Vishwanath Nandagudi, Co-Founder, Arnetta Technologies India Pvt Ltd, Bangalore, Karnataka, describes how digital systems are reshaping hybrid seed operations by automating key processes and improving workflow management. They enable real-time tracking, better resource allocation, and consistent product quality. By adopting these solutions, companies can boost efficiency and remain competitive in a fast-growing market.
What does seed discovery mean? Seed discovery occurs whenever the farming community requires better-quality hybrid seeds; there is a set of processes that take place before commercialising them. So we ensure that all of this particular process takes place much faster than it was done before, so that the farmers are able to get better quality hybrid seeds as soon as possible, and they are able to get better results out of their own farming practices as such. We have our presence in India, USA, and one of the main beliefs that we started with is that competitive advantage which is when a company in the seed industry develops new quality hybrid seeds they should have that competitive advantage so that they can fight the multinationals in a better way and faster way to develop a new hybrid seed according to the challenges of the ecosystem, the climate and the various factors. Climate is one, the different uses of the hybrid seed are another, and based on that, if we can develop a better new quality hybrid seed at a faster pace, that company will be more successful.
There are basically three processes within the seed company that require software automation. One is the breeding process, the second is the hybrid seed production process that needs a lot of technology to kind of develop that particular seed at a mass level. And then finally, there is a sales process. Those kinds of processes are also automated as such. So most of the Agri-inputs like our seed, fertilisers, pesticides, irrigation, etc. and the good quality seeds contribute to almost 50% of the overall need to ensure that the farmer is productive. So, to ensure that the farmer gets the best quality hybrid seeds at the earliest for good yield and food security for the entire population, the time factor is very critical. As of now, seed companies invest up to 15% of their revenue into seed production, ensuring better quality seeds, etc. So seed discovery is a very complex process, as such, and it needs a lot of investment of money and time. So our solutions are largely related to questions such as what is the entire opportunity that we're talking about, what is the problem that we are trying to address. During the seed discovery and production process, millions of data points are collected and analysed between the 3 to 6-year cycle of the hybrid seed process. So whenever a particular scientist conceptualises that he wants to develop a new better better-quality hybrid seed, he fuses two parents, does multiple tests of parental seeds and then comes up with an offspring or a new seed that can be of better quality, yield, disease resistance, and more shelf life. So the seed company would analyse these data points between 3 and 6-year cycles, and all this data needs to be recorded and statistically analysed.
Streamlined field data
So seed companies and research institutions or agricultural universities are investing a lot of time and money into building a better quality seed for better yield. As of now, most of them use basically manual or semi-automatic data collection, which is very time-consuming. So it is very important that they use software to automate all of this because most of the time, the manual or semi-automatic data collection is inefficient and inaccurate. The IBRMS is the integrated breeding and research management system. It is very similar to any other system available worldwide. So there are three processes within any seed company when they develop a new hybrid seed. One is where they do it in the nursery in the lab, wherein they develop new parental hybrids. Go on to trial this in their own farm, and then they use the farmer’s field, which we call a PD or a product development. The same terminology is taken into consideration, and all the historical data that is there from the past research is collected and put across in the system. The software ensures that all of this is automated and is available on the cloud. So nobody is able to steal any particular data, and it is all deployed on the cloud for analysis.
We also have something called functional planning and project design, wherein, at a plot level, we can tell them what it costs to actually develop a new hybrid seed, and the scientist can tell the seed company about it. The next is called a seed production system, wherein we plan our production, and whenever the seed needs to be multiplied, we hand out sample seeds or breeder seed to the growers or farmers. The farmers multiply that and give it back to the seed company, and then it is packaged and then sold to the distributor or the retailer. So the entire order stock and the dispatch management and analysing how the crop is doing or how the grower is performing, all of that is something that the system will handle. Right now, all of this is done manually or in Excel sheets. The idea of using software is that it is easy to understand the data, implement, and interpret the data.
