data from multiple sources, including satellite imagery, sensors, drones, and ground-based observations, converting raw information into actionable insights. By transforming data into knowledge and wisdom, these platforms enable informed decision-making, precise crop management, and resource optimisation. The digital agriculture market is expanding rapidly, reflecting the increasing demand for technologically enabled solutions. In 2019, the market was valued at approximately USD 11.5 billion and is projected to reach USD 20 billion by 2025, highlighting the growing opportunities in this sector.
The key technological trends driving Agriculture 4.0 include big data, digitisation, IoT, AI, machine learning, and blockchain. These technologies are applied across nine main sectors in agriculture, including crop management, farm management, automated irrigation, livestock management, drone applications, robotics, supply chain monitoring, sustainable agriculture practices, and decision support systems. In crop management, for example, remote sensing and satellite imagery allow stakeholders to monitor crop health, growth stages, soil conditions, and pest infestations. Farm management platforms integrate this data to optimise planting schedules, fertiliser application, and irrigation, reducing resource waste and maximising yield. Automated irrigation systems leverage soil moisture sensors and weather forecasts to deliver precise water amounts, conserving water and improving crop performance. Livestock management benefits from digital monitoring tools that track animal health, behaviour, and productivity. Drones support crop monitoring and spraying of inputs, while robotics addresses labour shortages by performing tasks such as harvesting, weeding, and planting with precision. Supply chain monitoring platforms track the movement, quality, and storage of agricultural commodities, ensuring that produce reaches markets efficiently and safely. Sustainable agriculture applications assess cropping systems, soil resources, environmental impact, and potential crop damage, enabling informed decisions that minimise ecological footprints. Decision support systems provide diagnostic, predictive, and prescriptive insights, allowing stakeholders to evaluate crop health, anticipate risks, and implement corrective measures.
Remote sensing, GIS, and GPS
Geospatial technology, also known as geoinformatics, plays a central role in modern agriculture. It combines remote sensing, Geographic Information Systems (GIS), and Global Positioning Systems (GPS) to collect, analyse, and visualise spatial data. Remote sensing involves capturing data, using satellites, aircraft, or drones, about crops, soil, and environmental conditions without physical contact. GIS attaches attributes to spatial data, allowing stakeholders to assess crop type, health, soil composition, and other relevant factors. GPS provides precise location information, enabling accurate mapping, monitoring, and management of agricultural land. The integration of these technologies constitutes geospatial technology 1.0, which has been in use for over five decades. GPS, or Global Positioning System, helps in telling us where the damage is exactly so that we can go there, do the investigation, and thus save time. It helps in reaching the right spot quickly.
Geospatial technology 2.0 represents an evolution of these methods, incorporating AI and machine learning to enhance speed, accuracy, and predictive capabilities. Satellite imagery covering large areas can now be analysed farm by farm, village by village, using AI models trained with historical and real-time data. This reduces the time required for manual data processing while improving the precision of insights. Raw data, such as images and sensor readings, is transformed into information by adding context, such as crop type, growth stage, or environmental conditions. Further analysis converts this information into knowledge through expert rules, allowing stakeholders to make informed decisions. Finally, the integration of experience and expertise produces actionable wisdom, which guides interventions, optimises resources, and ensures sustainable outcomes.
Digital agriculture systems, including decision support and expert systems, enable stakeholders to access actionable insights without extensive manual intervention. For instance, a satellite image can be automatically processed to identify crop type, health, and distribution. AI-powered expert systems use training data to detect patterns, classify crops, and predict yield or damage. This technology reduces the need for extensive fieldwork, accelerates decision-making, and increases operational efficiency. Applications include crop insurance assessment, precision agriculture, and farm management. Insurance companies can determine the extent and intensity of crop damage accurately, calculating compensation based on precise measurements rather than estimates. This ensures faster, fairer, and more efficient claim settlement for farmers.
Smarter ways to farm
Precision agriculture, a core component of digital agriculture, involves site-specific crop management to optimise inputs and maximise yields. While precision agriculture is often associated with large-scale farms in countries like the United States or Australia, it is increasingly relevant for smaller plots in India and Southeast Asia. By leveraging satellite imagery, drones, and sensors, farmers can monitor soil moisture, crop growth, pest infestation, and nutrient levels on a plot-by-plot basis. This allows targeted interventions such as selective irrigation, fertilisation, or pest control, reducing resource wastage and improving productivity. Precision farming also supports sustainable practices by minimising environmental impact. In the traditional farming method, we follow the package of practices, such as land preparation, seed sowing, adding fertilisers at regular intervals, etc. In precision farming, we optimize everything, doing things as per crop requirements and not as per the book. We thus avoid wastage. We optimize the resources, save the cost, time, and improve the quality of the commodity. This will give a better output with better nutrition content and better quality. It is a long-term benefit for the farmers. When doing precision farming, we can take satellite images, study each and every farm, and there are resolutions in these images. It means the quality of the image. In satellite imagery, if it is a 10 metre resolution, it means that we can get information on 10 m x 10 m. With such detailed information, we can get detailed information and manage as per the conditions without going to the field. We can prepare the data related to the crop, soil, plant condition, growth, density, canopy, etc, using the system, and we can decide to apply it in the field.
