These challenges cannot be solved through technology and data insights alone as it involves policy, policymakers, government, markets, the supply chain, and various stakeholders in the chain who get the crop from field to market and into the hands of the consumer.<\/p>\n
However, scientists and researchers at IBM Research India have conducted pilot trials to show that the use of remote sensing technology, combined with artificial intelligence and blockchain can solve some of the challenges outlined above, at scale.<\/p>\n
Helping Farmers Increase Crop Yield<\/strong><\/h3>\nIBM Research India combined AI with remote sensing to drive insights for agri. Since remote sensing data is widely available, it can use AI at scale.<\/p>\n
![\"Sriram](\"https:\/\/www.digitalcreed.in\/wp-content\/uploads\/2019\/02\/Sriram-4_Fotor-300x279.jpg\")
Sriram Raghavan, Vice President-AI, IBM Research<\/p><\/div>\n
\u201cWe have the opportunity to get deep insights about a farm, without the need to rely on deep on-ground instrumentation,\u201d said Sriram Raghavan, Vice President-AI, IBM Research.<\/strong>\u00a0You can use on-ground instrumentation selectively only to do some validation. But you can do quite a bit using AI and remote\u00a0sensing\u00a0data.\u201d<\/p>\nIBM researchers combined multiple global satellite-based information sources to compute actionable agronomic insights (such as crop health stress alert, water stress, pest\/disease risk forecast), for individual farmers at a sub-acre level. Then IBM Research\u2019s big data platform called PAIRS – Geoscope<\/a> (Physical Analytics Integrated Data Repository and Services) is used to ingest, curate, and manage the big data, and build analytical layers.<\/p>\n\u201cPAIRS is a platform for managing and curating vast amounts of satellite data and doing AI on it. It consistently curates 15 – 20 petabytes of data a day. It supports sophisticated AI on top that allows us to bring together different types of satellite data,\u201d said Raghavan.<\/p>\n
IBM can also enhance the quality of the data to overcome cloud-occlusion during the Indian monsoon season, using machine learning techniques to fuse multiple information sources.<\/p>\n
\u201cWe are providing insights about a farm and built a set of services that you can think of\u00a0as\u00a0a \u2018Digital Twin\u2019 of a farm. The attributes of interest are: what is happening under the ground (soil moisture), what is happening on the ground (crop health), and what is happening just above the ground, in terms of climatic and atmospheric conditions (weather data),\u201d added Raghavan.<\/p>\n
These precision insights can be delivered to low-end smartphones (costing around $50) used by the farmers and agronomists in the fields. Farmers can now tap the rich remote sensing data, and sophisticated output of cognitive analytics to get predictive insight on their farms at the right time, to manage scarce groundwater-based irrigation, optimise the timing\/amount of fertilizers & pesticides for the right part of their farm, and time their harvest to maximise produce and minimise wastage.<\/p>\n
\u201cPilots for agri have been done in India, the ASEAN region, Brazil and the US. Within India there are four pilots in Karnataka, Maharashtra, West Bengal, and UP with different use cases, either ongoing or just completed — in the spectrum from precision agriculture<\/a> to pricing to the insurance space,\u201d informed Raghavan.<\/p>\nThe remote sensed data can also be combined with parsimonious locally sensed IoT data (soil moisture sensors, local weather stations) to get even more precise insights. This is called on-ground instrumentation.<\/p>\n
IBM also announced its next-generation weather forecasting capability.<\/p>\n
\u201cPutting our\u00a0agri\u00a0and weather capabilities together, down to the size of land holdings we see in countries like India and other emerging markets, we can offer deep insights,\u201d said Raghavan.<\/p>\n
![\"Fields,](\"https:\/\/www.digitalcreed.in\/wp-content\/uploads\/2019\/02\/fields_IBM_Fotor.jpg\")
IMAGE CREDIT: IBM Research India<\/p><\/div>\n
An example where you can clearly see the impact of farm management through remote sensing. <\/strong>Left side of the image shows a controlled field and on the right side is the reference field. In the right side, you can see that the crop got damaged due to a disease attack. It did not happen to the left field as <\/strong>curative spray was done ahead of time.<\/strong><\/p>\nBeyond Precision Agriculture<\/strong><\/p>\nSo far most of the efforts at IBM Research have been directed at precision agriculture use cases \u2013 plant pathology (saving crops from pests or diseases), soil moisture monitoring, and improving yield. Using remote sensing data IBM has contributed heavily to the welfare of farmers in India and ASEAN. But IBM now wants to go beyond that into other areas where the entire supply chain can benefit.<\/p>\n
