Year: 2025
Type: article
Abstract: It's critical to identify and categorize agricultural diseases. To preserve the quality and yield of crops. Crop disease identification using traditional methods takes a lot of time and labor. As a result, methods based on computers have been created to automatically diagnose the illness. In addition, behind rice and maize, wheat is the third most harvested and consumed grain. Crop disease detection is one of the most popular study subjects these days. Recently, several wheat illnesses have been recognized and categorized using deep learning algorithms. This article presents a Residual Network (ResNet152), regarded as one type of Convolutional Neural Network (CNN), and deep learning-based approach for identifying and categorizing wheat illnesses. Compared to other approaches now in use, the suggested method provides a greater level of accuracy in the identification and classification of various wheat illnesses. Furthermore, the results show that the suggested strategy offers early diagnosis and treatment of wheat illnesses, improving crop quality and output.
Author: Shri Rama Chandra Murthy
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4409924811