Shimla: Scientists from the Indian Technology Institute (IIT) bath, have developed computing models for detection of automatic diseases in potato plants using photos of leaves.
This study was led by Srikant Srinivasan, Associate Professor, School of Computing and Electrical Engineering, IIT Bathing, in collaboration with the Institute of Research Center of the Center, Shimla, using the technique made by Intelligence (AI) to highlight the parts of the sick leaves.
The computing tool developed by IIT bath Scientists can detect Blights in potato leaf images.
This model is built using AI tool called the mask-based neural neural network architecture and can accurately highlight the parts that are sick from leaves in the middle of the background of the plant and complex soil.
To develop a strong model, healthy and painful leaf data is collected from fields throughout Punjab, up and Himachal Pradesh.
It is important that the model developed must have portability throughout the country.
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Funded by the Department of Biotechnology, the Government of India, the results of this study have recently been published in the journal PLANT Phenomics, in a paper written by Srikant Srinivasan, and Shyam K is cooked along with undergraduate research, Joe Johnson, and Geetanjali Sharma, from IIT Bathing, and Vijay Kumar Two, Sanjeev Sharma, and Jagdev Sharma, from the Central Potato Research Institute, Shimla.
“In India, as well as most developing countries, detection and Blight identification are carried out manually by trained personnel who lurk the fields and examine potato leaves visually,” explained Srinivasan in a statement issued on Monday.
This process, as expected, is boring and often impractical, especially for remote areas, because it requires horticultural specialist expertise that may not be physically accessible.
“Detection of automatic diseases can help in this regard and remember the extensive proliferation of cellphones throughout the country, smartphones can be a useful tool in this regard,” said Joe Johnson, research research, IIT bath, while highlighting the practical use of his research.
Advanced HD cameras, better computing power and road communication offered by smartphones offer a promising platform for automatic disease detection in plants, which can save time and help in timely disease management, in the case of plague.
Srinivasan said that detection performance analysis showed a total precision of 98% in leaf images in the field environment.
Following this success, the team is measuring the model up to several tens of megabytes so that it can be hosted on a smartphone as an application.
With this, when farmers will photograph the unhealthy leaves, the application will confirm in real-time if the leaves are infected or not.
With this timely knowledge, farmers will know exactly when to spray the field, save its products and minimize costs associated with unnecessary use of fungicides.
“This model is perfected because more countries are closed,” Srinivasan said, adding that it would be deployed as part of the Farmerzone application that would be available for free potato farmers.