Then comes the data capture. Now there are software-specific mobile apps by which they are able to record the particular data of the trait or the performance of the particular crop in the field. The record of that is mapped along with the experiment. So the experiment is basically for a particular crop and a particular crop type and a product segment, and then all of this data is again sent back to the system and the system analyses based on statistical reporting if that particular new hybrid seed is performing to the attributes that we have thought of. What we have also developed is something called genomic prediction or genotyping. So instead of doing 10,000 experiments in a particular land bank, we can do it in 1,000 experiments and actually start predicting whether this particular crop has that particular quality using machine learning algorithms and neural networks.
Then we have phenotyping, in which we use unmanned aerial vehicles and UAVs and soil sensors to analyse the data without human intervention. So there is no human error. So again, all of that data is taken, and there is a learning algorithm that captures the traits accurately and ensures that there is an interpretation of the trait and its performance on the field. So what we do is basically the digitisation of field and lab data. So this is very critical to all the seed companies, irrespective of who is doing what, we call creating a foundational data layer. So it is very critical for them to put a foundational what we call in the technology parlance, as a foundational data layer across the R&D process. To ensure that they can take advantage of the earlier research work and the results of it. This leads to standardisation of data nomenclatures important for the seed industry. Right now, the Indian seed industry does not have standardisation of data. Every seed company treats the data differently. So once we start putting a foundational data layer, it ensures that the nomenclatures and the standardisation of data happen effortlessly.
Boosting seed company efficiency
One more big advantage of this entire automation in the seed industry is the traceability of genetic assets. Right now, none of the seed companies can trace their genetic assets or put their intellectual property on paper because they do not know where this research was done, how it was done, how the data was recorded and whether the data is credible. So once we start using a software system, it becomes very easy to trace the genetic asset, and that becomes an asset to the organisation because we are the only company that has it. To prove the trait of a crop is very critical that they start recording that particular information as such on a software system. So what happens is that we have access to all the historical information, and we also ensure that we are the owner of that particular genetic asset.
The seed companies that use a software system have seen a 30% reduction in the commercialisation timeline. So when we talk about 3 to 6 years in commercialising a new hybrid seed, it has come down by 30% by using a software which is a big gain for the farmer because he can get better quality hybrid seeds, almost 30% much earlier than he was getting before, and it has a 2% what we call positive impact on the data because we know the cost of data acquisition and getting and recording the data properly is much easier. So there is a reduction in the manpower cost, human error, and that has a positive impact on the earnings of the seed company. There are only about four other companies that do similar products for seed companies worldwide. Agronomix, a Canadian company, and Dorian, a French company. Phenom, an Israeli company and ABS, a Dutch company. So all of these people build very similar solutions with the same objective to ensure that the farmer gets better quality hybrid seeds in a shorter period of time. All of these come as an enterprise licence fee. We charge about $10,000 per year for the seed company and about 8 lakh rupees, and we have seen that the companies earn that particular amount almost in the first year itself because of the cost reduction.
Faster hybrid seed delivery
So, crop hybrid seed breeding, hybrid seed production and the sales process are the three main pillars that help the seed companies monetise their particular opportunity and ensure that the farmer gets better quality hybrid seeds faster and more efficiently and at a much lower price. Once the breeder seed or the breeding process is completed, the breeder seed is handed over to the grower. So the grower is usually the farmer. There is no specific way to record that particular grower’s data. So the grower’s data is done by the grower himself in a more unorganised way. The software ensures that he gets a particular mobile app, and he can record the data. So all of the data with respect to the particular crop’s progress is recorded on the app, and the scientist or the breeder and the management of the seed company are able to see that almost immediately. We have seen about 30% reduction in what we call time to market which is from the time the sales team of the seed company comes with a problem statement a specific product to the research team and the research team then works on the various parental lines, and based on the parental lines they decide whether this particular new offspring by fusing two parents can give this particular yield and then they test it in the lab and then in the field and then the farmer’s field. This usually takes between 3 and 5 years because of data collection, data analysis and data interpretation. All of this is now reduced by 30%. So quickly the actual seed goes to the customer, they actually plant the seed and realise results 30% faster.
