Leave Identification And Disease Detection Through Rasberry Pi

Several methods to identify plants have been proposed by several researchers and Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product Commonly, the methods did not capture color  information,  because  color&nbs

Project Title

Leave Identification And Disease Detection Through Rasberry Pi

Project Area of Specialization

Artificial Intelligence

Project Summary

Several methods to identify plants have been proposed by several researchers and Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product Commonly, the methods did not capture color  information,  because  color  was  not  recognized  as  an important aspect to the identification. Our Proposed Solution is an classifier system for recognizing  plant spices on the basis of a color , shape and texture.  disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. This includes a series of steps from capturing the image of leaves to identifying the disease through the implementation in Raspberry PI. Raspberry PI is used to interface the camera and the display device along which the data is stored in the cloud. It requires tremendous amount of work, expertize in the plant diseases, and also require the excessive processing time. Hence, image processing is used for the detection of plant diseases. Disease detection involves the steps like image segmentation, feature extraction and classification. The proposed approach also compares favourably with the best results reported in the dataset.

In this study an image-processing-based approach is proposed and used for leaf disease detection. We test our program on five diseases which effect on the plants; they are: Early scorch, Cottony mold, ashen mold, late scorch, tiny whiteness.

The proposed approach is image-processing-based and is highly based on K-Means clustering technique and Artificial Neural Network (ANN). The approach is composed of four main phases; after the preprocessing phase, the images at hand are segmented using the K-means technique, then some texture features are extracted in which they are passed through a pre-trained set of leaves. As a testbed we use a set of leaf images taken from the chakwal area in Pakistan.

classifier that is based on statistical classification perform well and can successfully detect and classify the tested diseases with a precision of around 93%.

For future research, they have been some directions, such as, developing better segmentation technique; selecting better feature extraction and culling classification algorithms.

Project Objectives

In this project different classifier system and  applications have been investigated and different procedures for a plant identification and detection have been enlisted and a design was also proposed. The main contributions of this project can be structured into the following objectives:

• To enhance the immunity of plant by reducing the cost.
• To be achieved in our system out not only the base on a colour, it will be also on colour, style, and shape leaf identification and detect the diseases.
•  Recognizing infected leaf and stem
•  Measure the affected area
• Finding the shape of the infected region
•  Determine the color of infected region
•  And also influence the size and shape of the leaf
• To help farmer by identifying the plant name and disease present in the plant leafs and and to provide possible solution for that disease.
• To reduce the effort of farmer in term of time and decision making.

Project Implementation Method

ImplementationRequirements

Following requirements are necessary for developing this application:

• Raspberry Pi Board
• Camera Module
• Voltage Battery
• Iron Stand Connections 
• Light System or  Flashing Camera

  Main model of our System Application includes:

• Application will take image from camera & will convert the image using RGB factor.
• Application will evaluate the picture on the basis of colour will generate values.
• Application will evaluate the picture on the basis of shape will generate values.
• Application will evaluate the picture on the texture of shape will generate values.
• Application will identify the leaves name on the all above mentioned components.
•  Application Will Detect infected Leaves and provides the cure.
•      

Image-based methods are considered a promising approach for species identification  A user can take a picture of a plant in the field with the camera  analyze it with an installed recognition application to identify the species or at least to receive a list of possible species if a single match is impossible.

Benefits of the Project

Our project aim is to design a “Plant Identification and Diseases Detector through Raspberry Pi” using Artificial Intelligence. This technology will give us a more accurate and a precise results. Our aim is to be automated classification systems can prove extremely useful for quick and efficient classification of plant species and diseases. The accuracy of the our proposed approach will be  comparable to those reported in contemporary works is much better.n agricultural field loss of yield mainly occurs due to widespread disease. identification of the disease are noticed when the disease advances to the severe stage. Therefore, causing the loss in terms of yield, time and money. The proposed system is capable of detecting the disease at the earlier stage as soon as it occurs on the leaf. Hence saving the loss and reducing the dependency on the expert to a certain extent is possible. It can provide help for a person having less knowledge about the disease. Depending on these goals, we have to extract the features corresponding to the disease.

Technical Details of Final Deliverable

The leaf recognition framework was divided into leaf modeling and leaf recognition. For leaf modeling, leaves belonging to the same species were used to detect and extract leaf features. The extracted features were then used for leaf modeling, creating a leaf model for each leaf species in the database. During leaf recognition, a query leaf was also tested by detecting feature points and feature extraction. Using these features, the recognition system can identify the best matching model and recognize the species of the query leaf.Plant Identification and Diseases Detector through Raspberry Pi” the challenging task in the plant identification from leaf image is to find discriminant features that can be appropriate for distinguishing different plant species and diseases Dictator. In order to identify the plant species, several different features have been evaluated such as colour.shape,size usinh k mean clustering.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Agriculture

Other Industries

IT

Core Technology

Artificial Intelligence(AI)

Other Technologies

Artificial Intelligence(AI), Others

Sustainable Development Goals

Decent Work and Economic Growth

Required Resources

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