Apple Disease Identification Using Computer Vision

The Apple Disease Identification Project is a digital tool designed to assist farmers, orchard managers, and agricultural specialists in identifying diseases affecting apple trees. Leveraging technologies such as image recognition and machine learning, the project aims to streamline the process of diagnosing apple tree diseases. Users can upload images of affected leaves, fruits, or the entire tree, and the system provides real-time analysis, suggesting potential diseases and treatment options. By enabling rapid and accurate identification, the project helps farmers make informed decisions to protect their orchards and optimize crop yields.

Authors

• @printAsmamaw

Deployment

To deploy this machine learning model on mobile application we convert the .h5 model to .tflite model

  import tensorflow as tf
   model = tf.keras.models.load_model('your_model.h5')

  converter = tf.lite.TFLiteConverter.from_keras_model(model)
  tflite_model = converter.convert()

  with open('converted_model.tflite', 'wb') as f:
    f.write(tflite_model)

After that we deploye the tflite model on flutter mobile application by using

  String res = await Tflite.loadModel(
  model: "assets/appe_disease_model.tflite",
  labels: "assets/labels.txt",
  numThreads: 1, 
  isAsset: true, 
  useGpuDelegate: false 
);

🛠 Skills

Python,Tensorflow,Pandas,Numpy,Conventional Neural Network(CNN),flutter,

Installation

Installation for our project with pip

  !pip install tensorflow
  !pip install numpy
  !pip install pandas
  cd apple project

Roadmap

• Dataset collection for healthy and disease apple leaves

• Preprocess the image dataset

• Image augmentation

• Train test split

• Model selection Conventional Neural Network

• Add the parameter on Conventional Neural Network based on the collected dataset

• Model training with agusted epoch

• Model evaluation with training and testing dataset

• Mobile application development

• Model deployment

Usage/Examples

model =Sequential([ InputLayer(input_shape=input_shape),
Conv2D(32, (3,3),padding='same', activation='relu'), MaxPooling2D((2, 2)),

Conv2D(64, (3,3),padding='same', activation='relu'), MaxPooling2D((2, 2)),

Conv2D(128, (3,3),padding='same', activation='relu'), MaxPooling2D((2, 2)),

Conv2D(256, (3,3), padding='same',activation='relu'),
MaxPooling2D((2, 2)),

Conv2D(512, (3, 3),padding='same',activation='relu'),
MaxPooling2D((2, 2)),

Conv2D(1024, (3, 3),padding='same',activation='relu'),
MaxPooling2D((2, 2)), Dropout(0.5),

Conv2D(1024, (3, 3),padding='same',activation='relu'),
MaxPooling2D((2, 2)), Dropout(0.5),

Flatten(),

Dense(1024,activation='relu'),
Dense(128,activation='relu'),
Dense(64,activation='relu'),

Dense(5,activation='softmax')
                  ])

Screenshots

Sample Dataset

Evaluation of the model

Used By

This project is used by the following companies:

• Apple product Company

  License

Distributed under the MIT License. See LICENSE.txt for more information.

Contacts

--https://www.linkedin.com/in/asmamaw-damte-600a84264/

Acknowledgements

• Free dataset for practice