Cloud-powered seed analytics
All of this data is hosted on the cloud. We roughly import almost 15 years of historical data. So the system keeps the 15 years of historical data, analysing at the back end. Although the current year data is taken into consideration at any point in time, the historical data is always kept in the background, and the system keeps analysing using various machine learning algorithms, and it keeps prompting the particular seed breeder. So we largely use two levels of data based on statistical reports. We use stability analysis. We use regression graphs, and we also use pooled analysis and various kinds of reports based on the design of the farm. So we have three designs that we have. One is what we call RCBD, which is the random block design. Second is an augmented design. Third is a nested design. So, based on the design and needs of the particular field, we give different reports and data. On the production side, we do grower performance reports wherein we analyse whether a grower or the farmer can produce x number of seeds in the shortest period of time and how many resources they have used. Second is how much cost he is incurring in producing that particular amount of seeds. The third most important parameter that we use on the production side is the various kinds of farm practices that he used.
The software ensures that they are able to produce better quality hybrid seeds. So, when we mean better quality hybrid seeds, what we mean is that if there is a correction needed in the particular research thinking or breeding philosophy, it is implemented or corrected immediately, rather than waiting for the entire cycle to complete. The final seed quality is something that we do not interfere with because the quality certification and the quality standards that they have set are handled outside of the system. So all of our, both the software basically the breeding, especially the breeding management software has captured what we call DUS trait to provide a report to the National Seed Corporation (NSC) One of the biggest problems that we have seen in the last four or five years is that most of this data is kept in different places and the breeder or the head of breeding or head of research is not able to get the data at the time that he wants to submit it to the regulator. So the software ensures that all of the traits that are relevant to the regulator are met to get the regulatory approval much faster. We have both an Android and Apple iOS mobile app, and we usually recommend tablets, tablets that can be used in the field. We have two kinds of apps. One is for the breeder, and one is for the field assistant. So whenever a particular experiment in a breeding life cycle is designed, that particular experiment is assigned to a particular field assistant who will enter the particular values on their Android device or iOS device. He will be able to sync that particular data and send that data to the breeder.
So currently, we have a small module within our software that handles seed inventory, wherein automatically we use QR codes that are given on the breeder seeds and on the production-ready seeds, and they are captured by the particular system, and we know what quantity has been shipped over. We do not track the logistics, but we know what the inventory with the company is and what is available in the field. It helps crop breeders collect data faster, analyse using statistical tools, and interpret that particular data through dashboards to understand whether that particular crop has given them the result that they want. So data collection, data analysis and data interpretation are the three main features that the software helps the breeders. Each crop can have multiple experiments and can record multiple traits. So, as soon as you sign up as a breeder, all the traits related to rice are listed. We can choose all the traits and create a new experiment, and choose the locations where we want to do this. There are random block designs, augmented design and the nested design. Once I choose the design, it will be a field book that the breeder can then choose to push through a mobile app to the particular field assistant in the field. The field assistant records the particular data with respect to the crop performance and gives back the particular data to the breeder, and once the breeder receives this particular data, he analyses that using the statistical tools and gives a particular report.
Rapid hybrid seed response
In the seed industry, a real-time scenario does not really exist because most of the data is analysed a little later as such. So most of the time, what happens is that once the data is submitted from the field, it takes almost 1 week to 10 days for the breeder to actually analyse and come up with the result of the data. One of the biggest benefits of the product for the seed industry is ensuring that, naturally, with the advent of social media, the farmer is very well-informed as to what variety he wants and what kind of yield he is expecting out of his farm. One thing that the software automation ensures is that the supply chain is very well fed. Whenever a farmer demands a new product saying that there is a new pest that has come up and if we have that pest resistant particular seed, the seed company can supply that particular new quality hybrid seed at a much faster pace So whenever the client or a farmer demands a new particular hybrid seed and wants a particular quality the seed company can develop the seed faster and supply to the particular farmer. That way, the farmer is also very happy because he can cater to the needs of people who want a particular kind of vegetable or fruit.
