Merge pull request #758 from jeet-Abhi123/part-1

[Project Addition]: Almond Varieties Image Classification

Almond Varieties Image Classification/Dataset/README.md

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+# **Almond Varieties**
+
+Almond varieties named Ak, Nurlu, Kapadokya and Sıra collected from different regions of Turkey were used in the study. To capture images of the almonds, a special chamber with homogeneous lighting was designed. The Ak, Nurlu and Sıra almond varieties are grown in the Datça district of Muğla in the Aegean region of Turkey, while the Kapadokya almond is grown in Nevşehir in Central Anatolia. Both regions have suitable climate and soil properties for almond cultivation. A total of 1556 images of the four varieties were captured.
+### Dataset Link : https://www.kaggle.com/datasets/mahyeks/almond-varieties/data

Almond Varieties Image Classification/Models/README.md

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+# **Almond Varieties Image Classification**
+
+This project aims to classify different varieties of almonds using state-of-the-art deep learning models. The models implemented in this project are InceptionV3, VGG19, and ResNet50. These models are well-known for their performance in image classification tasks.
+
+### Models Implemented
+- **InceptionV3**
+InceptionV3 is a convolutional neural network architecture from the Inception family, originally introduced by Google. It employs a technique called "factorized convolutions" to reduce computational cost while maintaining high accuracy. InceptionV3 is known for its efficiency and accuracy in image classification tasks.
+
+- **VGG19**
+VGG19 is a deep convolutional neural network architecture from the Visual Geometry Group (VGG) at the University of Oxford. It has 19 layers and is characterized by its simplicity, using only 3x3 convolutional layers stacked on top of each other in increasing depth. VGG19 is known for its excellent performance on image recognition tasks.
+
+- **ResNet50**
+ResNet50 is a deep residual network introduced by Microsoft Research. It consists of 50 layers and uses residual learning to tackle the vanishing gradient problem, making it easier to train deeper networks. ResNet50 is renowned for its high accuracy and robustness in various image classification challenges.
+
+### Libraries Used
+The following libraries were used to implement the models:
+
+- TensorFlow: An open-source library developed by Google for numerical computation and large-scale machine learning.
+- Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow.
+- NumPy: A fundamental package for scientific computing with Python, used for handling arrays and performing mathematical operations.
+- Pandas: A data manipulation and analysis library for Python.
+- Matplotlib: A plotting library for Python and its numerical mathematics extension, NumPy.

