A Crop Pest Classification Model Using Deep Learning Techniques
Product Description
Aim:
To detect and classify large-scale multi-class pest using Convolution Neural Network.
Synopsis:
Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolution neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features.
Proposed System:
Our PestNet consists of three stages: pest feature extraction, pest regions search and pest prediction. In PestNet, the input image is firstly fed into a CNN backbone to extract feature maps, where CSA module is proposed for feature enhancement. Trained the model using CNN algorithm and predict the pest. Finally, we create the android application for Pest Classification.
When you order from finalyearprojects.in, you will receive a confirmation email. Once your order is shipped, you will be emailed the tracking information for your order's shipment. You can choose your preferred shipping method on the Order Information page during the checkout process.
The total time it takes to receive your order is shown below:
The total delivery time is calculated from the time your order is placed until the time it is delivered to you. Total delivery time is broken down into processing time and shipping time.
Processing time: The time it takes to prepare your item(s) to ship from our warehouse. This includes preparing your items, performing quality checks, and packing for shipment.
Shipping time: The time for your item(s) to tarvel from our warehouse to your destination.
Shipping from your local warehouse is significantly faster. Some charges may apply.
In addition, the transit time depends on where you're located and where your package comes from. If you want to know more information, please contact the customer service. We will settle your problem as soon as possible. Enjoy shopping!
Download Abstract
Click the below button to download the abstract.
Package Includes
Software Projects Includes
- Demo Video
- Abstract
- Base paper
- Full Project PPT
- UML Diagrams
- SRS
- Source Code
- Screen Shots
- Software Links
- Reference Papers
- Full Project Documentation
- Online support
The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days
Hardware Projects Includes
- Demo Video
- Abstract
- Base paper
- Full Project PPT
- Datasheets
- Circuit Diagrams
- Source Code
- Screen Shots & Photos
- Software Links
- Reference Papers
- Lit survey
- Full Project Documentation
- Online support
The Delivery time for Hardware
projects is 7-8 working days.
Mini Projects: Software Includes
- Demo Video
- Abstract
- Base paper
- Full Project PPT
- UML Diagrams
- SRS
- Source Code
- Screen Shots
- Software Links
- Reference Papers
- Full Project Documentation
- Online support
The
Delivery time for software Miniprojects is 2 -3 working days.
Mini Projects - Hardware includes
- Demo Video
- Abstract
- PPT
- Datasheets
- Circuit Diagrams
- Source Code
- Screen Shots & Photos
- Software Links
- Reference Papers
- Full Project Documentation
- Online
support
The Delivery time for Hardware Mini projects is 7-8 working days.