High-tech seed systems
So we started our venture with the seed division of Rallis, working with them to ensure the best practices that are needed to build such a system. So the remote monitoring is done through mobile apps, which are camera-enabled. So we have ensured that the photos and the videos can be captured and tagged to a particular trait as proof of that particular trait. We leave the deployment of the solution completely to the client. We do not interfere with that. All the system needs is a web-based application. All it needs is a stable internet connection and a browser. So we handle and control the yearly access with a small key, which we use to control the access. The entire installation is done by us, but at the same time, it is done in the facility provided by the company. So we do not take any responsibility for data leaks or data privacy breaches. So we have built the software, which is a very easy-to-use web-based system. So any additional reports can be integrated very easily with very minimal effort. The only change that is required is new reports or a new kind of dashboard views, which can be easily handled by the system. We can customise the software however we want.
So one of the biggest developments that we have seen in the South Asian scenario is crop performance prediction. Basically, the research costs are growing very high. So the seed companies are very keen to start using their historical data using machine learning and artificial intelligence to start predicting a particular outcome of the experiment or of the research project. So that is something that we see as a great game changer in the time to come. So basically, that reduces the cost for the seed company with respect to the research, and also ensures that the time to market will be drastically reduced. The second thing is the use of technology to collect data. So it could be UAVs, drones, soil sensors, it could be micro-irrigation trackers to ensure that all of this data is captured without human intervention. The third one is more about marker-assisted breeding or using genotyping. So, genomic markers and marker-assisted breeding are needed, but since the biotech cost is fairly very high, there are very few seed companies that can adopt or that can pay for it, but that is what is needed now.
Contact details
Vishwanath Nandagudi
Co-Founder, Arnetta Technologies India Pvt Ltd, Bangalore, Karnataka
Email: info@arnettatech.com
What does seed discovery mean? Seed discovery occurs whenever the farming community requires better-quality hybrid seeds; there is a set of processes that take place before commercialising them. So we ensure that all of this particular process takes place much faster than it was done before, so that the farmers are able to get better quality hybrid seeds as soon as possible, and they are able to get better results out of their own farming practices as such. We have our presence in India, USA, and one of the main beliefs that we started with is that competitive advantage which is when a company in the seed industry develops new quality hybrid seeds they should have that competitive advantage so that they can fight the multinationals in a better way and faster way to develop a new hybrid seed according to the challenges of the ecosystem, the climate and the various factors. Climate is one, the different uses of the hybrid seed are another, and based on that, if we can develop a better new quality hybrid seed at a faster pace, that company will be more successful.
There are basically three processes within the seed company that require software automation. One is the breeding process, the second is the hybrid seed production process that needs a lot of technology to kind of develop that particular seed at a mass level. And then finally, there is a sales process. Those kinds of processes are also automated as such. So most of the Agri-inputs like our seed, fertilisers, pesticides, irrigation, etc. and the good quality seeds contribute to almost 50% of the overall need to ensure that the farmer is productive. So, to ensure that the farmer gets the best quality hybrid seeds at the earliest for good yield and food security for the entire population, the time factor is very critical. As of now, seed companies invest up to 15% of their revenue into seed production, ensuring better quality seeds, etc. So seed discovery is a very complex process, as such, and it needs a lot of investment of money and time. So our solutions are largely related to questions such as what is the entire opportunity that we're talking about, what is the problem that we are trying to address. During the seed discovery and production process, millions of data points are collected and analysed between the 3 to 6-year cycle of the hybrid seed process. So whenever a particular scientist conceptualises that he wants to develop a new better better-quality hybrid seed, he fuses two parents, does multiple tests of parental seeds and then comes up with an offspring or a new seed that can be of better quality, yield, disease resistance, and more shelf life. So the seed company would analyse these data points between 3 and 6-year cycles, and all this data needs to be recorded and statistically analysed.
