plant disease prediction using machine learning

1. Vishwakarma Institute of
Technology
Software DevelopmentProject
Pune Maharashtra
Department of Computer Science Engineering
Guide by - Prof. Milind Kulkarni

2. Plant Disease
Prediction Using
ML
Mustafa Bohra (Roll no.-10)
Gr no – 12010085
Div – Sy-C
Using
Python

3. Contents
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Objective Tools Used
Programming
Language
Block Diagram

4. Introduction
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Modern technologies have given human society the ability to produce enough
food to meet the demand of more than 7 billion people. However, food
security remains threatened by a number of factors including climate change
(Tai et al., 2014), the decline in pollinators (Report of the Plenary of the Intergovernmental Science-
PolicyPlatform on Biodiversity Ecosystem and Services on the work of its fourth session, 2016), plant diseases
(Strange and Scott, 2005), and others. Plant diseases are not only a threat to food
security at the global scale, but can also have disastrous consequences for
smallholder farmers whose livelihoods depend on healthy crops. In the
developing world, more than 80 percent of the agricultural production is
generated by smallholder farmers (UNEP, 2013), and reports of yield loss of more
than 50% due to pests and diseases are common

5. Objective of this
project
Why plant disease prediction required ?
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6. Objective for the project
◉ The objective of this project is to classify the plant diseases by
assessing the images of the leaves with the application of Extreme
Learning Machine (ELM), a Machine Learning classification algorithm
with a single layer feed-forward neural network.
◉ And by this prediction help farmer to use as per pesticides and for
good yield
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7. Programming Language
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8. Programming language used -

9. Let’s see what goes in the backend
Tools used
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10. What is this coded in ?
Environment (IDE):
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11. Which modules have been
used?
◉ OpenCv
◉ Keras
◉ Numpy
◉ Sci-py
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12. How does the project actually work?
Flowchart
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13. 13
Flowchart

14. Result
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References
◉ https://www.frontiersin.org/articles/10.3389/fpls.2016.01419/full

17. Thank You!
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