Disease Detection In Wheat Crop (DDWC) / Talha Zaheer, Huma Kalsoom, Gulzar Lilla, Farhan Mustafa. (TCC-31 / BETE-56)

By: Zaheer, TalhaContributor(s): Supervisor Dr. Alina MirzaMaterial type: TextTextMCS, NUST Rawalpindi 2023Description: 85 pSubject(s): UG EE Project | TCC-31 / BETE-56DDC classification: 621.382,ZAH
Contents:
Our project aims to address the challenges faced by Pakistani farmers in identifying plant diseases quickly, leading to reduced crop quality and productivity. To achieve this, we propose the development of a cutting- edge smart phone app utilizing deep learning technology to accurately diagnose plant disease. The focus will be on wheat crops, and we will create our dataset of images to train a convolutional neural network. Our approach involves using transfer learning with the VGG16 architecture to achieve high accuracy and performance in disease identification. Through the implementation of our solution, we hope to empower farmers and increase agricultural productivity, contributing to a more sustainable and prosperous future for Pakistan. The project aims to revolutionize the agricultural industry in Pakistan by leveraging technology to improve plant disease diagnosis and management.
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Item type Current location Home library Shelving location Call number Status Date due Barcode Item holds
Project Report Project Report Military College of Signals (MCS)
Military College of Signals (MCS)
Thesis 621.382,ZAH (Browse shelf) Available MCSPTC-455
Total holds: 0

Our project aims to address the challenges faced by Pakistani farmers in identifying plant diseases quickly, leading to reduced crop quality and productivity. To achieve this, we propose the development of a cutting- edge smart phone app utilizing deep learning technology to accurately diagnose plant disease. The focus will be on wheat crops, and we will create our dataset of images to train a convolutional neural network. Our approach involves using transfer learning with the VGG16 architecture to achieve high accuracy and performance in disease identification. Through the implementation of our solution, we hope to empower farmers and increase agricultural productivity, contributing to a more sustainable and prosperous future for Pakistan. The project aims to revolutionize the agricultural industry in Pakistan by leveraging technology to improve plant disease diagnosis and management.

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