Plant Disease Detection System using Convolutional Neural Networks (CNN)
Project Overview
Project planning is an essential step in the successful execution of any project, and the development of a plant disease detection system using Convolutional Neural Networks (CNN) is no exception. The primary objective of this project is to create a robust machine learning model capable of accurately identifying various types of plant diseases from images of plants.
Project Planning
1.Defining Scope, Objectives, and Deliverables
The first step in the project planning process involves defining the scope, objectives, and deliverables. This includes identifying the types of plant diseases the system will detect, specifying accuracy levels, and outlining expected deliverables. For this project, the primary deliverables are a trained CNN model and a user-friendly application allowing users to upload plant images and receive disease information.
- Project Timeline and Resource Allocation
After defining the project scope, objectives, and deliverables, the next crucial step is to develop a project timeline and allocate resources. This entails breaking down the project into smaller tasks, estimating time requirements for each task, and identifying and allocating resources such as computing power, data storage, and personnel. Ensuring realistic and feasible timelines and resource allocation is essential to prevent delays and budget overruns.
Project Execution
With a well-defined plan in place, the project execution involves implementing the CNN model, training it on a diverse dataset of plant images, and developing the user-friendly application. Continuous testing and refinement are conducted to enhance model accuracy.
Future Enhancement
Building on the success of the current project, a future enhancement involves detecting the percentage of potential diseases in plants. This addition aims to provide more comprehensive insights into plant health.
Published Paper
A paper related to this project has been published in the International Conference on ICSCPC held at My undergrad college The paper discusses the methodology, results, and implications of the plant disease detection system.
Paper Repository: The paper is uploaded in the "papers" directory within this repository.
Conclusion
In conclusion, a well-planned project is critical to the success of developing a plant disease detection system using CNN. Defining project scope, objectives, and deliverables, developing a project timeline, and allocating resources are essential steps in the project planning process. Proper planning ensures efficient and effective project execution, with desired deliverables achieved within set timelines and budget constraints.