Phytovision Diagnosis: Identification of Plant Diseases using Deep Learning Techniques

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MADHURAPANTHULA ABHIRAM; MARELLA VENKATA RAGHU RAM; GUDURU CHAKRAVARTHI; ABDUL JALEEL; Dr.CH.RATHNA JYOTHI

Phytovision Diagnosis: Identification of Plant Diseases using Deep Learning Techniques

Authors: MADHURAPANTHULA ABHIRAM, MARELLA VENKATA RAGHU RAM, GUDURU CHAKRAVARTHI, ABDUL JALEEL, Dr.CH.RATHNA JYOTHI

Unique Paper ID: 175183
Volume: 11
Issue: 11
PageNo: 2195-2201

Keywords: Mobilenetv2-Resnet50 hybrid model New Plant diseases Dataset Upload page flask framework Mongodb User Profile user scan history About page Home Page Diseases Page.

Abstract:
This paper describes PlantPulse-a web-based application designed for identification of plant diseases using deep learning techniques. Plant diseases greatly reduce global agricultural productivity, which results in losses in yields and economies. So, identifying plant diseases early is of immense importance. However, traditional methods of identifying these plant diseases can be time-consuming. This project aims to leverage deep learning techniques to automate the identification of plant diseases. The study employs a hybrid model combining MobileNetV2 with Resnet50 to classify and identify different plant diseases. The model is trained and validated using a comprehensive dataset of New Plant Diseases dataset images, ensuring robust performance. The User uploads the images of Plant leafs and the results are displayed in upload page itself. Constructed with the Flask framework and MongoDB, this application gives users the ability to register, log in, and view their history of scans, allowing users to upload different format of images. The application also has educational details including - "About", "How It Works," and "Diseases" pages to explain to users the technology behind, its utility and also has details of symptoms and treatment plans for particular diseases. The purpose of this project is to help users to keep their plants healthy.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 2195-2201

Phytovision Diagnosis: Identification of Plant Diseases using Deep Learning Techniques

Available:https://ijirt.org/article?manuscript=175183

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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