Sugarcane and tomato farmer Sandip Shinde, 34, uses the Plantix app to check his crops for diseases. His images are helping Peat build a bigger picture about the health of crops across India.

Sugarcane and tomato farmer Sandip Shinde, 34, uses the Plantix app to check his crops for diseases. His images are helping Peat build a bigger picture about the health of crops across India. Jakob Hoff

Tushar Kamble knew there was something wrong with his chilli plants when he noticed they were smaller than average, and the leaves were starting to curl.

Last year the former academic, who’d taken a bold leap into farming in his 30s, asked his neighboring farmers for advice one morning at the border between his seven acres of land and theirs. 

Kamble, 38, got lots of different opinions about aphids and disease. Then he tried an app called Plantix, and used it to take a photo of his chilli plant. The app cross-referenced it against a database 50 different species using image recognition, a type of machine-learning, and within two minutes he had a different answer: his chillis weren’t getting enough water, and they’d benefit from a micronutrient spray to.

Within a few weeks, Kamble’s chillies had grown to a decent size. Today he is using the app to help him change his chemical usage so he can go organic, and export his crops to Europe. “I don’t depend on other people to help me,” he says with a smile.

Boosting the yields of one farmer’s crops may seem trivial, but smallholding farmers like him make up a very long tail of global food production.

Sandip Shinde using the Plantix app to inspect his crops.

Sandip Shinde using the Plantix app to inspect his crops. Image via Peat

Some 70% of the world’s food comes from smallholders like Kamble and there are around 500 million of them globally, says Simone Strey, co-founder and CEO of Peat, the Berlin startup that makes Plantix. Her company is using images from Kamble and others to build a bigger picture about the health of crops across India and beyond.

Peat, which Strey started with her husband and machine-learning specialist Robert Strey in 2015, now has 620,000 monthly active users, and 80% of them are in India.

Sandip Shinde is another farmer who uses Plantix on his 25 acres of land where he cultivates tomato, cauliflower and sugarcane, and despite his 13 years of experience, he believes the apps’ automated suggestion system and chat-network with other farmers has helped boost his yields too.

Farmer Tushar Kamble, 38, holding pomegranates that he grows on the same farm as his chillies.

Farmer Tushar Kamble, 38, holding pomegranates that he grows on the same farm as his chillies.Photo via Peat

But what’s good for smallholding farmers may also be useful for the world’s biggest agricultural and chemical companies. Peat has found a nexus between the two worlds for an initial business model, white-labelling its plant-recognition software for chemical company BASF, which has integrated it into its own crop management software called Maglis.

Professional farmers who use Maglis tend to have much bigger fields than the smallholders in India, and they also access Peat’s software less often – up to around 12,000 times a month. The startup charges a fee for each “call” to its API, or every time a professional farmer uses Peat’s feature on the Maglis app.

Peat co-founder and CEO Simone Strey

Peat co-founder and CEO Simone StreyImage via Peat

But Peat can use the images from these larger farms too, to help train its image-recognition algorithms. That’s potentially more valuable than the revenue stream (which is still well under $1 million annually, according to Strey). It all goes towards building predictive models about the spread of disease across crops, from wheat to potatoes to pomegranates, and the impact that might have on food production.

With one data scientist on her team and more hires in the pipeline, Strey, a former geographer and soil scientist, plans to sell those predictive insights to governments, insurers and agricultural suppliers, the latter two being “the most promising stakeholders to sell the data [to].”

“When we talk to seed and plant-protection suppliers, they say, ‘We have no data on what they need,'” says Strey of the smallholding farms. The same holds true for farmers across Africa and Asia, she adds, most of whom cultivate on land that’s less than two hectares, or about the size of a soccer field. “There’s no database for these farmers.”

Strey and her husband got the initial idea for Peat in the autumn of 2014, when they were conducting soil research in Brazil. A farmer in one village mentioned that whenever he tried searching Google for a plant disease known locally as “sudden death” it would only bring up images of car accidents, and nothing to do with crops.

With local language an obvious barrier, the pair thought it’d be easier for farmers to take a photo of a crop and get an automated answer for treatment.

