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Artificial Intelligence, Machine Learning Help Shrimp, Vegetable Farmers Reap Good Harvest

Aibono works with about 500 farmers and has about 200 acres are under active cultivation

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A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images.
There are about 232,000 new cases of melanoma, and 55,500 deaths, in the world each year, the research added.

Artificial Intelligence (AI) and machine learning have entered aquaculture and agriculture farms in some states benefitting the farmers in cutting down their labour and the uncertainties of trial and error methods.

Thanks to artificial intelligence and machine learning technologies used by companies like the city-based Coastal Aquaculture Research Institute (CARI) and Aibono Smart Farming Pvt Ltd, Bengaluru, shrimp and vegetable farmers are able to increase their yield, cut their costs and have better market access.

V. Geetha, who practices aquaculture in Andhra Pradesh, told IANS: “Before signing up for CARI’s ‘FarmMOJO’ — an AI app-enabled farm advisor tool — we used to jot down the critical data in a notebook and act on it. But we wouldn’t know how much to feed the shrimp. There would either be over or under feeding.”

Since he tied up with CARI six months back, Prakash said, the company takes care of water quality tests in his pond and all the required data is available on his mobile with suggested action to be taken.

“Earlier the quantum of feed used would differ. Now we use the correct feed quantity, which has reduced the feed cost. The cost of medicines has also decreased. Earlier many would suggest several things. Now we go by what CARI says,” Prakash remarked.

“At 600,000 ton a year, India’s shrimp exports stand at Rs 45,000 crore annually. But no major technology was used so far in shrimp farming,” Rajamanohar Somasundaram, Co-Founder and CEO, CARI told IANS.

He said, now that the shrimp farming has been digitised, the data collected has been fed into the FarmMOJO programme.

“From the data, we have built machine learning. The software advises farmers on the use of feed, medicines and other things. The tool also helps in predicting the chances of a disease outbreak in the client farm based on the data available from other ponds,” he said.

The company has two revenue streams viz., subscription fee for FarmMOJO and commission on sales of products of partner companies. “At present, we have about 750 ponds spread over Tamil Nadu, Andhra Pradesh, Gujarat and Odisha. We will soon enter West Bengal.This year we plan to cover 2,500 ponds,” Somasundaram said.

Farmers, India
An Indian woman helps her farmer husband irrigate a paddy field using a traditional system, on the outskirts of Gauhati, India, Feb. 1, 2019. VOA

In agriculture, Bengaluru-based Aibono Smart Farming Pvt Ltd and its AI product are helping the farmers in Nilgiris district of Tamil Nadu to match the supply and demand of hill vegetables.

“In India, the land holdings by farmers are small. So, precision farming could be used only if the supply and demand are matched. The other problem is, good price realisation if the yield is good. Farmers do not have a foresight on what to produce and when,” Vivek Rajkumar, Founder told IANS.

Rajkumar said fruits and vegetables are a $250 billion market in India far bigger than that of fast moving consumer goods. But there are no e-commerce players in this segment.

“Aibono is like a dairy cooperative. It assures farmers of buying every kilogram of their produce at a good price so that they can make money. The average land holding of the farmers in the network ranges between 0.5 to 1.5 acre,” Rajkumar said.

“We collect about 2,000 data points like weather, soil tests, photographs etc. Open farm is like a factory without a roof. It is dynamic. But farmer’s activities are routine and predictable. But agronomy has to be changed to dynamic mode,” Rajkumar said.

With farmers seeing increasing yield but not commensurate increase in realisation, Aibono decided to look at the demand side and started to study the consumption pattern.

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“At the retail end, people buy a fixed quantity of the vegetables. The buying pattern in predictable but it is the supply that varies,” Rajkumar said.

“We signed up with retailers and hotels assuring them of supplies. For the farmers, we started calibrating issue of seeds so that the supplies could be assured at certain quantities at a specified time,” Rajkumar said.

Aibono works with about 500 farmers and has about 200 acres are under active cultivation. Rajkumar said: “we about 300 retailers in its network and next year the number is set to grow several times. We charge Re 1 per kg as fee for service to the farmers.” (IANS)

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Researchers Develop Artificial Intelligence Tool in Chest X-Rays to Predict Long Term Mortality

Each image was paired with a key piece of data: Did the person die over a 12-year period?

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artificial intelligence
The goal was for CXR-risk to learn the features or combinations of features on a chest X-ray image that best predict health and mortality. Pixabay

Researchers have developed an Artificial Intelligence (AI)-powered tool that can harvest information in chest X-rays to predict long-term mortality.

The findings of this study, published in the journal JAMA Network Open, could help to identify patients most likely to benefit from screening and preventive medicine for heart disease, lung cancer and other conditions.

“This is a new way to extract prognostic information from everyday diagnostic tests,” said one of the researchers, Michael Lu, from Massachusetts General Hospital (MGH) of Harvard Medical School. “It’s information that’s already there that we’re not using, that could improve people’s health,” Lu said. Lu and his colleagues developed a convolutional neural network – an AI tool for analysing visual information – called CXR-risk.

artificial Intelligence
Next, Lu and colleagues tested CXR-risk using chest X-rays for 16,000 patients from two earlier clinical trials. Pixabay

It was trained by having the network analyse more than 85,000 chest X-rays from 42,000 participants who took part in an earlier clinical trial. Each image was paired with a key piece of data: Did the person die over a 12-year period? The goal was for CXR-risk to learn the features or combinations of features on a chest X-ray image that best predict health and mortality.

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Next, Lu and colleagues tested CXR-risk using chest X-rays for 16,000 patients from two earlier clinical trials. They found that 53 per cent of people the neural network identified as “very high risk” died over 12 years, compared to fewer than four per cent of those that CXR-risk labeled as “very low risk.”

The study found that CXR-risk provided information that predicts long-term mortality, independent of radiologists’ readings of the x-rays and other factors, such as age and smoking status. Lu believes this new tool will be even more accurate when combined with other risk factors, such as genetics and smoking status. (IANS)