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