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IIT Mandi: Detect Disease In Potato Crops Using Photograph

New IIT Mandi research shows ways to detect disease in potato crops using the photograph of its leaves.

Scientists from the Indian Institute of Technology (IIT) Mandi, have developed a computational model for automated disease detection in potato crops using photographs of its leaves. The research led by Srikant Srinivasan, Associate Professor, School of Computing and Electrical Engineering, IIT Mandi, in collaboration with the Central Potato Research Institute, Shimla, uses Artificial Intelligence (AI) techniques to highlight the diseased portions of the leaf.

Funded by the Department of Biotechnology, Govt. of India, the results of this research have recently been published in the journal Plant Phenomics, in a paper co-authored by Srikant Srinivasan, and Shyam K. Masakapalli along with research scholars, Joe Johnson, and Geetanjali Sharma, from IIT Mandi, and Vijay Kumar Dua, Sanjeev Sharma, and Jagdev Sharma, from Central Potato Research Institute, Shimla.

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 lead to rotting of the plant. If left undetected and unchecked, blight could destroy the entire crop within a week under conducive conditions.

The computational tool developed by the 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.

In order to develop a robust model, healthy and diseased leaf data were collected from fields across Punjab, U.P and Himachal Pradesh. It was important that the model developed should have portability across the nation.

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, the team is 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. 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.


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IIT Mandi Potato Crops Disease detection app

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