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+cotton-plant-disease-data/Aphids/1.jpg filter=lfs diff=lfs merge=lfs -text
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+cotton-plant-disease-data/Target[[:space:]]spot/36.jpg filter=lfs diff=lfs merge=lfs -text
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+images/end_to_end_project/sample_training_diseases_images.png filter=lfs diff=lfs merge=lfs -text
+images/end_to_end_project/sample-healthy-leaf.png filter=lfs diff=lfs merge=lfs -text
+ML/models/cotton_plant_disease_model/1/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/Cotton Plant Disease Detection-checkpoint.ipynb

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ML/models.config

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+model_config_list {
+    config {
+        name: 'saved_model'
+        base_path: '/models/cotton_plant_disease_model/'
+        model_platform: 'tensorflow'
+
+        model_version_policy {
+
+            specific {
+                version: 1
+                version: 2
+            }
+        }    
+
+            version_labels {
+                key: 'stable'
+                value: 1
+            }
+
+            version_labels {
+                key: 'canary'
+                value: 2
+            }
+    }
+
+}

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README.md

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----
-title: Cotton Disease Prediction
-emoji: 🌍
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.32.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Cotton-Plant-Disease-Detection
+A deep learning project for cotton plant disease detection using tensorflow
+
+It mainly focus on the diseases which occur only on leaves. However, more research is done on diseases that occur on stem, flowers, buds and boll.
+
+The diseases identified by this model are:
+
+  . Diseases caused by aphids,
+  
+  . Diseases caused by army worms,
+  
+  . Bacterial Blight,
+  
+  . Powdery Mildew and
+  
+  . Target sport.
+
+The data used in this project contains images of all the 5 types of diseases listed above including those of healthy leaves for comparison with the diseased ones.
+
+Below is an example of a healthy cotton plant's leaf:
+
+![sample-healthy-leaf](https://user-images.githubusercontent.com/78556152/210360017-06e7a605-2214-4074-9584-160850d47bcd.png)
+
+
+## Defining some parameters for the loader
+
+Batch size is set to 32
+
+image height set to 180 and 
+
+image width set to 180
+
+## Splitting the Dataset into Training and Validation
+
+The data is split into training and validation
+
+Training set is given 80% of the data and 
+
+Validation set is given 20% of the data
+
+## Classes
+
+The dataset is classified into six classes based on the plant's images of different diseases and the healthy ones.
+
+This classes include; Aphids, Army Worms, Bacterail Blight, Healthy leaf, Powdery Mildew and Target Spot.
+
+The image below shows the classes of the dataset:
+
+![Screenshot 2023-01-03 155358](https://user-images.githubusercontent.com/78556152/210361283-94b2de53-76cf-4787-9a65-75ea18eee1f7.png)
+
+
+Below are some images from the training dataset
+
+![sample_training_diseases_images](https://user-images.githubusercontent.com/78556152/210361611-af3d4977-5c15-4e4f-b591-4f690e390244.png)
+
+
+## Keras Model
+
+The dataset is configured for performance with two functions
+
+data.cache() and 
+
+data.prefetch()
+
+The RGB channel values are standardized to [0,1] range by the use of tf.keras.Rescalling
+
+A Keras model is created and compiled. Below is the summary of the model
+
+![Screenshot 2023-01-03 160123](https://user-images.githubusercontent.com/78556152/210362238-563e08ef-4545-4875-9a9f-444dacb6e0ce.png)
+
+## Training the Model
+
+The model is then trained for 10 epochs as shown below
+
+![Screenshot 2023-01-03 161615](https://user-images.githubusercontent.com/78556152/210364558-340c558f-74d9-4082-9a4d-dd564fa465a6.png)
+
+
+The results are not remarkable with validation accuracy being only 0.6170 despite training accuracy being 0.9895
+
+## Visualize Training Results
+
+Plots on accuracy and loss for training and validation sets are created and below are the results
+
+![training_and_validation_accuracy_and_loss_1](https://user-images.githubusercontent.com/78556152/210365383-57cdef02-3f4a-4e15-ae72-639fc8a1bcea.png)
+
+From visualizing the training results above, the training accuracy is high but the validation accuracy is very low. The same applies to loss; the training loss is lower than the validation loss.
+
+This shows that the model did not fit well causing a problem of overfitting that resulted into huge margins between training and validation results.
+
+Some measures are taken to solve the overfitting problem below.
+
+## Solving the problem of Overfitting
+
+Two methods are used to solve overfitting:
+
+  1. Data Augmentation- this creates modified copies of the dataset using existing data to artificially increase the training set.
+  2. Dropout - This is a layer that randomly sets input units to 0 with a frequency of rate at each step during training time, which helps prevent overfitting.
+
+Below is an example of augmented images:
+
+![sample_augmented_images](https://user-images.githubusercontent.com/78556152/210369044-61e52e36-b7f1-4b65-aa41-325d998cc47a.png)
+
+The code snippet below shows a new model with a dropout layer
+
+![Screenshot 2023-01-03 164714](https://user-images.githubusercontent.com/78556152/210369702-e78e45e3-4631-4d37-90db-e6c027b56293.png)
+
+## Training the New Model and Visualizing the Training Results
+
+The new model trains with remarkable results. The training accuracy is 80% and the validation accuracy is 70%.
+
+Plotting a graph of Accuracy and loss, the training and validation results are closer to each other indicating that the model fit well as shown in the image below.
+
+![training_and_validation_accuracy_and_loss_2](https://user-images.githubusercontent.com/78556152/210370737-6f5a82f5-940e-4967-bce5-50fe9d4780c5.png)
+
+## Predicting on New Data
+
+A new image is given to the model for prediction, the model predicts the image's class with a high degree of accuracy and confidence.
+
+![Screenshot 2023-01-03 165756](https://user-images.githubusercontent.com/78556152/210371600-4312f7ec-f235-4b6e-8a2e-6e9bd4ee9fcc.png)
+
+## Saving the Model and Serving it with tensorflow serving
+
+The model is saved and served with tensorflow serving in docker during production.
+
+![Screenshot 2023-01-03 170143](https://user-images.githubusercontent.com/78556152/210372276-feb6398c-df68-4d29-b5bc-2ac565c5db47.png)
+
+## Conclusion
+
+There are a lot of crop diseases that affect different crops. In this project I focused on those that affect cotton plant specifically on the leaves. This model has done a good job of training and classifying images of five diseases that affect leaves of a cotton plant after which it can then detect a disease if new data is given to it based on those five classes of diseases. I can conclude that it is very possible to train a deep learning model to detect different types of crop diseases when given enough data to train on.