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IIT Mandi develops computational model for automated disease detection in potato crops – Times of India

SHIMLA: Scientists from the Indian Institute of Technology (IIT) Mandi, have developed a computational model for automated disease detection in potato crops utilizing images of its leaves. The analysis led by Srikant Srinivasan, Associate Professor, School of Computing and Electrical Engineering, IIT Mandi, in collaboration with the Central Potato Research Institute, Shimla, makes use of Artificial Intelligence (AI) strategies to spotlight the diseased parts of the leaf.

The computational instrument developed by the IIT Mandi scientists can detect blight in potato leaf pictures. The model is constructed utilizing an AI instrument known as masks area-primarily based convolutional neural community structure and might precisely spotlight the diseased parts of the leaf amid a fancy background of plant and soil matter.

In order to develop a strong model, wholesome and diseased leaf information have been collected from fields throughout Punjab, UP and Himachal Pradesh. It was vital that the model developed ought to have portability throughout the nation.


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Funded by the Department of Biotechnology, Government of India, the outcomes of this analysis have not too long ago been revealed in the journal Plant Phenomics, in a paper co-authored by Srikant Srinivasan, and Shyam Ok Masakapalli together with analysis students, Joe Johnson, and Geetanjali Sharma, from IIT Mandi, and Vijay Kumar Dua, Sanjeev Sharma, and Jagdev Sharma, from Central Potato Research Institute, Shimla.

“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,” defined Srinivasan in an announcement issued on Monday. This course of, as anticipated, is tedious and sometimes impractical, particularly for distant areas, as a result of it requires the experience of a horticultural specialist who will not be bodily accessible.

“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,” stated Joe Johnson, Research Scholar, IIT Mandi, whereas highlighting the sensible utilization of his analysis. The superior HD cameras, higher computing energy and communication avenues provided by smartphones provide a promising platform for automated disease detection in crops, which may save time and assist in the well timed administration of ailments, in circumstances of outbreaks.

Srinivasan stated that evaluation of the detection efficiency signifies an general precision of 98% on leaf pictures in area environments.

Following this success, the workforce is sizing down the model to a couple tens of megabytes in order that it may be hosted on a smartphone as an utility. With this, when the farmer will {photograph} the leaf which seems unhealthy, the applying will affirm in actual-time if the leaf is contaminated or not. With this well timed information, the farmer would know precisely when to spray the sector, saving his produce and minimising prices related to pointless use of fungicides.

“The model is being refined as more states are covered,” stated Srinivasan, including that it could be deployed as half of the FarmerZone app that will likely be out there to potato farmers for free.

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