Prasad Chaskar
Prasad Chaskar

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Models Home » Domain Usecases » Agriculture » Potato Leaf Diseases Detection

Potato Leaf Diseases Detection

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Model Overview

Introduction :

Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region.


Problem Solving Approach :
Recently, numerous types of deep learning architecture has been proposed for plant disease classification. The most prominent technique is the convolutional neural network (CNN). A convolutional neural network is a supervised deep learning model inspired by the biological nervous system and vision system with significant performance compared to other models. As compared to Artificial Neural Network (ANN), CNN requires few neurons and multilayer convolution layers to learn the features, but it required an extensive dataset for training.

Required Libraries :

import tensorflow as tf

from tensorflow.keras import models, layers

import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd

import cv2

import numpy as np

import PIL

from sklearn.metrics import  classification_report,confusion_matrix

from sklearn.model_selection import train_test_split

​

Load Dataset :

from pathlib import Path

my_path = Path(r'/content/PlantVillage')



Potato___Early_blight = list(my_path.glob('Potato___Early_blight/*'))

Potato___Late_blight = list(my_path.glob('Potato___Late_blight/*'))

Potato___Late_blight = list(my_path.glob('Potato___Late_blight/*'))​

potato_images_dict = {

    'Potato___Early_blight': list(my_path.glob('Potato___Early_blight/*')),

    'Potato___Late_blight': list(my_path.glob('Potato___Late_blight/*')),

    'Potato___healthy': list(my_path.glob('Potato___healthy/*'))

}

potato_labels_dict = {

    'Potato___Early_blight': 0,

    'Potato___Late_blight': 1,

    'Potato___healthy': 2

}​

Summary of PlantVillage Dataset:

Split Data into Dependent variable and Independent variable :


X = []

y = []



for name,images in potato_images_dict.items():

    for image in images:

        img = cv2.imread(str(image))

        resize = cv2.resize(img,(256,256))

        X.append(resize)

        y.append(potato_labels_dict[name])

Data Preprocessing : 


X = np.array(X)

y = np.array(y)



X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.1)



X_train_scaled = X_train / 255

X_test_scaled = X_test /  255

Create Dictionary with class names and corresponding lables :


class_names = {0:'Potato___Early_blight',1:'Potato___Late_blight',2:'Potato___healthy'}

Print Some Images from our Dataset : 


plt.figure(figsize=(16,10))

for i in range(12):

      ax = plt.subplot(3,4,i+1)

      plt.imshow(X_train_scaled[i])

      plt.title(class_names[y_train[i]],{'fontsize':20})

      plt.axis("off")




Train Model :

model = models.Sequential([

    layers.Conv2D(filters=32, kernel_size=(4, 4),

                  activation='relu', input_shape=(256, 256, 3)),

    layers.MaxPooling2D(pool_size=(2, 2)),

    

    layers.Conv2D(filters=32, kernel_size=(3, 3),

                  activation='relu'),

    layers.MaxPooling2D(pool_size=(2, 2)),

    layers.Flatten(),

    layers.Dense(60,activation='relu'),

    layers.Dense(3,activation='softmax')

]

)

model.compile(

    optimizer='adam',

    loss='sparse_categorical_crossentropy',

    metrics=['accuracy']​

model.fit(X_train_scaled,y_train,epochs=10)​


Model Evaluation : 


model.evaluate(X_test_scaled,y_test)


Classification Report : 


y_pred = list()

for i in model.predict(X_test_scaled):

  y_pred.append(np.argmax(i))

y_pred = np.array(y_pred)

class 0 :Potato___Early_blight
class 1 :Potato___Late_blight
class 2 :Potato___healthy


print(classification_report(y_test,y_pred))


Confusion Matrix :


class_names = [0,1,2]

fig,ax = plt.subplots()

tick_marks = np.arange(len(class_names))

plt.xticks(tick_marks,class_names)

plt.yticks(tick_marks,class_names)

cnf_matrix = confusion_matrix(y_test,y_pred)

sns.heatmap(pd.DataFrame(cnf_matrix), annot = True, cmap = 'YlGnBu',

                fmt = 'g')

ax.xaxis.set_label_position('top')

plt.tight_layout()

plt.ylabel('Actual label')

plt.xlabel('Predicted label')

plt.show()


Thank You ):


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