Scientists from the Indian Institute of Technology (IIT) Mandi have developed a computational model for automated disease detection in potato crops using photographs of the 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.
Potatoes, in the history of the world, have been the cause of the world’s 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, 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.
“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 Srinivasan. This process, as expected, is tedious and often impractical, especially for remote areas, because it requires the presence of a horticulture specialist.
“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 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.
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, UP and Himachal Pradesh. It was important that the model developed had portability across the nation. “The model is being refined as more states are covered,” added Srinivasan and highlighted that it would be deployed as part of the FarmerZone app that will be available to potato farmers for free.
This article has been published from the source link without modifications to the text. Only the headline has been changed.
