Grape Leaves Disease Detection



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Grape Leaves Disease Detection

Built a Deep Learning model using TensorFlow and Keras for grape leaf disease detection

Photo by Sasha Kolesnik on Dribbble

Aim

To develop a Deep Learning model to identify diseased grape leaves using python programming language and Deep learning libraries

Prerequisites

Before getting started, you should have a good understanding of:

  1. Python programming language
  2. Deep Learning Libraries(Tensorflow, Keras, OpenCV)

Dataset

Link to download the dataset:

URL: https://cainvas-static.s3.amazonaws.com/media/user_data/vomchaithany/Grapes-Leaf-Disease-data.zip

get and unzip the data

out:

--2021-06-28 13:36:55--  https://cainvas-static.s3.amazonaws.com/media/user_data/vomchaithany/Grapes-Leaf-Disease-data.zip
Resolving cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)... 52.219.64.72
Connecting to cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)|52.219.64.72|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 23864365 (23M) [application/zip]
Saving to: ‘Grapes-Leaf-Disease-data.zip’
Grapes-Leaf-Disease 100%[===================>] 22.76M 81.8MB/s in 0.3s 2021-06-28 13:36:55 (81.8 MB/s) - ‘Grapes-Leaf-Disease-data.zip’ saved [23864365/23864365]

Preparing data for the model

we assign train data to dirtrain and test data to dirtest

we are going to deal with 4 categories :

  1. Black_rot
  2. Esca_(Black_Measles)
  3. Health
  4. Leaf_blight_(Isariopsis_Leaf_Spot)

out:

Converting the images into arrays and storing them with labels

shuffling the data

Resizing the images

out:

Creating the pickle files

Model

out:

Model: “sequential”
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 256, 256, 32) 896
_________________________________________________________________
conv2d_1 (Conv2D) (None, 254, 254, 32) 9248
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 31, 31, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 31, 31, 32) 9248
_________________________________________________________________
conv2d_3 (Conv2D) (None, 29, 29, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 3, 3, 32) 0
_________________________________________________________________
activation (Activation) (None, 3, 3, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 288) 0
_________________________________________________________________
dense (Dense) (None, 256) 73984
_________________________________________________________________
dense_1 (Dense) (None, 4) 1028
=================================================================
Total params: 103,652
Trainable params: 103,652
Non-trainable params: 0
_________________________________________________________________

out:

Epoch 1/10
2/42 [>.............................] - ETA: 4s - loss: 33.4274 - accuracy: 0.4062WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0359s vs `on_train_batch_end` time: 0.0988s). Check your callbacks.
42/42 [==============================] - 6s 140ms/step - loss: 4.3365 - accuracy: 0.4440 - val_loss: 5.3007 - val_accuracy: 0.3276
Epoch 2/10
42/42 [==============================] - 6s 135ms/step - loss: 1.2223 - accuracy: 0.5103 - val_loss: 2.2340 - val_accuracy: 0.4095
Epoch 3/10
42/42 [==============================] - 6s 134ms/step - loss: 0.8702 - accuracy: 0.6398 - val_loss: 1.6332 - val_accuracy: 0.4698
Epoch 4/10
42/42 [==============================] - 5s 120ms/step - loss: 0.6871 - accuracy: 0.7380 - val_loss: 0.5808 - val_accuracy: 0.7198
Epoch 5/10
42/42 [==============================] - 6s 133ms/step - loss: 0.5256 - accuracy: 0.7761 - val_loss: 0.7016 - val_accuracy: 0.7457
Epoch 6/10
42/42 [==============================] - 6s 133ms/step - loss: 0.4148 - accuracy: 0.8378 - val_loss: 0.8888 - val_accuracy: 0.7155
Epoch 7/10
42/42 [==============================] - 6s 135ms/step - loss: 0.3828 - accuracy: 0.8462 - val_loss: 0.6643 - val_accuracy: 0.7931
Epoch 8/10
42/42 [==============================] - 5s 123ms/step - loss: 0.2999 - accuracy: 0.8919 - val_loss: 0.3769 - val_accuracy: 0.8664
Epoch 9/10
42/42 [==============================] - 5s 125ms/step - loss: 0.2474 - accuracy: 0.9177 - val_loss: 0.3600 - val_accuracy: 0.9009
Epoch 10/10
42/42 [==============================] - 5s 130ms/step - loss: 0.1429 - accuracy: 0.9528 - val_loss: 0.2745 - val_accuracy: 0.8966

Save the model

loss and accuracy of the model

out:

18/18 - 1s - loss: 0.1902 - accuracy: 0.9304
Restored model, accuracy: 93.04%

The Metrics

out:

out:

Custom Predictions

out:

out:

'Healthy'

Cainvas Gallery: https://cainvas.ai-tech.systems/gallery/

Conclusion

We’ve trained our Deep Learning model using TensorFlow and Keras for detecting grape leaf disease and got an accuracy of 93%.

Credit: Om Chaithanya V

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