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Plant Deies

This is the simple presentation of plant disease

Snehal23
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Plant Deies

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  1. Plant leaf disease detection using CNN Final Year Project BTI Click to Edit Title Click to Edit Sub Title Snehal Karki 1BH19CS103 Sanjay Malla 1BH19CS094 Prasant Poudel 1BH19CS069 Jitendra Kohar 1BH19CS034

  2. Contents ? i) Objectives ? ii) Abstract ? iii) Existing System ? iv) Literature Survey ? v) Proposed System vi) Architecture Diagram ?vii) Conclusion and Futurework

  3. Objective of Project To detect unhealthy regions of plant leaves. Classification of plant leaf diseases using texture features. To analyze the leaf infection. To give remedy information to the user. To make this services availabe on Mobile App which can run on low level configuration devices.

  4. Abstract Plant Disease Prediction is an application which will detect and provide some remedial measures for diseases in the crop to the users. Initially the client can either click or upload the image of the diseased crop in the application. Once the plant disease is matched with the existing data, then the effective remedial measures such as what action should they take about the disease is provided. The image is processed for the effective remedial measures using the machine learning. In its current form, our application would be as a preliminary tool that could assess the users by providing some remedial measures like what type of fertilizers to use and the measures to be taken by comparing it with the datasets provided in the database. This comes with the simple Mobile application for handy and easy use of this service.

  5. Literature Survey

  6. Proposed System This project focuses on deploying the model on a Mobile App with Increased accuracy. Training Phase Machine Learning model is trained and tested in a Conda environment and converted into TFLITE model which will be used to do classification in Mobile Application. Deployment Phase Converted TFLITE model is embedded into the Android Application and the input are fed to get the predicted disease. By using the predicted disease, the remedial measures and the details of the disease are fetched from the Local JSON file and displayed to the user

  7. Architecture Diagram System

  8. System Low Level Design

  9. Conclusion and Futurework The use of automated monitoring and management systems are gaining increasing demand with technological advancement. In the agricultural field loss of yield mainly occurs due to widespread disease. ?Mostly the detection and identification of the disease is noticed when the disease advances to 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 the help for a person having less knowledge about the disease,Depending on these goals, we have to extract the features corresponding to the disease.

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