1.	Reproducibility
o	README file: Introduce and explain your code; provide steps for implementation. A README file should generally have:
      Ans:   We are going to provide all details  README file accordingly. 
2.	‎Title – Name of the project or dataset.
Ans: VGG-19-based Automated Fruit Classification for Smart Agriculture. We have used Fruits-360 dataset available on Kaggle.  I have used 2 datasets to check the generalizability of the proposed model. These both datasets are available on Kaggle. Links are as fellow. 
Dataset1: https://www.kaggle.com/datasets/asifalij/dataset1
Dataset2: https://www.kaggle.com/datasets/asifalij/dataset2

3.	Description – An overview of the code/dataset.
Ans: This project implements an image classification model using the VGG-19 architecture to classify fruits into various categories. The approach utilizes transfer learning with the VGG-19 convolutional neural network pre-trained on ImageNet, fine-tuned on a custom fruit dataset to improve classification performance.
4.	Dataset Information.
Ans: The following fruits, vegetables and nuts and are included in dataset: Apples (different varieties: Crimson Snow, Golden, Golden-Red, Granny Smith, Pink Lady, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red, Lady Finger), Beans, Beetroot Red, Blackberry, Blueberry, Cabbage, Caju seed, Cactus fruit, Cantaloupe (2 varieties), Carambula, Carrot, Cauliflower, Cherimoya, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Chestnut, Clementine, Cocos, Corn (with husk), Cucumber (ripened, regular), Dates, Eggplant, Fig, Ginger Root, Goosberry, Granadilla, Grape (Blue, Pink, White (different varieties)), Grapefruit (Pink, White), Guava, Hazelnut, Huckleberry, Kiwi, Kaki, Kohlrabi, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango (Green, Red), Mangostan, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine (Regular, Flat), Nut (Forest, Pecan), Onion (Red, White), Orange, Papaya, Passion fruit, Peach (different varieties), Pepino, Pear (different varieties, Abate, Forelle, Kaiser, Monster, Red, Stone, Williams), Pepper (Red, Green, Orange, Yellow), Physalis (normal, with Husk), Pineapple (normal, Mini), Pistachio, Pitahaya Red, Plum (different varieties), Pomegranate, Pomelo Sweetie, Potato (Red, Sweet, White), Quince, Rambutan, Raspberry, Redcurrant, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red, Yellow, not ripened, Heart), Walnut, Watermelon, Zucchini (green and dark). Its very difficult to classify all fruits, vegetables and nuts. The dataset We have used consisted of fruits. Detail is provided in manuscript.
5.	Code Information.
      Ans: The code uses TensorFlow/Keras to implement VGG-19-based transfer learning.
      Includes:
•	Data augmentation using ImageDataGenerator
•	Model training and validation
•	Confusion matrix for performance evaluation
•	Accuracy and loss curves plotting
6.	Usage Instructions – How to use or load the dataset and code.
Ans:  To run the notebook:
1.	Ensure Python environment with the required dependencies.
2.	Place the dataset in the appropriate directory structure.
3.	Run the notebook cells sequentially to preprocess data, train the model, and visualize results.

7.	Requirements – Any dependencies (e.g., Python libraries).
Ans: Install the following Python libraries
          Keras , Numpy, Sklearn, matplotlib, tensorflow, seaborn and matplotlib plotly
8.	Methodology (if applicable) – Steps taken for data processing or modeling.
Ans: Data Loading: Using ImageDataGenerator for loading and augmenting images.
      Model Architecture: Pre-trained VGG-19 from Keras without the top layer; a custom dense layer is added for classification.
Training:
•	Epochs: 10
•	Batch size: 32
Evaluation:
•	Confusion matrix
•	Accuracy and loss plots for training and validation sets

9.	Citations (if applicable) – If this dataset was used in research, provide references.
Ans: The dataset link is provided in dataset sections. And I have also added references for VGG-19 model in the revised manuscript. 
10.	License & Contribution Guidelines (if applicable).
Ans: No license or contribution guidelines were specified in the notebook. You can include a default license
11.	Materials & Methods
o	Computing infrastructure (operating system, hardware, etc)
o	Selection method: Explain the selection method for the techniques you have chosen to implement. For example: you must explain and justify why VGG-19 models were used.
   Ans:  Computing Infrastructure
•	Operating System: Windows but I used Kaggle and Google Colab environment)
•	Libraries: TensorFlow, Keras, NumPy, Matplotlib, scikit-learn
•	Hardware: GPU (recommended)
Selection Method
•	Why VGG-19?
•	Pre-trained on ImageNet – suitable for transfer learning.
•	Deep architecture (19 layers) allows rich feature extraction.
•	Proven performance in many image classification tasks.
•	Simplifies training on small datasets by leveraging learned features.

12.	Conclusions (Limitations: Identify limitations in your study).
Ans:  The following are the limitation of this model.
•	Dataset Quality: Model performance heavily depends on the quality and variety of images.
•	Overfitting Risk: Without proper regularization or validation, risk of overfitting remains.
•	Fixed Input Size: Resizing to 100x100 may distort certain features, reducing accuracy.