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An Overview of Deep Learning Models for Foliar Disease Detection in Maize Crop

Jagrati Paliwal1, Sunil Joshi2

Corresponding Author:

Jagrati Paliwal

Affiliation(s):

1. Department of ECE, CTAE, MPUAT, Udaipur, Rajasthan 

2. Professor, Department of ECE, CTAE, MPUAT, Udaipur, Rajasthan


Abstract:

Agriculture is an important sector of Indian economy and India is among the top three global producers of agricultural products. Protecting the crops and producing healthy yields is a prime goal of the agriculture industries. The agricultural crops are susceptible to diseases and demands proactive early diagnosis and treatment. Studies and Research are in progress to find smart methods and techniques for accurate diagnosis of crop diseases to prevent major yield losses and financial losses. The present study outlines the role of Deep Learning in the crop disease detection and discusses the future advancements in maize disease detection. The paper focuses on the role of Deep Learning in identification of diseases on maize plant leaf and describes about some common maize diseases and its classification methods. The paper shall help readers to gain insight on Deep Learning techniques to solve classification problems and encourage them to proceed for future work in the concerned domain.

Keywords:

Agriculture, Deep Learning, Maize, CNN

Downloads: 152 Views: 797
Cite This Paper:

Jagrati Paliwal, Sunil Joshi (2022). An Overview of Deep Learning Models for Foliar Disease Detection in Maize Crop. Journal of Artificial Intelligence and Systems, 4, 1–21. https://doi.org/10.33969/AIS.2022040101.

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