Crop Disease Analyzer

In the process of plant?s growth and development, there are some diseases mainly caused by bacteria, fungi and viruses etc. And in the past time, the accordingly scientific knowledge is not widespread, so the farmers can?t understand these reasons. There has been ?trying everything?&n

Project Title

Crop Disease Analyzer

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

In the process of plant’s growth and development, there are some diseases mainly caused by bacteria, fungi and viruses etc. And in the past time, the accordingly scientific knowledge is not widespread, so the farmers can’t understand these reasons. There has been “trying everything” phenomenon, the farmers spraying lots of pesticides. As a result of this untargeted way, it not only caused great waste, but also reduced the quality of agricultural products, and produced excessive pesticide residues. The Crop Disease Analyzer can accurately diagnose the vegetable dyes pathogens in the early stages of infection, and also can help farmers to determine the varieties of pesticide use quickly and accurately, then to reduce the losses and increase the efficiency.

Project Objectives

The objective of this project is to recognize abnormalities
that occur on plants in their greenhouses or natural
environment. The image captured is usually taken with a plain background to eliminate occlusion. The algorithm was
contrasted with other machine learning models for accuracy.Using Random forest classifier, the model was trained using images of papaya leaves. The model could classify with approximate 70 percent accuracy. The accuracy can be increased when trained with vast number of images and by using other local features together with the global features such as SIFT (Scale Invariant Feature Transform), SURF (Speed Up Robust Features) and DENSE along with BOVW Bag Of Visual Word).

1.Sampling parts: various types of plant stalk, stem, leaf and fruit can be sampled
2.Scope: a variety of crops, plants, fruits, vegetables and tea etc.
3.Quickly diagnose a variety of viruses and bacteria:
1) Fungus: botrytis cinerea , downy mildew, damping off, yellows, seedling blight,morning and late Pestilence, stem blight, gummy stem blight, scab, black spot, rust disease, ring rot, powdery mildew, alternaria leaf spot, shot hole, full rot.
2) Bacterial diseases: ulcers disease, bacterial spot, soft rot, bacterial wilt
3) Viral diseases: stubby disease, plexus dwarf, mosaic virus disease
4.Chinese large-screen liquid crystal display and prompts, easy to use
5.Automatic control, automatic calculation, automatic calibration, automatic printing, and has high precision
6.Automatic printer integrated design
7.It can be connected to the computer and the printer, store test data for the user profile to provide guidance of dispense
8.Continuous testing of multiple samples, low-cost test

Project Implementation Method

To find out whether the leaf is diseased or healthy, certain steps must be followed. i.e., Preprocessing, Feature extraction,Training of classifier and Classification. Preprocessing of image, is bringing all the images size to a reduced uniform size. Then comes extracting features of a preprocessed image which is done with the help of HOG . HoG is a feature descriptor used for object detection. In this feature descriptorthe appearance of the object and the outline of the image isdescribed by its intensity gradients. One of the advantage ofHoG feature extraction is that it operates on the cells created.Any transformations doesn’t affect this.Here we made use of three feature descriptors.

Hu moments: Image moments which have the important characteristics of the image pixels helps in describing the objects. Here Hu moments help in describing the outline of particular leaf. Hu moments are calculated over single channel only. The first step involves converting RGB to Gray scale and then the Hu moments are calculated. This step gives an array of shape descriptors.
Haralick Texture: Usually the healthy leaves and diseased leaves have different textures. Here we use Haralick texture feature to distinguish between the textures of healthy and diseased leaf. It is based on the adjacency matrix which stores the position of (I,J). Texture is calculated based on the frequency of the pixel I occupying the position next to pixel J. To calculate Haralick texture it is required that the image be converted to gray scale.

Color Histogram: Color histogram gives the representation of the colors in the image. RGB is first converted to HSV color space and the histogram is calculated for the same. It is needed to convert the RGB image to HSV since HSV model aligns closely with how human eye discerns the colors in    an image.Histogram plot provides the description about the number of pixels available in the given color ranges.

Benefits of the Project

The agriculturist in provincial regions may think that it’s
hard to differentiate the malady which may be available in
their harvests. It's not moderate for them to go to agribusiness office and discover what the infection may be. Our principle objective is to distinguish the illness introduce in a plant by watching its morphology by picture handling and machine learning.Pests and Diseases results in the destruction of crops or part of the plant resulting in decreased food production leading to food insecurity. Also, knowledge about the pest management or control and diseases are less in various less developed countries. Toxic pathogens, poor disease control, drastic climate changes are one of the key factors which arises in dwindled food production.

In recent times, server based and mobile based approach for disease identification has been employed for disease identification. Several factors of these technologies being high resolution camera, high performance processing and extensive built in accessories are the added advantages resulting in automatic disease recognition.Modern approaches such as machine learning and deep learning algorithm has been employed to increase the recognition rate and the accuracy of the results. Various researches have taken place under the field of machine learning for plant disease detection and diagnosis, such traditional machine learning approach being random forest, artificial neural network, support vector machine(SVM), fuzzy logic, K-means method, Convolutional neural networks etc.…

Technical Details of Final Deliverable

First for any image we need to convert RGB image into gray scale image. This is done just because Hu moments shape descriptor and Haralick features can be calculated over single channel only. Therefore, it is necessary to convert RGB to gray scale before computing Hu moments and Haralick features.To calculate histogram the image first must be converted to HSV (hue, saturation and value), so we are converting RGB image to an HSV image.Finally, the main aim of our project is to detect whether it is diseased or healthy leaf with the help of a Random forest classifier which is as depicted.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Agriculture

Other Industries

Food

Core Technology

Others

Other Technologies

Big Data

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com