Plant Disease Recognition Using Raspberry pi PYTHON AI

Agriculture is the backbone of Pakistan?s economy. It contributes 21 percent to the GDP of the country. Agriculture plays a very vital role for economy of Pakistan and its development. 48% of labour force is engaged directly with agriculture.Plants get affected not only due to de

Project Title

Plant Disease Recognition Using Raspberry pi PYTHON AI

Project Area of Specialization

Artificial Intelligence

Project Summary

Agriculture is the backbone of Pakistan’s economy. It contributes 21 percent to the GDP of the country. Agriculture plays a very vital role for economy of Pakistan and its development. 48% of labour force is engaged directly with agriculture.Plants get affected not only due to deficiency but also caused due to microorganisms like fungi, bacteria, virus and mites. These kind of borne diseases are very dangerous as they affect large farming. And so it is very important to take steps for maintaining the crops.Manual observation over the health of the crops might result in errors and may even be difficult in case of large acres of land. The best approach to overcome requirement of labor as well as the reduction of errors is smart way of monitoring the plant through image processing techniques. Detection of plant diseases can be easily done through leaves as they are the prominent and delicate part of a plant.

In simple terms we are building a system that detect diseases at the early stage.We mainly focus on.

Image processing techniques. 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. Here the main feature is that the crops in the field are continuously monitored and the data is streamed lively. The captured images are analyzed by various steps like acquisition, preprocessing, segmentation, clustering.This in turn reduces the need for labor in large farm lands.Also the cost and efforts are reduced whereas the productivity is increased.

Project Objectives

Plant leaves structure are also different, it’s little bit hard to identifying differences for system. Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products.   The scheme consists of four main steps, first a color transformation structure for the input RGB image is created, then the green pixels are masked and removed using specific threshold value followed by segmentation process, the texture statistics are computed for the useful segments, finally the extracted features are passed through the classifier.

Aim & Objectives:

• To recognize the plant fast rather than looking up in the book etc.
• It will help new farmer to detect the plants easily.
• It can also help people with no knowledge of plants.

Project Implementation Method

The detection of plant is done through image processing techniques. The images of the plant are captured by a digital camera which is interfaced with the raspberry pi board. Various image processing techniques are applied on the acquired image to obtain the features for further analysis. This method of image. Processing involves a series of phases.

STEPS:

• Capture image using camera.
• The data is feed in the tensorflow object detection API.
•  The API iterates over the image until we are satisfied with the result.
• After the training is completed the model is tested using different images.

Benefits of the Project

Accurate estimates of disease incidence, disease severity, and the negative effects of diseases on the quality and quantity of agricultural produce are important for field crop, horticulture, plant breeding, and for improving fungicide efficacy as well as for basic and applied plant research. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves.

Automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for automatic detection and classification of plant leaf diseases.

Technical Details of Final Deliverable

This project reduces human labor in collecting plants and sending them to lab to identify diseases. Our porject provides the faster and better services to farmers and individuals.

Our project uses Python and tensorflow object detection API to detect different plant diseases.User first login to the system after that he can capture an image to identify disease.

SYSTEM LEVEL ARCHITECTURE:

The diagram clearly shows that the raspberry pi is connected to power supply that provides power to the board, camera for capturing images of plants and a monitor to display the captured image. The pi will process the image of the plant and display the disease on the monitor if any.

DATAFLOW DIAGRAM:

The system consists of the following hardware::

• Raspberry pi 4.
• Pi 4 8 megapixel camera.
• 5 inch LCD display.
• A Battery to powerup the pi4.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

IT

Other Industries

Core Technology

Internet of Things (IoT)

Other Technologies

Sustainable Development Goals

Responsible Consumption and Production

Required Resources

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