The paper presentation "CATTLE BREED DETECTION AND CATEGORIZATION USING IMAGE PROCESSING AND MACHINE LEARNING" was presented at the ASSIC IEEE Conference in 2022 at KIIT. The presentation focused on using image processing and machine learning techniques to detect and categorize different breeds of cattle. The presentation began by discussing the importance of accurately identifying cattle breeds for breeding programs, health management, and genetic conservation. The presenters highlighted the challenges associated with manual identification of cattle breeds, which can be time-consuming, labor-intensive, and error-prone. To address these challenges, the presenters proposed an automated system that uses image processing and machine learning algorithms to detect and categorize cattle breeds. The system consists of a camera that captures images of the cattle, which are then processed using computer vision techniques to extract relevant features such as the color, texture, and shape of the animal. The extracted features are then fed into a machine learning algorithm that has been trained on a dataset of labeled cattle images to classify the breed of the animal. The presenters discussed the performance of different machine learning algorithms and feature extraction techniques and compared their results. The presentation concluded by highlighting the potential benefits of using the proposed system in cattle management, such as reducing the time and cost associated with manual breed identification, improving breeding programs, and aiding in genetic conservation efforts. Overall, the paper presentation "CATTLE BREED DETECTION AND CATEGORIZATION USING IMAGE PROCESSING AND MACHINE LEARNING" showcased the application of cutting-edge technologies in the field of agriculture and demonstrated the potential benefits of using these technologies in cattle management.

1. Paper Presentation
CATTLE BREED DETECTION AND
CATEGORIZATION USING IMAGE
PROCESSING AND MACHINE
LEARNING
Designed and developed by:
1.Neil Duraiswami
2.Shrikant Bhalerao
3.Abhishek Watni
4.Chetan Aher

2. Problem statement :
Cattle Breed Detection and Categorization using Machine Learning
Objectives achieved:
A technique has been proposed which involves the use of support vector
machine to identify cow breeds.
To develop a system which can process image and enable user to get
information about cow breed name, breed quality like high or low ,
Breed's basic information like colour, avg milk quantity, avg body mass,
etc.

3. Introduction
The development of machine learning and soft computing techniques
has provided many opportunities for researchers to establish new
analytical methods in different scientific fields.
In our project, a technique has been proposed which involves the
image processing and Support vector machine to extract the features
by processing images.
The proposed system enables us to identify 7 different breeds and
display their basic information like colour, average milk quantity,
average body mass, etc.

4. PROPOSED SYSTEM
Input Images Preprocessing Segmentation Features Clustering Classification Prediction
The proposed system offers a robust approach to the selection
about unique livestock breeds. The Block diagram describes the
architecture of the proposed system.

5. Dataset Composition
In this model, over 1000 images were collected as a dataset. The dataset was
categorized into seven types of cattle in different regions of India and other
countries.
1. Ayrshire
2. Brown Swiss
3. Holstein Friesian
4. Jersey
5. Kankrej
6. Malvi
7. Red Dane.
20%
19%
21%
20%
2%
2%
16%
Dataset
Ayrshire
Brown Swiss
Holstein Friesian
Jersey
Kankrej
Malvi
Red Dane

6. Image Grey Scaling
• Grey scaling is the process of transforming an image from another colour
space e.g., RGB, CMYK, HSV, etc. It differs between all black and all
white. In digital images, greyscale means that the value of each pixel
represents only light intensity information. Such images usually only
display from the darkest black to the lightest white.

7. Grey Scaling Algorithm
•Step 1: Obtain the red, green, and blue values ​​for the pixel
•Step 2: Use mathematical functions to transfigure these numbers to a single
grey value.
Formula: 𝑮 = 𝟎. 𝟐𝟗𝟗𝑹 + 𝟎. 𝟓𝟖𝟔𝑮 + 𝟎. 𝟏𝟏𝟒𝑩
Here G is the greyscale value, and R, G and B are intensity values of red,
green and blue respectively.
•Step 3: Reconstitute the original red, green and blue values ​​with the new
grey values.

8. Image Watershed
• Watershed algorithms are used in image processing mainly for the purpose
of object segmentation, i.e., to separate different objects in an image. This
allows counted objects or separate objects to be analysed further. The
watershed algorithm is a classical algorithm used for segmentation and is
particularly useful when extracting objects that touch or overlap in an
image. There are different technical definitions of watershed. In a graph,
watersheds can be defined on nodes, on edges, or hybrids on nodes and
edges. Watersheds can also be defined in the continuum. There are also
various algorithms for calculating watersheds. Source classification
algorithms are used in image processing mainly for the purpose of object
segmentation, i.e., to separate different objects in an image. This allows
counted objects or separate objects to be analysed further

9. Watershed Algorithm Steps
Step 1: Read in the Color Image and Convert it to Grayscale.
Step 2: Use the Gradient Magnitude as the Segmentation Function.
Step 3: Mark the Foreground Objects.
Step 4: Compute Background Markers.
Step 5: Compute the Watershed Transform of the Segmentation
Function.
Step 6: Visualize the Result.

10. Grey Scaling
Water Shading

11. FEATURE EXTRACTION
Feature extraction is a dimensionality reduction process in
which an initial set of raw data is reduced to a more manageable
group for processing. The hallmark of these large amounts of data
is the large number of variables that require a lot of computational
resources to process.
The model extracts local features of the cattle (horn, ears, back humps,
coat patterns).
Ear
Fur pattern

12. What is Support Vector Machine?
• SVM is a supervised machine learning algorithm used for classification
or regression problems.
• It transforms the data using a technique called kernel tricks and finds
the optimal boundaries between possible outputs based on those
transformations.
• The goal of the SVM algorithm is to create optimal lines or decision
boundaries that can divide n-dimensional space into classes so that
new data points can be easily placed in the correct category in the
future. This best decision boundary is called the hyperplane.

13. The SVM algorithm steps include the
following:
Step 1: Load the important libraries.
Step 2: Import dataset and extract the X variables and Y separately.
Step 3: Divide the dataset into train and test.
Step 4: Initializing the SVM classifier model.
Step 5: Fitting the SVM classifier model.
Step 6: Coming up with predictions.

14. Polynomial Kernel for Higher Dimensions
Following is the formula for the polynomial kernel:
𝒇(𝑿𝟏,𝑿𝟐) = (𝑿𝟏𝑻 .𝑿𝟐 + 𝟏) 𝒅
Here X1 and X2 are features, d is the degree and of the polynomial,
which we need to specify manually.

15. System Architecture

16. Use-Case
Diagram:

17. Output of the Predictive Model

18. Conclusion
Dairy is one of the biggest agri-business in the world and a significant
contributor to any countries economy. Automatic identification of cattle
breeds is most important since cross breeds are becoming more and native
breeds are lost. In our proposed system we collected more than 100 cattle
image datasets, these images went through a preprocess where they were
converted to a particular dimension, removed noises. Then proceeded with
feature extraction using SIFT method where the different body parts of the
cattle were extracted. Finally, we classified the cattle to 7 classes using SVM
and predicted the cattle breed. This proposed system works only when the
image of the cattle is complete single animal. Future work involves
identifying cattle in different backgrounds where other animals are also
there in the images. Further segregation of cattle’s where the image consists
different breeds of cattle’s. The propose system enable us to know Breed's
basic information like colour, avg milk quantity, avg body mass, etc.

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21. Thank You