Record Details

Deep learning-based approach for identification of diseases of maize crop

KRISHI: Publication and Data Inventory Repository

View Archive Info
 
 
Field Value
 
Title Deep learning-based approach for identification of diseases of maize crop
Not Available
 
Creator Md. Ashraful Haque
Sudeep Marwaha
Chandan Kumar Deb
Sapna Nigam
Alka Arora
Karambir Singh Hooda
P. Lakshmi Soujanya
Sumit Kumar Aggarwal
Brejesh Lall
Mukesh Kumar
Shahnawazul Islam
Mohit Panwar
Prabhat Kumar
R. C. Agrawal
 
Subject Deep Learning
Convolutional Neural Network
Disease Diagnosis
Maize crop
Image Recognition
 
Description Not Available
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in‑field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR‑IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non‑destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception‑v3’ network were trained with the collected diseased images of maize using baseline training approach. The best‑performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best‑performing model with some pre‑trained state‑of‑the‑art models and presented the comparative results in this manuscript. The results reported that best‑performing model performed quite better than the pre‑trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best‑performed model is efficient in recognizing diseases of maize from in‑field images even with varied backgrounds.
Not Available
 
Date 2022-04-19T07:38:39Z
2022-04-19T07:38:39Z
2022-04-15
 
Type Research Paper
 
Identifier Haque, M.A., Marwaha, S., Deb, C.K. et al. Deep learning-based approach for identification of diseases of maize crop. Sci Rep 12, 6334 (2022). https://doi.org/10.1038/s41598-022-10140-z
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/71626
 
Language English
 
Relation Not Available;
 
Publisher Not Available