Click here to Skip to main content
15,921,452 members
Please Sign up or sign in to vote.
1.00/5 (3 votes)
See more:
sir, I need a matlab coding for "How to identify plant nutrient deficiency by image processing in matalb"

Then type your question into Google; this site does not provide code to order.
Share this answer

You did not give much information about your task.

Here is a general checklist on how to solve what you describe.

  1. Get as many pictures as you can of healthy plants and plants with nutrition deficits. If you make these images yourself setup an environment that improves the quality of your images (e.g. use a room w/o daylight and stable light sources, even background.. things like that). This will help you later in the process of developing your recognition algorithm.
  2. Classify your images and find specific characteristics that you can use to build feature groups, do some statistical research maybe on the colors. What range of colors are on ill leafs and are they always from the same interval of colors. What greens tones can you find on healthy plants?
  3. Develop filters to get the different image segments. I guess you will roughly get three segments types in your case (background, green part of leafs, brown parts of leafs). Color filtering would be the first thing I would try here, but this step depends heavily on how your input data exactly looks like and what features you chose.
  4. Often the main parts just begins when you have got the image segments. Basically, now, you do calculation, run some more algorithms and finally you present a report with results back to the user of your program.

I hope this helps, but please be aware that these are only very general steps. You need to provide at least some images that you use as input for a precise answer to this kind of question.

Share this answer

This content, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

CodeProject, 20 Bay Street, 11th Floor Toronto, Ontario, Canada M5J 2N8 +1 (416) 849-8900