Plant Disease Detection Project Overview

The video tutorial introduces a Python project on plant disease detection and classification using machine learning. It highlights the importance of detecting plant diseases in agriculture and presents a model based on the Inception V3 architecture. The tutorial provides a step-by-step guide on executing the project, testing it with various plant images, and evaluating the model's accuracy. The project aims to classify healthy and diseased leaves, achieving a training accuracy of 91.34% and a validation accuracy of 89.21%.

What is the main goal of the plant disease detection project discussed in the video?

• To create a new agricultural tool

• To develop a new type of plant

• To classify healthy and diseased leaves

• To increase the yield of crops

How many diseases and classes are involved in the project?

• 30 diseases and 50 classes

• 26 diseases and 38 classes

• 15 diseases and 30 classes

• 10 diseases and 20 classes

Which architecture is used in the proposed model for plant disease detection?

• VGG16

• Inception V3

• AlexNet

• ResNet

What is the achieved training accuracy of the model?

• 85.67%

• 95.12%

• 91.34%

• 78.45%

What is the first step in executing the project?

• Open the browser

• Install the necessary libraries

• Copy the source code location

• Run the application

What is the default username and password for the static login page?

• guest, guest

• root, root

• user, user

• admin, admin

Which plant disease is predicted for an apple image?

• Apple mildew

• Apple scab

• Apple rust

• Apple blight

What is the predicted result for a tomato image?

• Tomato blight

• Tomato wilt

• Tomato rust

• Tomato bacterial spot

Which disease is identified in a grape image?

• Grape downy mildew

• Grape black rot

• Grape powdery mildew

• Grape black measles

What type of graph is shown in the chat part of the project?

• Dynamic graph

• Interactive graph

• Static graph

• Real-time graph