Farmer Sandip Shinde using the Plantix app on tomato crops.

Farmer Sandip Shinde using the Plantix app on tomato crops. Image via Peat

Today Berlin-based Peat has 25 employees and has raised close to $5 million in venture-capital funding, with their latest Series A round led by London’s Index Ventures in December2017

Strey’s app, if it can continue to go viral across the world of smallholding farmers in the developing world, shows how the increasing ubiquity of AI, or machine-learning tools, can turn reams of data into something valuable.

Image-recognition as a form of AI once skirted the realm of sci-fi, but today thanks to off-the-shelf machine-learning libraries that are freely available from Google, Microsoft and even Amazon and Facebook, the act of “using AI” is becoming commoditized in favor of unique, hard-to-reach data like Peat’s.

Peat uses Google’s TensorFlow software library for its image-recognition tool, notes Strey. “Everyone can do this,” she says of her tool. “What we think is the most powerful in the end, is creating insights out of the data we get from our users.”

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Sugarcane and tomato farmer Sandip Shinde, 34, uses the Plantix app to check his crops for diseases. His images are helping Peat build a bigger picture about the health of crops across India.
(******** )Sugarcane and tomato farmer Sandip Shinde,34, utilizes the Plantix app to examine his crops for illness. His images are assisting Peat construct a larger image about the health of crops throughout India. Jakob Hoff

Tushar Kamble understood there was something incorrect with his chilli plants when he discovered they were smaller sized than average, and the leaves were beginning to curl.

In 2015 the previous scholastic, who had actually taken a vibrant leap into farming in his 30 s, asked his surrounding farmers for recommendations one early morning at the border in between his 7 acres of land and theirs.

Kamble, 38, got great deals of various viewpoints about aphids and illness. Then he attempted an app called Plantix, and utilized it to take a picture of his chilli plant. The app cross-referenced it versus a database 50 various types utilizing image acknowledgment, a kind of machine-learning, and within 2 minutes he had a various response: his chillis weren’t getting adequate water, and they ‘d gain from a micronutrient spray to.

Within a couple of weeks, Kamble’s chillies had actually grown to a good size. Today he is utilizing the app to assist him alter his chemical use so he can go natural, and export his crops to Europe. “I do not depend upon other individuals to assist me,” he states with a smile.

Improving the yields of one farmer’s crops might appear unimportant, however smallholding farmers like him comprise a long tail of worldwide food production.

Sandip Shinde using the Plantix app to inspect his crops.

Sandip Shinde utilizing the Plantix app to check his crops.(********* )Image through Peat(********** )

Some70% of the world’s food originates from smallholders like Kamble and there are around 500 countless them worldwide, states Simone Strey, co-founder and CEO of Peat, the Berlin start-up that makes Plantix. Her business is utilizing images from Kamble and others to construct a larger image about the health of crops throughout India and beyond.(*********** )

Peat, which Strey began with her other half and machine-learning professional Robert Strey in 2015, now has 620,000 month-to-month active users, and 80% of them remain in India.

Sandip Shinde is another farmer who utilizes Plantix on his 25 acres of land where he cultivates tomato, cauliflower and sugarcane, and in spite of his 13 years of experience, he thinks the apps’ automatic tip system and chat-network with other farmers has actually assisted improve his yields too.

Farmer Tushar Kamble, 38, holding pomegranates that he grows on the same farm as his chillies.

Farmer Tushar Kamble,38, holding pomegranates that he grows on the very same farm as his chillies. Image through Peat (*********** )

However what benefits smallholding farmers might likewise work for the world’s greatest farming and chemical business. Peat has actually discovered a nexus in between the 2 worlds for a preliminary company design, white-labelling its plant-recognition software application for chemical business BASF, which has actually incorporated it into its own crop management software application called Maglis

Expert farmers
who utilize Maglis tend to have much larger fields than the smallholders in India, and they likewise gain access to Peat’s software application less frequently -as much as around 12,000 times a month. The start-up charges a charge for each” call” to its API, or whenever an expert farmer utilizes Peat’s function on the Maglis app.

