Researchers have made significant progress in the field of artificial intelligence by applying deep learning techniques to automate the detection and classification of crop leaf diseases.

Early detection of tomato leaf diseases is critical to prevent their spread, but manual detection methods for the same are time-consuming, inconsistent, and labor-intensive. To address this ...

A research team led by Prof. Jiang Ni from the Institute of Genetics and Developmental Biology (IGDB) of the Chinese Academy of Sciences (CAS) proposed a cost-effective method for in-field acquisition ...

A new open-sourced framework developed at the University of Michigan proactively detects silent errors as they happen during deep learning training. These difficult-to-detect issues do not cause ...

Researchers have made significant progress in the field of artificial intelligence (AI) by applying deep learning techniques to automate the detection and classification of crop leaf diseases.

They focused on five common diseases that affect tomato leaves and developed a machine learning model, called PLPNet, that can accurately detect these diseases from images taken in real-time.