Diese Präsentation wurde erfolgreich gemeldet.
Die SlideShare-Präsentation wird heruntergeladen. ×

plant disease prediction.pptx

Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Anzeige
Wird geladen in …3
×

Hier ansehen

1 von 17 Anzeige
Anzeige

Weitere Verwandte Inhalte

Aktuellste (20)

Anzeige

plant disease prediction.pptx

  1. 1. Vishwakarma Institute of Technology Software DevelopmentProject Pune Maharashtra Department of Computer Science Engineering Guide by - Prof. Milind Kulkarni
  2. 2. Plant Disease Prediction Using ML Mustafa Bohra (Roll no.-10) Gr no – 12010085 Div – Sy-C Using Python
  3. 3. Contents 3 1 3 4 2 Objective Tools Used Programming Language Block Diagram
  4. 4. Introduction 4 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. 5. Objective of this project Why plant disease prediction required ? 1 5
  6. 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 6
  7. 7. Programming Language 7 2
  8. 8. Programming language used -
  9. 9. Let’s see what goes in the backend Tools used 9 3
  10. 10. What is this coded in ? Environment (IDE): 10
  11. 11. Which modules have been used? ◉ OpenCv ◉ Keras ◉ Numpy ◉ Sci-py 11
  12. 12. How does the project actually work? Flowchart 12 4
  13. 13. 13 Flowchart
  14. 14. Result 14
  15. 15. 15
  16. 16. 16 References ◉ https://www.frontiersin.org/articles/10.3389/fpls.2016.01419/full
  17. 17. Thank You! 17

×