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Peat co-founder and CEO Simone Strey (***** )

Peat co-founder and CEO Simone Strey Image through Peat

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However Peat can utilize the images from these bigger farms too, to assist train its image-recognition algorithms. That’s possibly better than the profits stream( which is still well under$ 1 million yearly, according to Strey). Everything goes towards developing predictive designs about the spread of illness throughout crops, from wheat to potatoes to pomegranates, and the effect that may have on food production.(*********** )(*************** )With one information researcher on her group and more hires in the pipeline, Strey, a previous geographer and soil researcher, prepares to offer those predictive insights to federal governments, insurance providers and farming providers, the latter 2 being” the most appealing stakeholders to offer the information & lsqb; to & rsqb;.”

(*************** )” When we speak to seed and plant-protection providers, they state, ‘We have no information on what they require,'” states Strey of the smallholding farms.
The very same is true for farmers throughout Africa and Asia, she includes, the majority of whom cultivate on land that’s less than 2 hectares, or about the size of a soccer field.” There’s no database for these farmers.”

Strey and her other half got the preliminary concept for Peat in the fall of 2014, when they were carrying out soil research study in Brazil. A farmer in one town discussed that whenever he attempted browsing Google for a plant illness understood in your area as” unexpected death” it would just raise pictures of vehicle mishaps, and absolutely nothing to do with crops.

(*************** )With regional language an apparent barrier, the set believed it ‘d be simpler for farmers to take a picture of a crop and get an automatic response for treatment.

(*** )Farmer Sandip Shinde using the Plantix app on tomato crops.

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Farmer Sandip Shinde utilizing the Plantix app on tomato crops. Image through Peat

Today Berlin-based Peat has25 workers and has actually raised near$ 5 million in venture-capital financing, with their newest Series A round led by London’s Index Ventures in December 2017.

Strey’s app, if it can continue to go viral throughout the world of smallholding farmers in the establishing world, demonstrates how the increasing universality of AI, or machine-learning tools, can turn reams of information into something important.

(*************** )Image-recognition as a type of AI when skirted the world of sci-fi, however today thanks to off-the-shelf machine-learning libraries that are easily offered from Google, Microsoft and even Amazon and Facebook, the act of” utilizing AI” is ending up being commoditized in favor of special, hard-to-reach information like Peat’s.(*********** )

Peat utilizes Google’s TensorFlow software application library for its image-recognition tool, keeps in mind Strey. “Everybody can do this, “she states of her tool.” What we believe is the most effective in the end, is developing insights out of the information we receive from our users.”

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(*** )Sugarcane and tomato farmer Sandip Shinde, 34, uses the Plantix app to check his crops for diseases. His images are helping Peat build a bigger picture about the health of crops across India.

Sugarcane and tomato farmer Sandip Shinde, 34, utilizes the Plantix app to examine his crops for illness. His images are assisting Peat construct a larger image about the health of crops throughout India.(********* )Jakob Hoff(********** )(*********** )(************ )

(****************************** )Tushar Kamble understood there was something incorrect with his chilli plants when he discovered they were smaller sized than average, and the leaves were beginning to curl.

In 2015 the previous scholastic, who had actually taken a vibrant leap into farming in his 30 s, asked his surrounding farmers for recommendations one early morning at the border in between his 7 acres of land and theirs.

Kamble,38, got great deals of various viewpoints about aphids and illness. Then he attempted an app called Plantix, and utilized it to take a picture of his chilli plant. The app cross-referenced it versus a database50 various types utilizing image acknowledgment, a kind of machine-learning, and within 2 minutes he had a various response: his chillis weren’t getting adequate water, and they ‘d gain from a micronutrient spray to.

Within a couple of weeks, Kamble’s chillies had actually grown to a good size. Today he is utilizing the app to assist him alter his chemical use so he can go natural, and export his crops to Europe. “I do not depend upon other individuals to assist me,” he states with a smile.

Improving the yields of one farmer’s crops might appear unimportant, however smallholding farmers like him comprise a long tail of worldwide food production.