\u201cWhile IBM has so far focussed on putting together the building blocks for precision agriculture, in 2019 we will focus on commodities and pricing as well as financing and insurance,\u201d said Raghavan.<\/p>\n
IBM will use emerging technologies like AI and blockchain for price prediction, crop insurance, financing, and credit scoring.<\/p>\n
Here are some use cases.<\/p>\n
Price Prediction:<\/strong> To be able to inform one about commodity pricing. If one can accurately estimate yield and acreage under yield, at a fine resolution, while considering the chances of spoilage because of pests or weather, one can improve the yield that enables better commodity trading. It will also enable those in the public sector to understand price fluctuations better.<\/p>\nFinancing and Credit<\/strong>: Better insights about yield from the farm can drive better decisions and expectations in insurance, credit and financing.<\/p>\nCrop Insurance<\/strong>: There are three use cases for insurance. One type is providing insights that help in pricing. Usually, insurance companies use historical data to give them a sense of what the\u00a0yield\u00a0might be. IBM wants to improve the accuracy of models used by insurers through real-time insights. That will allow them to do better pricing upfront.\u00a0But it can also inform farmers about risk exposure to their crops, halfway through the season, enabling them to do better financial planning. The third case is claims\u00a0validation, which takes time since a person on the ground goes to do the validation. They check crop loss, low yield etc. If this validation is done remotely, then that drives down the cost of validation and speeds up the issuance against the claim.<\/p>\nBanks and insurers must rely on historical information to assess the credit. And there are many parameters. The challenge is: a significant portion of the creditworthiness of the people in the ecosystem is driven by the output that they can\u00a0produce,\u00a0and the price that they are likely to get for the output. The accuracy of this assessment can be improved in more insights are available about the health of the farmers\u2019 output, and the price of their output. These become parameters to the risk model.<\/p>\n
With the rich insights it provides IBM is solving a major challenge for banks as these inputs are not available at scale today.<\/p>\n
\u201cWhile we are not claiming to build a risk model for the bank, we are providing insights that help the bank make better decisions. We are providing insights into their risk model, which is expected yield, price and risk of\u00a0crop,\u201d said Raghavan.<\/p>\n
IBM is also trying to solve a pricing challenge for the farmer to advise where to go to sell the crop, at what price, and what type of crop he should be growing.<\/p>\n
\u201cWe have not yet started working in this area, but technology innovation can also help here.\u00a0 This challenge will be addressed as we start taking our\u00a0precision\u00a0agri\u00a0work into yield prediction and acreage estimation, which has\u00a0an impact\u00a0on pricing,\u201d said Raghavan.<\/p>\n
![\"\"](\"https:\/\/www.digitalcreed.in\/wp-content\/uploads\/2019\/02\/IBM_a-potato-leaf-with-early-blight-infestation_Fotor.jpg\")
IMAGE CREDIT: IBM<\/p><\/div>\n
<\/h3>\n
This a potato leaf with early blight infestation. IBM is using Watson Decision platform and advanced image analytics to detect this disease early. Farmers can take a picture of the crop and Watson can detect the disease on the<\/strong>\u00a0fly.<\/p>\nBlockchain and the Food Supply Chain<\/strong><\/h3>\nThe movement of food from the fields to the consumer is complex as it involves numerous transactions between various entities in the supply chain. IBM is trying to simplify the process, to enable transactional information to be shared securely between all stakeholders in the food supply chain. It is using blockchain technology and has created the IBM Food Trust network.<\/p>\n
Sharing his views on the relevance of the predictions for the India market\u00a0Raghavan said, “India as a country faces many challenges\u00a0across\u00a0its Food Supply Chain\u00a0–<\/b>\u00a0small fragmented land holdings (many less than a hectare), waste in the food supply chain, and the urgent need to efficiently manage precious resources like land and water while responding to rapidly changing weather conditions.\u00a0<\/b>We believe that the digitization of the food supply chain powered by technologies like AI, IoT and Blockchain are critical tools in addressing these challenges and transforming the food ecosystem in India.”<\/span><\/p>\nIBM Food Trust<\/a> aims to create visibility and accountability in the food supply chain. This network connects growers, processors, distributors, and retailers through a permissioned, permanent and shared record of food system data.<\/p>\n\u201cThe insights derived from precision agriculture using remote sensing and AI, can now move through the supply chain and inform business processes downstream in the chain,\u201d said Raghavan. \u201cThe insights derived could predict the likelihood of a pest attack or crop disease, or the fact that the quality of output may not be high. If my vegetation index is low, then either the output will be low in volume or low in quality. If there is a high instance of pests or crop disease I can estimate the impact of the total crop yield.\u201d<\/p>\n
