Plant Disease Recognition System

Project Title

Plant Disease Recognition System

Project Area of Specialization Artificial IntelligenceProject Summary

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. Various modern technologies have emerged to minimize postharvest processing, to fortify agricultural sustainability and to maximize the productivity. Various Laboratory based approaches such as polymerase chain reaction, gas chromatography, mass spectrometry, thermography and hyper spectral techniques have been employed for disease identification. However, these techniques are not cost effective and are high time consuming. 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. Random forests are as a whole, learning method for classification, regression and other tasks that operate by constructing a forest of the decision trees during the training time. Unlike decision trees, Random forets overcome the disadvantage of over fitting of their training data set and it handles both numeric and categorical data. The histogram of oriented gradients (HOG) is an element descriptor utilized as a part of PC vision and image processing for the sake of object detection.

Project Objectives

The objective of this work is to develop a system that capable to detect and identify the type of disease based on Blobs Detection and Statistical Analysis. A total 19641 sample leaves images from different colour and type were used and the accuracy is analysed. The Blobs Detection technique are used to detect the healthiness of plant leaves. While Statistical Analysis is used by calculating the Standard Deviation and Mean value to identify the type disease. Result is compared with manual inspection and it is found that the system has 90% in accuracy compared to manual detection process.Plant is exposed to many attacks from various micro-organism, bacterial disease and pests. The symptoms of the attacks are usually distinguished through the leaves, stem or fruit inspection. Disease that are commonly attack plants are Powdery Mildew and Leaf Blight and it may cause severe damaged if not controlled in early stages. Image processing has widely being used for identification, detection, grading and quality inspection in the agriculture field. Detection and identification disease of a plant is very important especially, in producing a high-quality fruit. Leaves of a plant can be used to determine the health status of that plant.

Project Implementation Method

Project implementation is the process of putting a project plan into action to produce the deliverables, otherwise known as the products or services, for clients or stakeholders. It takes place after the planning phase, during which a team determines the key objectives for the project, as well as the timeline and budget.To implement a project effectively, project managers must consistently communicate with a team to set and adjust priorities as needed while maintaining transparency about the project's status with the clients or any key stakeholders.There are several steps involved in implementing a project.Here is a list of steps for implementing a project effectively:

1. Assess the project plan

In the first phase of the project cycle, it's beneficial to establish a plan that meets the expectations of management, clients and key stakeholders. Before implementing a project, assess the plan and make sure that everyone on the team understands the project deliverables. The project manager may want to hold an initial meeting to outline everyone's assigned roles and the expected timeline, as well as any project milestones that a team works toward in the implementation phase. This initial step can help to unite the project team and set a collaborative standard for work.

2. Execute the plan

With a plan in place and expectations set for the team, it's time to start work on the project. During this step, project managers want to have regular discussions with the team about their progress. Measure the project's timeline against the projected schedule and monitor resources to ensure the team has what they need to complete the project successfully.

3. Make changes as needed

During any type of project, it's likely that a project manager needs to make changes during implementation, such as to address additional requests from the client or to keep the project within its scope. Make these adjustments as necessary, relying on the project plan to identify solutions. Continue to communicate with the team, asking questions to determine areas where they need more support. Be prepared to allocate more staff or resources if a project deviates from the plan. Change is a reality for many projects and how effectively a project manager implements those changes can affect the project's outcome.

Benefits of the Project

Computer vision and machine-learning solutions offer great opportunities for the automatic recognition of sick plants by visual inspection of damaged leaves. Crop diseases are an important problem, as they cause serious reduction in quantity as well as quality of agriculture products. An automatic plant-disease detection system provides clear benefit in monitoring of large fields, as this is the only approach that provides a chance to discover diseases at an early stage. The solution includes a set of cameras and computing hardware installed on a vehicle. The computer vision core system inspects image flow from cameras, detects diseased leaves, and performs classification. The inspection results can be provided in various ways.Our solution of automated early disease detection is based on an artificial neural network, which is now the most robust technique for image classification. The main advantages of our solution include high processing speed and high classification accuracy. A plant disease recognition system can work as a universal detector, recognizing general abnormalities on the leaves, such as scorching or mold. After extensive training on diverse datasets our machine learning model will be capable of distinguishing a large number of different diseases.

Technical Details of Final Deliverable

Our final deliverable will consist of a website, which we will create by using web languages such as html,css,javascript and bootstrap. On that website we will export or deploy our artifical intelligence deep learning project (Plant Disease Recognition System ). The website will be a useful environment where we will able to predict the images of plant leaves whether it is diseased or not. Our final deliverable will be an end to end project that would be able to use by everyone to get predictions about their plants. It will have an accuracy of greater than 90+ which will evaluate and predict images accurately and efficiently.

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Agriculture , Food Core Technology Artificial Intelligence(AI)Other Technologies OthersSustainable Development Goals Responsible Consumption and ProductionRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
GPU	Equipment	1	70000	70000
Detecting device	Miscellaneous	1	10000	10000