Paddy Disease Effected Images

Citation Author(s):
Sampath Kumar
S
Submitted by:
Dr. S. Sampath Kumar
Last updated:
Fri, 06/21/2024 - 13:52
DOI:
10.21227/xzhf-tk14
Data Format:
.jpeg(ZIP)
License:
Creative Commons Attribution
509 Views
Categories:
Agriculture
Image Processing
Keywords:
RGB-colored; deep learning; Agriculture; Visual dataset; Automated harvesting; Paddy Disease classification;
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Abstract 

The "Paddy Disease Dataset" represents a comprehensive collection of data related to various diseases commonly found in paddy crops. Paddy, or rice, is a staple crop crucial for global food security. However, paddy crops are susceptible to a range of diseases that can significantly impact yield and quality. This dataset encompasses a diverse array of disease-related information, including disease types, symptoms, geographical distribution, severity levels, and potential management strategies. By analyzing this dataset, researchers, agricultural experts, and policymakers can gain valuable insights into the prevalence, patterns, and potential impact of diseases on paddy crops. This understanding can contribute to the development of proactive disease management approaches, aimed at safeguarding paddy yields and ensuring sustained agricultural productivity.

Instructions: 

The "Paddy Disease Dataset" is a thorough database of vital details about common diseases in paddy crops. Being a primary food source around the world, paddy is prone to a number of illnesses that can negatively impact its yield and quality. A comprehensive collection of disease-related data, including disease classifications, in-depth symptomatology, geographic distribution, severity gradations, and suggested mitigation tactics, are included in this dataset. The dataset helps researchers, agronomists, and policymakers conduct thorough assessments by properly documenting these elements. Such analyses help in the formulation of efficient disease management procedures to protect paddy yields and maintain sustained agricultural production in addition to providing insights into disease prevalence and patterns.

Comments

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Submitted by Youssef Hedefa on Mon, 11/13/2023 - 16:42

good work

Submitted by SENTHIL RAMASWAMY on Mon, 04/29/2024 - 02:02

m

Submitted by Abhilash Kasidi on Tue, 08/27/2024 - 09:37

thanks

Submitted by Wilson Adrian D... on Thu, 09/19/2024 - 01:55

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