This kind of information is immensely useful to all stakeholders in the supply chain, who can plan, once they receive this information through the IBM Food Trust blockchain network.<\/p>\n
There is another use case. A food processor who is procuring vegetables through the supply chain, cleans and cuts these vegetables, puts it into boxes and sends it along to the distributor. The food processor spends on inspection for health and quality control. But this is done on a random sample, and it involves risk. The likelihood of risk of inspection could drive the insight on what to inspect. That is estimated by all the information flowing through the blockchain, starting from the original production, thereby mitigating risk.<\/p>\n
\u201cToday IBM Food Trust handles information only from when the food is packaged. But now with the\u00a0agri\u00a0capabilities, we can go upstream right up to production, where crops are grown. And we can drive insights such as risk insights, yield. The blockchain network carries it downstream and allows better decision making for logistics providers, food processors, and retailers,\u201d said Raghavan.<\/p>\n
That\u2019s why IBM is trying to connect its agri play to blockchain. When the two are linked, deep insights about every aspect of farming can be sent down the supply chain, thus helping all entities to make informed decisions and be better prepared for unexpected results, based on current weather conditions and crop health.<\/p>\n
![\"\"](\"https:\/\/www.digitalcreed.in\/wp-content\/uploads\/2019\/02\/IBM_palm-tree-infested-with-a-disease-called-Ganoderma_Fotor-225x300.jpg\")
IMAGE CREDIT: IBM Research India<\/p><\/div>\n
This a palm tree infested with a disease called Ganoderma. This is a very severe disease and there is no curation for this disease. If it isn\u2019t detected early, infestation might spread to the nearby trees, and all of them needs to cut. To stop spreading this disease, IBM is using remote sensing and multi-spectral image analysis to detect Ganoderma at a very early stage.<\/strong><\/p>\n
\nAlso see: From Seed to Shelf: How IBM Innovations Will Transform Every Stage of the Food Supply Chain Within the Next Five Years<\/a><\/h3>\n<\/h3>\nChallenges Ahead<\/strong><\/h3>\nWhile the network carries information and advisories, there could be some challenges on the ground. IBM sees the last mile problem as one of the biggest challenges.<\/p>\n
\u201cFor the use cases around banks and\u00a0insurance,\u00a0you don’t need feet on the ground. You just go with the data and make your decision. But for precision\u00a0agriculture,\u00a0you need feet on the ground. And that’s a challenge to do at scale,\u201d said Raghavan.<\/p>\n
Sriram Raghavan, Vice President-AI, IBM Research<\/p><\/div>\n
\u201cWe have the opportunity to get deep insights about a farm, without the need to rely on deep on-ground instrumentation,\u201d said Sriram Raghavan, Vice President-AI, IBM Research.<\/strong>\u00a0You can use on-ground instrumentation selectively only to do some validation. But you can do quite a bit using AI and remote\u00a0sensing\u00a0data.\u201d<\/p>\n IBM researchers combined multiple global satellite-based information sources to compute actionable agronomic insights (such as crop health stress alert, water stress, pest\/disease risk forecast), for individual farmers at a sub-acre level. Then IBM Research\u2019s big data platform called PAIRS – Geoscope<\/a> (Physical Analytics Integrated Data Repository and Services) is used to ingest, curate, and manage the big data, and build analytical layers.<\/p>\n \u201cPAIRS is a platform for managing and curating vast amounts of satellite data and doing AI on it. It consistently curates 15 – 20 petabytes of data a day. It supports sophisticated AI on top that allows us to bring together different types of satellite data,\u201d said Raghavan.<\/p>\n IBM can also enhance the quality of the data to overcome cloud-occlusion during the Indian monsoon season, using machine learning techniques to fuse multiple information sources.<\/p>\n \u201cWe are providing insights about a farm and built a set of services that you can think of\u00a0as\u00a0a \u2018Digital Twin\u2019 of a farm. The attributes of interest are: what is happening under the ground (soil moisture), what is happening on the ground (crop health), and what is happening just above the ground, in terms of climatic and atmospheric conditions (weather data),\u201d added Raghavan.