In modern Agriculture and Forestry purposes Plant Pathological Research is very much essential for the crop improvement. In every year, huge crop is damaged by pathogen attack. In most of the cases, Farmers and lay men suffers huge loss due to severe pathogenic infestation. For crop improvement, the first and foremost essential key point is diseases identification. After proper identification of diseases we will be able to treat the diseases. Most of the cases lab oriented research is time taking and very much expendable but instant identification of plant diseases with its causal organism by using modern technology is most suitable and cost effective and also authentic. This type identifying methodology will help us to identify the diseases with its proper control measures. Above 95 % accuracy and authentic identification is possible by using this methodology.
What it does
In my present research, I have taken two of the most economically important and valuable species such as Tea(Camellia Sinensis) and various species of Citrus as experimental basis. I have done the Canker and Blister Blight Diseases of the above mentioned plant species. And it shows almost 99% accurate identification of the disease in all the infected plants. It is very easy and simple technique which any farmer or lay men can use this methodology to Identify the symptoms and signs of the diseases. By using this methodology we can easily identify the various types of diseases which mostly occurs in Leaf, Stem, Root , Fruits and Tubers.
How I built it
We are approaching the following methodology :-
Write a web crawler that will gather plant data from google images. Label our dataset and create .xml file corresponding to each image. Create train and test data directories. Create a script for generating the train.csv and test.csv for the data. Generate the train.record and test.record files. We will then train our classifier algorithm with the data using Tensorflow 2.0 and create a model out of it.
Then we will create a setup using the Inference API so that it is easily gets optimized results on the CPU using the camera and finally we identify the plant disease given input as an image, a video or even a live camera feed. Once the identification is done, then the model will provide other details like why the disease has happened, the possible cures, preventive measures etc.
Technologies Used Hardwares Used :-
Intel Powered PC (Intel 7th Gen i5 NUC - NUC7i5BNH Barebone). Intel RealSense Camera. Technologies Used :-
Intel Optimised Python. Powered by TensorFlow 2.0 Intel's OpenVino ToolKit for Computer Vision.
Challenges I ran into
It was difficult doing it in parts but piloting the project realtime helped us resolve lot of queries
Accomplishments that I'm proud of
Our Project was featured in Leading dailies that was big accomplishment for us on specially Earth Day Economic Times India
*livemint a business daily in India * 2.link
NEWS ROOM Intel
Devmesh project Intel
** Similar project on Plant Anatomy created by us featured by Intel**
## What I learned Tensorflow2.0 Basic needs for farmers Great tool for Agriculturists
Demo Implementation Video
What's next for Pathological Plant Disease detection
Scale it up for Tensorflow lite and IOS too