Streamlined field data
So seed companies and research institutions or agricultural universities are investing a lot of time and money into building a better quality seed for better yield. As of now, most of them use basically manual or semi-automatic data collection, which is very time-consuming. So it is very important that they use software to automate all of this because most of the time, the manual or semi-automatic data collection is inefficient and inaccurate. The IBRMS is the integrated breeding and research management system. It is very similar to any other system available worldwide. So there are three processes within any seed company when they develop a new hybrid seed. One is where they do it in the nursery in the lab, wherein they develop new parental hybrids. Go on to trial this in their own farm, and then they use the farmer’s field, which we call a PD or a product development. The same terminology is taken into consideration, and all the historical data that is there from the past research is collected and put across in the system. The software ensures that all of this is automated and is available on the cloud. So nobody is able to steal any particular data, and it is all deployed on the cloud for analysis.
We also have something called functional planning and project design, wherein, at a plot level, we can tell them what it costs to actually develop a new hybrid seed, and the scientist can tell the seed company about it. The next is called a seed production system, wherein we plan our production, and whenever the seed needs to be multiplied, we hand out sample seeds or breeder seed to the growers or farmers. The farmers multiply that and give it back to the seed company, and then it is packaged and then sold to the distributor or the retailer. So the entire order stock and the dispatch management and analysing how the crop is doing or how the grower is performing, all of that is something that the system will handle. Right now, all of this is done manually or in Excel sheets. The idea of using software is that it is easy to understand the data, implement, and interpret the data.
Then comes the data capture. Now there are software-specific mobile apps by which they are able to record the particular data of the trait or the performance of the particular crop in the field. The record of that is mapped along with the experiment. So the experiment is basically for a particular crop and a particular crop type and a product segment, and then all of this data is again sent back to the system and the system analyses based on statistical reporting if that particular new hybrid seed is performing to the attributes that we have thought of. What we have also developed is something called genomic prediction or genotyping. So instead of doing 10,000 experiments in a particular land bank, we can do it in 1,000 experiments and actually start predicting whether this particular crop has that particular quality using machine learning algorithms and neural networks.
Then we have phenotyping, in which we use unmanned aerial vehicles and UAVs and soil sensors to analyse the data without human intervention. So there is no human error. So again, all of that data is taken, and there is a learning algorithm that captures the traits accurately and ensures that there is an interpretation of the trait and its performance on the field. So what we do is basically the digitisation of field and lab data. So this is very critical to all the seed companies, irrespective of who is doing what, we call creating a foundational data layer. So it is very critical for them to put a foundational what we call in the technology parlance, as a foundational data layer across the R&D process. To ensure that they can take advantage of the earlier research work and the results of it. This leads to standardisation of data nomenclatures important for the seed industry. Right now, the Indian seed industry does not have standardisation of data. Every seed company treats the data differently. So once we start putting a foundational data layer, it ensures that the nomenclatures and the standardisation of data happen effortlessly.
Boosting seed company efficiency
One more big advantage of this entire automation in the seed industry is the traceability of genetic assets. Right now, none of the seed companies can trace their genetic assets or put their intellectual property on paper because they do not know where this research was done, how it was done, how the data was recorded and whether the data is credible. So once we start using a software system, it becomes very easy to trace the genetic asset, and that becomes an asset to the organisation because we are the only company that has it. To prove the trait of a crop is very critical that they start recording that particular information as such on a software system. So what happens is that we have access to all the historical information, and we also ensure that we are the owner of that particular genetic asset.