<\/p>\n These precision insights can be delivered to low-end smartphones (costing around $50) used by the farmers and agronomists in the fields. Farmers can now tap the rich remote sensing data, and sophisticated output of cognitive analytics to get predictive insight on their farms at the right time, to manage scarce groundwater-based irrigation, optimise the timing\/amount of fertilizers & pesticides for the right part of their farm, and time their harvest to maximise produce and minimise wastage.<\/p>\n \u201cPilots for agri have been done in India, the ASEAN region, Brazil and the US. Within India there are four pilots in Karnataka, Maharashtra, West Bengal, and UP with different use cases, either ongoing or just completed — in the spectrum from precision agriculture<\/a> to pricing to the insurance space,\u201d informed Raghavan.<\/p>\n The remote sensed data can also be combined with parsimonious locally sensed IoT data (soil moisture sensors, local weather stations) to get even more precise insights. This is called on-ground instrumentation.<\/p>\n IBM also announced its next-generation weather forecasting capability.<\/p>\n \u201cPutting our\u00a0agri\u00a0and weather capabilities together, down to the size of land holdings we see in countries like India and other emerging markets, we can offer deep insights,\u201d said Raghavan.<\/p>\n IMAGE CREDIT: IBM Research India<\/p><\/div>\n An example where you can clearly see the impact of farm management through remote sensing. <\/strong>Left side of the image shows a controlled field and on the right side is the reference field. In the right side, you can see that the crop got damaged due to a disease attack. It did not happen to the left field as <\/strong>curative spray was done ahead of time.<\/strong><\/p>\n Beyond Precision Agriculture<\/strong><\/p>\n So far most of the efforts at IBM Research have been directed at precision agriculture use cases \u2013 plant pathology (saving crops from pests or diseases), soil moisture monitoring, and improving yield. Using remote sensing data IBM has contributed heavily to the welfare of farmers in India and ASEAN. But IBM now wants to go beyond that into other areas where the entire supply chain can benefit.<\/p>\n \u201cWhile IBM has so far focussed on putting together the building blocks for precision agriculture, in 2019 we will focus on commodities and pricing as well as financing and insurance,\u201d said Raghavan.<\/p>\n IBM will use emerging technologies like AI and blockchain for price prediction, crop insurance, financing, and credit scoring.<\/p>\n Here are some use cases.<\/p>\n Price Prediction:<\/strong> To be able to inform one about commodity pricing. If one can accurately estimate yield and acreage under yield, at a fine resolution, while considering the chances of spoilage because of pests or weather, one can improve the yield that enables better commodity trading. It will also enable those in the public sector to understand price fluctuations better.<\/p>\n Financing and Credit<\/strong>: Better insights about yield from the farm can drive better decisions and expectations in insurance, credit and financing.<\/p>\n Crop Insurance<\/strong>: There are three use cases for insurance. One type is providing insights that help in pricing. Usually, insurance companies use historical data to give them a sense of what the\u00a0yield\u00a0might be. IBM wants to improve the accuracy of models used by insurers through real-time insights. That will allow them to do better pricing upfront.\u00a0But it can also inform farmers about risk exposure to their crops, halfway through the season, enabling them to do better financial planning. The third case is claims\u00a0validation, which takes time since a person on the ground goes to do the validation. They check crop loss, low yield etc. If this validation is done remotely, then that drives down the cost of validation and speeds up the issuance against the claim.<\/p>\n Banks and insurers must rely on historical information to assess the credit. And there are many parameters. The challenge is: a significant portion of the creditworthiness of the people in the ecosystem is driven by the output that they can\u00a0produce,\u00a0and the price that they are likely to get for the output. The accuracy of this assessment can be improved in more insights are available about the health of the farmers\u2019 output, and the price of their output. These become parameters to the risk model.<\/p>\n With the rich insights it provides IBM is solving a major challenge for banks as these inputs are not available at scale today.<\/p>\n \u201cWhile we are not claiming to build a risk model for the bank, we are providing insights that help the bank make better decisions. We are providing insights into their risk model, which is expected yield, price and risk of\u00a0crop,\u201d said Raghavan.<\/p>\n IBM is also trying to solve a pricing challenge for the farmer to advise where to go to sell the crop, at what price, and what type of crop he should be growing.<\/p>\n \u201cWe have not yet started working in this area, but technology innovation can also help here.