Geospatial technology success stories
Case studies from around the world illustrate the power of geospatial technology and digital agriculture. In Guatemala, high-resolution satellite imagery and AI models were used to map banana plantations, identify tree counts, and detect gaps in crop growth. NDVI (Normalized Difference Vegetation Index) analysis allowed stakeholders to assess crop health and plan targeted interventions. In West Bengal, drones were deployed over vegetable gardens to monitor crop performance, detect damaged plots, and guide precise fertiliser and pesticide applications. In Uganda, geospatial analysis supported the establishment of a sugarcane plantation, optimising land use, irrigation planning, and crop suitability assessments. In India, satellite imagery is used to monitor sowing patterns, soil moisture, and crop health, enabling timely interventions and better decision-making at the village and district levels. Another major client, which is one of the largest procurement companies for cashews globally. For them, we are doing satellite imagery and machine learning based cashew plantation crop acreage and satellite-derived index-based crop age classification in six countries of Africa and in Cambodia. We have different models like NDVI, NDWI, GCI, SAVI, and EVI that tell us about the crop condition, such as growth and condition. These indices help us in differentiating the crops into types, health, and the growth of the crops etc. We have also developed a cloud-based application to run the model to get the result at the farm level.
Agri-tech shaping the future.
Digital agriculture also contributes to environmental sustainability. Technologies such as remote sensing, drones, and AI facilitate carbon monitoring, soil health assessment, water management, and sustainable input use. Controlled environment agriculture, including net houses and polyhouses, enables the cultivation of non-seasonal crops with minimal resource use, contributing to year-round food production while conserving natural resources. These practices help farmers adapt to climate change, mitigate environmental impact, and ensure long-term productivity. The future of agriculture lies in the integration of advanced technologies with traditional knowledge, enabling data-driven, sustainable, and highly efficient farming systems. Students, professionals, and entrepreneurs entering the field have immense opportunities to develop skills in digital agriculture, contribute to innovative solutions, and participate in a rapidly expanding market. Companies leveraging geospatial technology, AI, machine learning, and precision farming have attracted significant investments, reflecting confidence in the sector’s growth potential.
Agriculture has evolved from simple subsistence practices to sophisticated, technology-driven systems capable of feeding billions while addressing environmental and economic challenges. Agriculture 4.0, powered by digital agriculture and geospatial technology, represents the next frontier, offering precise, data-informed solutions for crop management, supply chain optimisation, and sustainable production. By integrating remote sensing, GIS, GPS, AI, and IoT, stakeholders can enhance productivity, reduce costs, and make informed decisions that benefit farmers, consumers, and the environment. The opportunities for students, educators, professionals, and entrepreneurs in this sector are vast, making digital agriculture a transformative force that will shape the future of global food security and sustainable farming.
Advanced farming sensors
There are different sensors, such as a multispectral sensor, which has colour and infrared bands. The hyperspectral sensor has colour bands, shortwave, and thermal infrared bands. Yet another sensor called SAR (Synthetic Aperture Radar). When there is a cloud, the satellites with optical sensors cannot take pictures, and the microwave sensors take the data. IoT devices are also a kind of sensor that gets information from the ground on aspects like moisture, temperature, humidity, rainfall, etc. The drones can also be used to fetch the data. Soil moisture sensors are used in a smart irrigation system. Via the mobile linked to it, we can get information on whether the soil needs water and the moisture level in other places. Based on that, we can switch on the irrigation system and release water. Once it is done, we can switch off the supply. As the satellite is in space, during cloudy weather, it cannot take good imagery. In such a case, drones play a role as they can fly low and get us an image. Satellites can give medium to low resolution imagery, but a drone can give us very accurate data. It can also reach out to inaccessible areas to get the information. We can also use drones to apply pesticides or fertilizers. Some tools on the phone tell us about the damage to the plant. We just need to upload the photo and get the answer, such as nutritional deficiency, pest attack, or disease. We will also get the prescription to solve the issue.
The speaker concludes by saying that he has a B.Sc. and M.Sc. in agriculture and has done his MBA in marketing. He has worked for about 27 years in remote sensing GIS in agricultural applications. His clients are mainly from the input industry, such as seeds, fertilizer, crop insurance, agri-commodity traders, the processing industry, and government departments. He is presently working in the nature-based carbon sector on how digital technology can be used in agricultural carbon sequestration.
Contact details
Dinesh Kar
Co-Founder and COO at Crop Intellix Pvt.Ltd in Hyderabad, Telangana
Email: kar.dinesh@gmail.com
Mob: 91777 79553