The seed companies that use a software system have seen a 30% reduction in the commercialisation timeline. So when we talk about 3 to 6 years in commercialising a new hybrid seed, it has come down by 30% by using a software which is a big gain for the farmer because he can get better quality hybrid seeds, almost 30% much earlier than he was getting before, and it has a 2% what we call positive impact on the data because we know the cost of data acquisition and getting and recording the data properly is much easier. So there is a reduction in the manpower cost, human error, and that has a positive impact on the earnings of the seed company. There are only about four other companies that do similar products for seed companies worldwide. Agronomix, a Canadian company, and Dorian, a French company. Phenom, an Israeli company and ABS, a Dutch company. So all of these people build very similar solutions with the same objective to ensure that the farmer gets better quality hybrid seeds in a shorter period of time. All of these come as an enterprise licence fee. We charge about $10,000 per year for the seed company and about 8 lakh rupees, and we have seen that the companies earn that particular amount almost in the first year itself because of the cost reduction.
Faster hybrid seed delivery
So, crop hybrid seed breeding, hybrid seed production and the sales process are the three main pillars that help the seed companies monetise their particular opportunity and ensure that the farmer gets better quality hybrid seeds faster and more efficiently and at a much lower price. Once the breeder seed or the breeding process is completed, the breeder seed is handed over to the grower. So the grower is usually the farmer. There is no specific way to record that particular grower’s data. So the grower’s data is done by the grower himself in a more unorganised way. The software ensures that he gets a particular mobile app, and he can record the data. So all of the data with respect to the particular crop’s progress is recorded on the app, and the scientist or the breeder and the management of the seed company are able to see that almost immediately. We have seen about 30% reduction in what we call time to market which is from the time the sales team of the seed company comes with a problem statement a specific product to the research team and the research team then works on the various parental lines, and based on the parental lines they decide whether this particular new offspring by fusing two parents can give this particular yield and then they test it in the lab and then in the field and then the farmer’s field. This usually takes between 3 and 5 years because of data collection, data analysis and data interpretation. All of this is now reduced by 30%. So quickly the actual seed goes to the customer, they actually plant the seed and realise results 30% faster.
Cloud-powered seed analytics
All of this data is hosted on the cloud. We roughly import almost 15 years of historical data. So the system keeps the 15 years of historical data, analysing at the back end. Although the current year data is taken into consideration at any point in time, the historical data is always kept in the background, and the system keeps analysing using various machine learning algorithms, and it keeps prompting the particular seed breeder. So we largely use two levels of data based on statistical reports. We use stability analysis. We use regression graphs, and we also use pooled analysis and various kinds of reports based on the design of the farm. So we have three designs that we have. One is what we call RCBD, which is the random block design. Second is an augmented design. Third is a nested design. So, based on the design and needs of the particular field, we give different reports and data. On the production side, we do grower performance reports wherein we analyse whether a grower or the farmer can produce x number of seeds in the shortest period of time and how many resources they have used. Second is how much cost he is incurring in producing that particular amount of seeds. The third most important parameter that we use on the production side is the various kinds of farm practices that he used.
The software ensures that they are able to produce better quality hybrid seeds. So, when we mean better quality hybrid seeds, what we mean is that if there is a correction needed in the particular research thinking or breeding philosophy, it is implemented or corrected immediately, rather than waiting for the entire cycle to complete. The final seed quality is something that we do not interfere with because the quality certification and the quality standards that they have set are handled outside of the system. So all of our, both the software basically the breeding, especially the breeding management software has captured what we call DUS trait to provide a report to the National Seed Corporation (NSC) One of the biggest problems that we have seen in the last four or five years is that most of this data is kept in different places and the breeder or the head of breeding or head of research is not able to get the data at the time that he wants to submit it to the regulator. So the software ensures that all of the traits that are relevant to the regulator are met to get the regulatory approval much faster. We have both an Android and Apple iOS mobile app, and we usually recommend tablets, tablets that can be used in the field. We have two kinds of apps. One is for the breeder, and one is for the field assistant. So whenever a particular experiment in a breeding life cycle is designed, that particular experiment is assigned to a particular field assistant who will enter the particular values on their Android device or iOS device. He will be able to sync that particular data and send that data to the breeder.
