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Will AI save potatoes from future famines?

Scientists at IIT Mandi develop an AI-based model for disease detection in potato plants

The researchers are working on launching the AI-based model as a smartphone app. (Monika Grabkowska, Unsplash)
The researchers are working on launching the AI-based model as a smartphone app. (Monika Grabkowska, Unsplash)

Researchers at the Indian Institute of Technology (IIT) Mandi, have developed a computational model based on Artificial Intelligence (AI) for automated disease detection in potato crops using photographs of its leaves.

The research in collaboration with the Central Potato Research Institute, Shimla, uses AI techniques to highlight the diseased portions of the leaf and has also been published in the journal Plant Phenomics.

The computational tool developed by IIT Mandi scientists can detect blight in potato leaf images. The model is built using an AI tool called mask region-based convolutional neural network architecture and can accurately highlight the diseased portions of the leaf amid a complex background of plant and soil matter.

The researchers are further working on converting the developed tool to a smartphone application for a more practical usage.

According to the team, potatoes, in the history of the world, have been the cause of the great famine of the mid-nineteenth century that killed over a million people in Ireland and rang the death knell for the Irish language. The reason? Potato Blight.

"The blight is a common disease of the potato plant, that starts as uneven light green lesions near the tip and the margins of the leaf and then spreads into large brown to purplish-black necrotic patches that eventually leads to rotting of the plant. If left undetected and unchecked, blight could destroy the entire crop within a week under conducive conditions," said Srikant Srinivasan, Associate Professor, School of Computing and Electrical Engineering, IIT Mandi.

"In India, as with most developing countries, the detection and identification of blight are performed manually by trained personnel who scout the field and visually inspect potato foliage," he said adding, this process, as expected, is tedious and often impractical, especially for remote areas, because it requires the expertise of a horticultural specialist who may not be physically accessible.

Joe Johnson, research scholar at IIT Mandi explained that the automated disease detection can help in this regard and given the extensive proliferation of the mobile phones across the country, the smartphone could be a useful tool in this regard.

"The advanced HD cameras, better computing power and communication avenues offered by smartphones offer a promising platform for automated disease detection in crops, which can save time and help in the timely management of diseases, in cases of outbreaks," he said.

For the research, in order to develop a robust model, healthy and diseased leaf data were collected from fields across Punjab, Uttar Pradesh and Himachal Pradesh.

"It was important that the model developed should have portability across the nation. Analysis of the detection performance indicates an overall precision of 98 per cent on leaf images in field environments," Srinivasan said.

The seven-member team claimed even though potato is not a staple food in most regions of the world, it is a cash crop, and failure in it can have disastrous consequences, particularly to farmers with marginal landholding.

Thus, early detection of blight is important to prevent financial catastrophe to the farmer and the country’s economy.

"Following this success, we are now sizing down the model to a few tens of megabytes so that it can be hosted on a smartphone as an application. With this, when the farmer will photograph the leaf which appears unhealthy, the application will confirm in real-time if the leaf is infected or not," said Srinivasan.

"With this timely knowledge, the farmer would know exactly when to spray the field, saving his produce and minimising costs associated with unnecessary use of fungicides. The model is being refined as more states are covered," added Srinivasan.

He also highlighted that it would be deployed as part of the FarmerZone app that will be available to potato farmers for free.

The other members of the team include Shyam K Masakapalli from IIT Mandi along with research scholars -- Joe Johnson and Geetanjali Sharma, and Vijay Kumar Dua, Sanjeev Sharma and Jagdev Sharma from the Central Potato Research Institute, Shimla.

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