Sandip Shinde using the Plantix app to inspect his crops.

Sandip Shinde utilizing the Plantix app to check his crops. Image through Peat

Some 70 % of the world’s food originates from smallholders like Kamble and there are around 500 countless them worldwide, states Simone Strey, co-founder and CEO of Peat, the Berlin start-up that makes Plantix. Her business is utilizing images from Kamble and others to construct a larger image about the health of crops throughout India and beyond.

Peat, which Strey began with her other half and machine-learning professional Robert Strey in 2015, now has 620, 000 month-to-month active users, and 80 % of them remain in India.

Sandip Shinde is another farmer who utilizes Plantix on his 25 acres of land where he cultivates tomato, cauliflower and sugarcane, and in spite of his 13 years of experience, he thinks the apps’ automatic tip system and chat-network with other farmers has actually assisted improve his yields too.

Farmer Tushar Kamble, 38, holding pomegranates that he grows on the same farm as his chillies.

Farmer Tushar Kamble, 38, holding pomegranates that he grows on the very same farm as his chillies. Image through Peat

However what benefits smallholding farmers might likewise work for the world’s greatest farming and chemical business. Peat has actually discovered a nexus in between the 2 worlds for a preliminary company design, white-labelling its plant-recognition software application for chemical business BASF, which has actually incorporated it into its own crop management software application called Maglis

.

Expert farmers who utilize Maglis tend to have much larger fields than the smallholders in India, and they likewise gain access to Peat’s software application less frequently – as much as around 12, 000 times a month. The start-up charges a charge for each “call” to its API, or whenever an expert farmer utilizes Peat’s function on the Maglis app.

Peat co-founder and CEO Simone Strey

Peat co-founder and CEO Simone Strey Image through Peat

However Peat can utilize the images from these bigger farms too, to assist train its image-recognition algorithms. That’s possibly better than the profits stream (which is still well under $ 1 million yearly, according to Strey). Everything goes towards developing predictive designs about the spread of illness throughout crops, from wheat to potatoes to pomegranates, and the effect that may have on food production.

With one information researcher on her group and more hires in the pipeline, Strey, a previous geographer and soil researcher, prepares to offer those predictive insights to federal governments, insurance providers and farming providers, the latter 2 being “the most appealing stakeholders to offer the information [to].”

“When we speak to seed and plant-protection providers, they state, ‘We have no information on what they require,'” states Strey of the smallholding farms. The very same is true for farmers throughout Africa and Asia, she includes, the majority of whom cultivate on land that’s less than 2 hectares, or about the size of a soccer field. “There’s no database for these farmers.”

Strey and her other half got the preliminary concept for Peat in the fall of 2014, when they were carrying out soil research study in Brazil. A farmer in one town discussed that whenever he attempted browsing Google for a plant illness understood in your area as “unexpected death” it would just raise pictures of vehicle mishaps, and absolutely nothing to do with crops.

With regional language an apparent barrier, the set believed it ‘d be simpler for farmers to take a picture of a crop and get an automatic response for treatment.

Farmer Sandip Shinde using the Plantix app on tomato crops.

Farmer Sandip Shinde utilizing the Plantix app on tomato crops. Image through Peat

Today Berlin-based Peat has 25 workers and has actually raised near $ 5 million in venture-capital financing, with their newest Series A round led by London’s Index Ventures in December2017

.

Strey’s app, if it can continue to go viral throughout the world of smallholding farmers in the establishing world, demonstrates how the increasing universality of AI, or machine-learning tools, can turn reams of information into something important.

Image-recognition as a type of AI when skirted the world of sci-fi, however today thanks to off-the-shelf machine-learning libraries that are easily offered from Google, Microsoft and even Amazon and Facebook, the act of “utilizing AI” is ending up being commoditized in favor of special, hard-to-reach information like Peat’s.

Peat utilizes Google’s TensorFlow software application library for its image-recognition tool, keeps in mind Strey. “Everybody can do this,” she states of her tool. “What we believe is the most effective in the end, is developing insights out of the information we receive from our users.”

.