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1. Bhoj Reddy Engineering College for Women
(Sponsored by Sangam Laxmibai Vidyapeet, approved by AICTE and affiliated to JNTUH)
Vinaynagar, IS Sadan Crossroads, Saidabad, Hyderabad – 500 059, Telangana. www.brecw.ac.in
Department of Computer Science and Engineering
Mini Project Design Seminar on
Weed Detection in Agricultural fields
Internal Guide: Mrs B.Pravalika Date : 19 Aug 2023
Assistant Professor Batch No : 20MP32
CSE-B Coordinator: Mrs K.Shireesha T.SaiPriya Reddy 20321A0577
Associate Professor R.Shivani 20321A0591
Ch.Vani 20321A05B5
Dr B Raveendranadh Singh Dr E Madhusudhana Reddy
Professor & Head Professor & Principal

2. Introduction
The growths of invasive weeds are hard to control as it grows fast and compete with
another crop aggressively. Fungi bacteria and nematodes may be introduced and this is
difficult to control and causing the grower to decrease harvest yield and revenue. It is costly
to destroy the weed with herbicide and reduce the margin of the cultivator. By using deep
learning we can detect the weed and it is a tool used to build intelligent systems as close
to human brain.

3. Scope
• The scope of weed detection in agricultural fields using deep learning is to automate the
process of identifying and removing weeds from crops.
• By detecting and removing weeds, farmers can improve their crop yields and reduce the
use of herbicides, which can be harmful to the environment.

4. Existing System
At present, weeds and crops research at home and abroad generally study and explore
from the following perspectives. On the one hand, the field seedlings are identified based on
image features such as color, texture, shape, and the like. However, factors such as the
similar color of crops and weeds and overlapping leaves may affect the recognition rate. On
the other hand, it is a method of identifying grass and grass based on stereo vision.
Disadvantages:
 Low Accuracy
 Low FPS

5. Proposed System
This system introduces a weed identification method based on convolutional neural
network. The proposed method can be applied not only to the data set of , but also to the
type of weed. Combining ground truth images and constructing multiple convolutional neural
networks for identification and comparison, the only regret is that crops and weeds in the
picture are infected to some extent, and the recognition accuracy needs to be
improved.This system proposes a convolutional neural network weed recognition based on
machine vision combined with digital image processing technology.

6. Advantages of Proposed System
o More Accuracy
o Convent to use
o High FPS
o Automatic Feature Extraction

7. Requirement Analysis
Functional Requirements:
System:
Image Processing
Annotated Dataset Collection
Feature-Extraction
Classification
User:
Input: User can submit an image for prediction of proper weed.
Output: Process the user input image and views the output.

8. Non-Functional Requirements:
Portability:Ability to run and operate seamlessly across different platforms ,
environments.
 Security:Code and database should be secured so that unauthorized user cannot
access it.
 Maintainability: Code maintainability and readability for ease of future updates.
 Scalability:Ability to handle a growing number of users and increasing data volumes.
 Performance: Real-time disease detection to provide timely results.

9. Computational Resources:
Hardware Requirements:
Processor : Intel i5 and above
RAM : 8GB and above
Hard Disk : 500GB Minimum
Software Requirements:
Programming Languages /Platform : Python 3.6
IDE : pycharm
Operating System : Windows 10 and above

10. Data Pre-processing Techniques:
• Resize images
• Convert images into pixels
• Scale pixel
• Augmentation

11. Design
• Design represents the number of components we are using as a part of the project and
the flow of request processing i.e., what components in processing the request and in
which order.
• An architecture description is a formal description and representation of a system
organized in a way that supports reasoning about the structure of the system.

12. Software Architecture

13. Architecture

14. Software Process Model

15. Implementation steps and algorithm
• A CNN is a kind of network architecture for deep learning algorithms and is specially used
for image recognition and tasks that involve the processing of pixel data.
• The architecture of a convolutional neural network is a multi-layered network, made by
stacking many hidden layers on top of each other in sequence.
• It is this sequential design that allows convolutional neural networks to learn hierarchical
features.
• A general CNN consists of the following layers:
• Convolutional layers
• Pooling layers

16. Steps:
1.Input layer:
The input layer receives the raw image data, typically in the form of pixel values.The
size of this layer corresponds to the dimensions of the input image, e.g., 224x224 pixels
for many standard architectures.
2.Convolutional layer:
• Convolutional layers are the core building blocks of CNNs. They consist of multiple
learnable filters (kernels) that slide across the input image.
• Each filter extracts different features, such as edges, textures, or more complex patterns,
by performing element-wise multiplications and summing the results.
• Convolutional layers help the network automatically learn relevant features from the
input data.

17. Activation Function:
• After each convolution operation, an activation function is applied, typically Rectified
Linear Unit (ReLU).
• ReLU introduces non-linearity into the model, allowing it to learn complex relationships in
the data.
3.Pooling layer:
• Pooling layers reduce the spatial dimensions of the feature maps while retaining the most
essential information.
• Max pooling is a common technique where the maximum value in a local region of the
feature map is taken, reducing the feature map's size.
• Pooling helps make the model more robust to variations in the input and reduces
computational complexity.

18. Fully Connected layer:
• Fully connected layers come after one or more convolutional and pooling layers.These
layers flatten the output from the previous layers into a 1D vector and connect it to a
dense neural network.
• The final fully connected layer often has as many neurons as there are classes in the
problem (two for weed detection: weed or non-weed).
4.Output Layer:
• The output layer typically uses a softmax activation function to convert the network's raw
output into class probabilities.
• In weed detection, there are two classes: one for weeds and one for non-weeds.

19. Algorithm:
 VGG Algorithm:
• VGG (Visual Geometry Group) is a convolutional neural network (CNN) architecture that
gained popularity for its simplicity and effectiveness in image classification tasks. The VGG
architecture is characterized by its use of small 3x3 convolutional filters and its deep
structure with multiple layers.
• The layers in VGG are composed of a combination of convolutional layers, followed by max-
pooling layers for downsampling, and fully connected layers at the end for classification.
VGG networks are known for their deeper architecture, which makes them suitable for
capturing intricate features in images.
• However VGG networks are used in making training and deployment more resource-
demanding compared to some later architectures like ResNet or Inception.

20. CNN Architecture

21. Expected output

22. Conclusion
This project concludes that Weed detection is a critical task for agricultural productivity. This
requires improved computational methods that allow faster response.Therefore, the proposed
method has higher accuracy than the existing methods.

23. Any Queries?

24. References
• G.L.N. Murthy, Baji Baba Shaik, Ch Uday Reddy, V ManikanteswarRao,”Multilayered
CNN Architecture for Assistance in Smart Farming", 2023 International Conference
on Inventive Computation Technologies (ICICT), pp.347-351.
• Weed Identification Using Deep Learning Techniques",2023 International Conference
on Computer Communication and Informatics (ICCCI), pp.
• K. Osorio, A. Puerto, C. Pedraza, D. Jamaica and L. Rodríguez, "A deep learning
approach for weed detection in lettuce crops using multispectral images", Agri Engi-
neering, vol. 2, no. 3, pp. 471-488