Advanced search
Publication Cover
IETE Journal of Research Volume 70, 2024 - Issue 3
Submit an article Journal homepage
296
Views
14
CrossRef citations to date
0
Altmetric
Computers and Computing

A Unified Lightweight CNN-based Model for Disease Detection and Identification in Corn, Rice, and Wheat

Sahil VermaDepartment of Computer Science and Engineering, National Institute of Technology, Bihar, IndiaORCID Iconhttps://orcid.org/0000-0003-3919-874XView further author information
ORCID Icon,
Prabhat KumarDepartment of Computer Science and Engineering, National Institute of Technology, Bihar, IndiaORCID Iconhttps://orcid.org/0000-0001-9945-7702View further author information
ORCID Icon &
Jyoti Prakash SinghDepartment of Computer Science and Engineering, National Institute of Technology, Bihar, IndiaORCID Iconhttps://orcid.org/0000-0002-3742-7484View further author information
ORCID Icon
Pages 2481-2492 | Published online: 27 Feb 2023

Citations (14)

Keep up to date with the latest research on this topic with citation updates for this article.

Subscribe to citation updates

Read on this site (1)

Sahil Verma, Prabhat Kumar & Jyoti Prakash Singh. MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks. Network: Computation in Neural Systems 0:0, pages 1-24.
Read now

Articles from other publishers (13)

Caihua Yao, Ziqi Yang, Peifeng Li, Yuxia Liang, Yamin Fan, Jinwen Luo, Chengmei Jiang & Jiong Mu. (2024) Two-Stage Detection Algorithm for Plum Leaf Disease and Severity Assessment Based on Deep Learning. Agronomy 14:7, pages 1589.
Crossref
Amit Bhola & Prabhat Kumar. (2024) Farm-Level Smart Crop Recommendation Framework Using Machine Learning. Annals of Data Science.
Crossref
Oumayma Jouini, Mohamed Ould-Elhassen Aoueileyine, Kaouthar Sethom & Anis Yazidi. (2024) Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement. AgriEngineering 6:3, pages 2001-2022.
Crossref
Burak Gülmez. (2024) Advancements in rice disease detection through convolutional neural networks: A comprehensive review. Heliyon 10:12, pages e33328.
Crossref
Amit Bhola & Prabhat Kumar. (2024) Deep feature-support vector machine based hybrid model for multi-crop leaf disease identification in Corn, Rice, and Wheat. Multimedia Tools and Applications.
Crossref
Sanam Kazi & Bhakti Palkar. (2024) Design of Enhanced CNN Model for Rice Disease Classification with Comparative Analysis on Different Variants of Dataset. Design of Enhanced CNN Model for Rice Disease Classification with Comparative Analysis on Different Variants of Dataset.
Wassem I. A. E. Altabaji, Muhammad Umair, Wooi-Haw Tan, Yee-Loo Foo & Chee-Pun Ooi. (2024) Comparative Analysis of Transfer Learning, LeafNet, and Modified LeafNet Models for Accurate Rice Leaf Diseases Classification. IEEE Access 12, pages 36622-36635.
Crossref
Soham Lalit Patil. 2024. Smart Trends in Computing and Communications. Smart Trends in Computing and Communications 277 291 .
Muhammad Bello Kusharki & Bilkisu Larai Muhammad-Bello. 2024. Artificial Intelligence of Things for Achieving Sustainable Development Goals. Artificial Intelligence of Things for Achieving Sustainable Development Goals 143 160 .
Bilkisu Larai Muhammad & Muhammad Bello Kusharki. (2023) Federated Learning for Collaborative Crop Disease Monitoring in Wheat Production. Federated Learning for Collaborative Crop Disease Monitoring in Wheat Production.
Riki Ruli A. Siregar, Kudang Boro Seminar, Sri Wahjuni & Edi Santosa. (2023) Convolutional Neural Network Model Architecture for Rice Leaf Digital Image Identification. Convolutional Neural Network Model Architecture for Rice Leaf Digital Image Identification.
Riki Ruli A. Siregar, Kudang Boro Seminar, Sri Wahjuni, Edi Santosa & Hengki Sikumbang. (2023) Backpropagation Algorithm to Detect The Color of The Leaf to Determine The Need for Nutrients. Backpropagation Algorithm to Detect The Color of The Leaf to Determine The Need for Nutrients.
Rupesh Gupta & Kanwarpartap Singh Gill. (2023) Rice Image Classification and Detection using improvised VGG16 Model through Deep Learning Techniques. Rice Image Classification and Detection using improvised VGG16 Model through Deep Learning Techniques.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.