\u00a0 This challenge will be addressed as we start taking our\u00a0precision\u00a0agri\u00a0work into yield prediction and acreage estimation, which has\u00a0an impact\u00a0on pricing,\u201d said Raghavan.<\/p>\n IMAGE CREDIT: IBM<\/p><\/div>\n This a potato leaf with early blight infestation. IBM is using Watson Decision platform and advanced image analytics to detect this disease early. Farmers can take a picture of the crop and Watson can detect the disease on the<\/strong>\u00a0fly.<\/p>\n The movement of food from the fields to the consumer is complex as it involves numerous transactions between various entities in the supply chain. IBM is trying to simplify the process, to enable transactional information to be shared securely between all stakeholders in the food supply chain. It is using blockchain technology and has created the IBM Food Trust network.<\/p>\n Sharing his views on the relevance of the predictions for the India market\u00a0Raghavan said, “India as a country faces many challenges\u00a0across\u00a0its Food Supply Chain\u00a0–<\/b>\u00a0small fragmented land holdings (many less than a hectare), waste in the food supply chain, and the urgent need to efficiently manage precious resources like land and water while responding to rapidly changing weather conditions.\u00a0<\/b>We believe that the digitization of the food supply chain powered by technologies like AI, IoT and Blockchain are critical tools in addressing these challenges and transforming the food ecosystem in India.”<\/span><\/p>\n IBM Food Trust<\/a> aims to create visibility and accountability in the food supply chain. This network connects growers, processors, distributors, and retailers through a permissioned, permanent and shared record of food system data.<\/p>\n \u201cThe insights derived from precision agriculture using remote sensing and AI, can now move through the supply chain and inform business processes downstream in the chain,\u201d said Raghavan. \u201cThe insights derived could predict the likelihood of a pest attack or crop disease, or the fact that the quality of output may not be high. If my vegetation index is low, then either the output will be low in volume or low in quality. If there is a high instance of pests or crop disease I can estimate the impact of the total crop yield.\u201d<\/p>\n This kind of information is immensely useful to all stakeholders in the supply chain, who can plan, once they receive this information through the IBM Food Trust blockchain network.<\/p>\n There is another use case. A food processor who is procuring vegetables through the supply chain, cleans and cuts these vegetables, puts it into boxes and sends it along to the distributor. The food processor spends on inspection for health and quality control. But this is done on a random sample, and it involves risk. The likelihood of risk of inspection could drive the insight on what to inspect. That is estimated by all the information flowing through the blockchain, starting from the original production, thereby mitigating risk.<\/p>\n \u201cToday IBM Food Trust handles information only from when the food is packaged. But now with the\u00a0agri\u00a0capabilities, we can go upstream right up to production, where crops are grown. And we can drive insights such as risk insights, yield. The blockchain network carries it downstream and allows better decision making for logistics providers, food processors, and retailers,\u201d said Raghavan.<\/p>\n That\u2019s why IBM is trying to connect its agri play to blockchain. When the two are linked, deep insights about every aspect of farming can be sent down the supply chain, thus helping all entities to make informed decisions and be better prepared for unexpected results, based on current weather conditions and crop health.<\/p>\n IMAGE CREDIT: IBM Research India<\/p><\/div>\n This a palm tree infested with a disease called Ganoderma. This is a very severe disease and there is no curation for this disease. If it isn\u2019t detected early, infestation might spread to the nearby trees, and all of them needs to cut. To stop spreading this disease, IBM is using remote sensing and multi-spectral image analysis to detect Ganoderma at a very early stage.<\/strong><\/p>\n While the network carries information and advisories, there could be some challenges on the ground. IBM sees the last mile problem as one of the biggest challenges.<\/p>\n \u201cFor the use cases around banks and\u00a0insurance,\u00a0you don’t need feet on the ground. You just go with the data and make your decision. But for precision\u00a0agriculture,\u00a0you need feet on the ground. And that’s a challenge to do at scale,\u201d said Raghavan.<\/p>\n<\/h3>\n
Blockchain and the Food Supply Chain<\/strong><\/h3>\n
\nAlso see: From Seed to Shelf: How IBM Innovations Will Transform Every Stage of the Food Supply Chain Within the Next Five Years<\/a><\/h3>\n
<\/h3>\n
Challenges Ahead<\/strong><\/h3>\n