So currently, we have a small module within our software that handles seed inventory, wherein automatically we use QR codes that are given on the breeder seeds and on the production-ready seeds, and they are captured by the particular system, and we know what quantity has been shipped over. We do not track the logistics, but we know what the inventory with the company is and what is available in the field. It helps crop breeders collect data faster, analyse using statistical tools, and interpret that particular data through dashboards to understand whether that particular crop has given them the result that they want. So data collection, data analysis and data interpretation are the three main features that the software helps the breeders. Each crop can have multiple experiments and can record multiple traits. So, as soon as you sign up as a breeder, all the traits related to rice are listed. We can choose all the traits and create a new experiment, and choose the locations where we want to do this. There are random block designs, augmented design and the nested design. Once I choose the design, it will be a field book that the breeder can then choose to push through a mobile app to the particular field assistant in the field. The field assistant records the particular data with respect to the crop performance and gives back the particular data to the breeder, and once the breeder receives this particular data, he analyses that using the statistical tools and gives a particular report.
Rapid hybrid seed response
In the seed industry, a real-time scenario does not really exist because most of the data is analysed a little later as such. So most of the time, what happens is that once the data is submitted from the field, it takes almost 1 week to 10 days for the breeder to actually analyse and come up with the result of the data. One of the biggest benefits of the product for the seed industry is ensuring that, naturally, with the advent of social media, the farmer is very well-informed as to what variety he wants and what kind of yield he is expecting out of his farm. One thing that the software automation ensures is that the supply chain is very well fed. Whenever a farmer demands a new product saying that there is a new pest that has come up and if we have that pest resistant particular seed, the seed company can supply that particular new quality hybrid seed at a much faster pace So whenever the client or a farmer demands a new particular hybrid seed and wants a particular quality the seed company can develop the seed faster and supply to the particular farmer. That way, the farmer is also very happy because he can cater to the needs of people who want a particular kind of vegetable or fruit.
High-tech seed systems
So we started our venture with the seed division of Rallis, working with them to ensure the best practices that are needed to build such a system. So the remote monitoring is done through mobile apps, which are camera-enabled. So we have ensured that the photos and the videos can be captured and tagged to a particular trait as proof of that particular trait. We leave the deployment of the solution completely to the client. We do not interfere with that. All the system needs is a web-based application. All it needs is a stable internet connection and a browser. So we handle and control the yearly access with a small key, which we use to control the access. The entire installation is done by us, but at the same time, it is done in the facility provided by the company. So we do not take any responsibility for data leaks or data privacy breaches. So we have built the software, which is a very easy-to-use web-based system. So any additional reports can be integrated very easily with very minimal effort. The only change that is required is new reports or a new kind of dashboard views, which can be easily handled by the system. We can customise the software however we want.
So one of the biggest developments that we have seen in the South Asian scenario is crop performance prediction. Basically, the research costs are growing very high. So the seed companies are very keen to start using their historical data using machine learning and artificial intelligence to start predicting a particular outcome of the experiment or of the research project. So that is something that we see as a great game changer in the time to come. So basically, that reduces the cost for the seed company with respect to the research, and also ensures that the time to market will be drastically reduced. The second thing is the use of technology to collect data. So it could be UAVs, drones, soil sensors, it could be micro-irrigation trackers to ensure that all of this data is captured without human intervention. The third one is more about marker-assisted breeding or using genotyping. So, genomic markers and marker-assisted breeding are needed, but since the biotech cost is fairly very high, there are very few seed companies that can adopt or that can pay for it, but that is what is needed now.
Contact details
Vishwanath Nandagudi
Co-Founder, Arnetta Technologies India Pvt Ltd, Bangalore, Karnataka
Email: info@arnettatech.com
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