Almond Varieties Image Classification/README.md

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+# **Almond Varieties Image Classification**
+
+### 🎯 Goal
+The primary goal of this project is to develop an accurate and efficient image classification system capable of distinguishing between different varieties of almonds. By leveraging advanced deep learning models, the project aims to provide a robust solution for the automated identification of almond varieties, which can be beneficial for various applications in agriculture, food industry, and quality control.
+
+### Purpose
+The purpose of this project includes the following:
+
+1. Automation of Almond Classification: To automate the process of identifying almond varieties, reducing the need for manual inspection and classification.
+2. Enhancing Accuracy: To achieve high accuracy in classification using state-of-the-art deep learning models (InceptionV3, VGG19, and ResNet50), ensuring reliable identification of almond types.
+3. Efficiency in Processing: To provide a fast and efficient method for processing large volumes of almond images, making it scalable for industrial applications.
+4. Support for Agricultural Research: To contribute to agricultural research by providing a tool that can help in studying and monitoring different almond varieties.
+5. Quality Control: To aid in quality control processes by accurately identifying and sorting almond varieties, ensuring product consistency and quality in the food industry.
+Through this project, we aim to demonstrate the practical application of deep learning models in agricultural and industrial contexts, showcasing how technology can enhance productivity and accuracy in classification tasks.
+
+### 🧵 Dataset : https://www.kaggle.com/datasets/mahyeks/almond-varieties/data
+
+### 🧾 Description
+This project focuses on developing a sophisticated image classification system to identify different varieties of almonds using deep learning techniques. The system leverages three state-of-the-art convolutional neural network (CNN) architectures: InceptionV3, VGG19, and ResNet50, each known for their robustness and high performance in image recognition tasks.
+
+**Key Features**
+Model Implementations: Utilizes three prominent CNN models:
+
+- InceptionV3: Known for its efficiency and factorized convolutions, reducing computational cost while maintaining high accuracy.
+- VGG19: Characterized by its deep yet simple architecture, using 3x3 convolutional layers to achieve excellent image recognition performance.
+- ResNet50: Employs residual learning to train very deep networks, addressing the vanishing gradient problem and achieving high accuracy.
+
+### 🚀 Models Implemented
+- **InceptionV3**: Chosen for its balance between efficiency and accuracy, making it suitable for large-scale image classification with moderate computational resources.
+- **VGG19**: Selected for its simplicity and high performance in tasks requiring detailed feature extraction, making it a strong baseline model.
+- **ResNet50**: Opted for its ability to train very deep networks without suffering from vanishing gradients, allowing for the capture of intricate image details necessary for accurate classification of almond varieties.
+
+### 📚 Libraries Needed
+- TensorFlow: For building and training deep learning models.
+- Keras: For simplifying the creation and training of neural networks.
+- NumPy: For numerical computations and array operations.
+- Pandas: For data manipulation and analysis.
+- Matplotlib: For plotting and visualizing data.
+- Pillow: For image processing tasks.
+
+### 📊 Exploratory Data Analysis Results
+A total of 1556 images of the four varieties were captured.
+![Screenshot 2024-06-07 203324](https://github.com/jeet-Abhi123/Road-Safety-Data-Analysis-Power-BI-/assets/143840497/baa18e75-2a03-4905-994d-cfb1ee128b46)
+
+### 📈 Performance of the Models based on the Accuracy Scores
+
+**InceptionV3 Performance**
+![InceptionV3_results](https://github.com/jeet-Abhi123/Road-Safety-Data-Analysis-Power-BI-/assets/143840497/824d4893-3d6f-404c-8094-48ae64f888cc)
+
+**VGG19 Performance**
+![VGG19_results](https://github.com/jeet-Abhi123/Road-Safety-Data-Analysis-Power-BI-/assets/143840497/e648c009-1a20-4662-9f5a-72d39a88be3c)
+
+**ResNet50 Performance**
+![ResNet_results](https://github.com/jeet-Abhi123/Road-Safety-Data-Analysis-Power-BI-/assets/143840497/709f2625-7d23-499b-9af9-71014c1c063f)
+
+### 📢 Conclusion
+This project successfully demonstrates the use of advanced deep learning models to classify different varieties of almonds. By leveraging InceptionV3, VGG19, and ResNet50, the project highlights the effectiveness and performance of these architectures in image classification tasks.
+
+### Accuracy Results
+1. InceptionV3
+	- Training Accuracy: 99.75%
+	- Validation Accuracy: 99.65%
+2. VGG19
+	- Training Accuracy: 95.71%
+	- Validation Accuracy: 98.95%
+3. ResNet50
+	- Training Accuracy: 48.91%
+	- Validation Accuracy: 59.03%
+
+**Best Fitted Model**
+Based on the accuracy scores, InceptionV3 is the best-fitted model for this project, achieving the highest training and validation accuracy. This indicates that InceptionV3 is most effective for classifying almond varieties with excellent precision.
+
+- InceptionV3: Best performance with near-perfect accuracy, suitable for deployment.
+- VGG19: High accuracy but slightly lower than InceptionV3, still very reliable.
+- ResNet50: Underperformed in this context, likely due to training issues or data requirements.
+In conclusion, InceptionV3 is recommended for practical applications in almond variety classification due to its superior accuracy and efficiency.
+
+## ✒️ Contributor
+### Name : Abhijeet Kaithwas
+LinkedIn : https://www.linkedin.com/in/abhijeet-kaithwas-1866b5256/