An Effecient Model for Plant Disease Identification

Plants are the world's food source. The country's rural development is depending on the productivity of crops. Specialists frequently inspect the plants to locate and identify illnesses. This approach, however, is typically time-consuming, expensive, and prone to mistakes. Automatic identi...

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Bibliographic Details
Main Authors: P, Nithya Sri, D, Sajitha, B, Chancalviben N, Lincy R, Babitha
Format: Conference Proceeding
Language:English
Subjects:
CNN
Crop Monitorin
Crops
Deep
Deep learning
Graphics processing units
Image Handling
Internet
Learning
Plant diseases
Productivity
Technological innovation
Online Access:Request full text
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Summary:Plants are the world's food source. The country's rural development is depending on the productivity of crops. Specialists frequently inspect the plants to locate and identify illnesses. This approach, however, is typically time-consuming, expensive, and prone to mistakes. Automatic identification is essential. As an alternative to the usual time-consuming cycle, various research projects aim to discover viable methods for securing plants. Recently, advancements in invention have resulted in a few alternatives to traditional tedious methods. In order to allow the model to discriminate between sick and healthy leaves or between different climates, the strong leaves and foundational images are processed with various classifications. In classification challenges, advanced deep learning approaches are particularly helpful. This study uses deep convolutional networks to tackle an alternative method of dealing with the automatic identification of plant infectious recognition models.
ISSN:2575-7288
DOI:10.1109/ICACCS57279.2023.10112990