IIT Mandi Finds Way To Detect Disease In Potato Crops Via Photographs
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.
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 Dr 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, Government of India, the results of this research have recently been published in the journal Plant Phenomics, in a paper co-authored by Dr Srikant Srinivasan, and Dr Shyam K Masakapalli along with research scholars, Joe Johnson, and Geetanjali Sharma, from IIT Mandi, and Dr Vijay Kumar Dua, Dr Sanjeev Sharma, and Dr 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,” explained Dr Srinivasan. 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.
“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,” said Joe Johnson, Research Scholar, IIT Mandi, while highlighting the practical usage of his research.
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.