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    [Solved] leaf disease detection using keras

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    • S
      sreu13 @arunksoman last edited by

      @arunksoman
      i'll follow this proceedure, but would I be able to deploy this code in raspberry pi 4?

      A 1 Reply Last reply Reply Quote 0
      • A
        arunksoman @sreu13 last edited by

        @sreu13 As the @SuperGops says you have to use fit_transform. It can be implemented on Raspberry Pi 4. But if it slows down your pi you have use multiprocessing as well as threads to improve that.

        S 1 Reply Last reply Reply Quote 0
        • S
          sreu13 @arunksoman last edited by

          @arunksoman
          by multiprocessing, do you mean to use multilabel binarizer, if yes , then I have already used it during the training process.
          @SuperGops , fit_transform has also been used in the code prior to using label_binarizer.

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          • A
            arunksoman @sreu13 last edited by

            @sreu13 I didn't mean that. I said if you are trying to run your code on RasPi4 or any other version of RasPi, you have to do some optimization on the code for better performance. Then Multiprocessing and threading Module comes into the picture.

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            • S
              sreu13 @arunksoman last edited by

              @arunksoman
              i followed your proceedure to and installed the requirements.txt file
              after running the code, I got the following error

              File "C:\Users\admin\Anaconda3\envs\test2\lib\site-packages\scipy\special\basic.py", line 15, in <module>
              from ._ufuncs import (ellipkm1, mathieu_a, mathieu_b, iv, jv, gamma,

              ImportError: cannot import name 'ellipkm1'

              A 1 Reply Last reply Reply Quote 0
              • A
                arunksoman @sreu13 last edited by arunksoman

                @sreu13 I didn't tell you to install anything via anaconda package manager. You have to uninstall those things first and install python 3.6.5. It was the first step.You have to read things carefully before executing anything.

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                • salmanfaris
                  salmanfaris last edited by

                  @sreu13 how is the progress?

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                  • S
                    sreu13 @salmanfaris last edited by

                    @salmanfaris i know there was a delay in replies, i thought of finishing the project and replying here!....
                    i ddnt get the exact reason, but the problem was solved

                    what i did was , label binarized was working fine when i did created the training model. So i loaded my training model and deleted the script for the training procedure. i ended up with the code given below. and it worked up fine.

                    import numpy as np
                    import pickle
                    import cv2
                    from os import listdir
                    from sklearn.preprocessing import LabelBinarizer
                    from keras.models import Sequential
                    from keras.layers.normalization import BatchNormalization
                    from keras.layers.convolutional import Conv2D
                    from keras.layers.convolutional import MaxPooling2D
                    from keras.layers.core import Activation, Flatten, Dropout, Dense
                    from keras import backend as K
                    from keras.preprocessing.image import ImageDataGenerator
                    from keras.optimizers import Adam
                    from keras.preprocessing import image
                    from keras.preprocessing.image import img_to_array
                    from sklearn.preprocessing import MultiLabelBinarizer
                    from sklearn.model_selection import train_test_split
                    import matplotlib.pyplot as plt
                    
                    EPOCHS = 25
                    INIT_LR = 1e-3
                    BS = 32
                    default_image_size = tuple((256, 256))
                    image_size = 0
                    directory_root = 'PlantVillage'
                    width=256
                    height=256
                    depth=3
                    
                    #Function to convert images to array
                    def convert_image_to_array(image_dir):
                        try:
                            image = cv2.imread(image_dir)
                            if image is not None :
                                image = cv2.resize(image, default_image_size)   
                                return img_to_array(image)
                            else :
                                return np.array([])
                        except Exception as e:
                            print(f"Error : {e}")
                            return None
                    
                    listdir(directory_root)
                    
                    image_list, label_list = [], []
                    try:
                        print("[INFO] Loading images ...")
                        root_dir = listdir(directory_root)
                        for directory in root_dir :
                            # remove .DS_Store from list
                            if directory == ".DS_Store" :
                                root_dir.remove(directory)
                    
                        for plant_folder in root_dir :
                            plant_disease_folder_list = listdir(f"{directory_root}/{plant_folder}")
                            
                            for disease_folder in plant_disease_folder_list :
                                # remove .DS_Store from list
                                if disease_folder == ".DS_Store" :
                                    plant_disease_folder_list.remove(disease_folder)
                    
                            for plant_disease_folder in plant_disease_folder_list:
                                print(f"[INFO] Processing {plant_disease_folder} ...")
                                plant_disease_image_list = listdir(f"{directory_root}/{plant_folder}/{plant_disease_folder}")
                               
                                    
                                for single_plant_disease_image in plant_disease_image_list :
                                    if single_plant_disease_image == ".DS_Store" :
                                        plant_disease_image_list.remove(single_plant_disease_image)
                    
                                for image in plant_disease_image_list[:200]:
                                    image_directory = f"{directory_root}/{plant_folder}/{plant_disease_folder}/{image}"
                                    if image_directory.endswith(".jpg") == True or image_directory.endswith(".JPG") == True:
                                        image_list.append(convert_image_to_array(image_directory))
                                        label_list.append(plant_disease_folder)
                        print("[INFO] Image loading completed")  
                    except Exception as e:
                        print(f"Error : {e}")
                    
                    image_size = len(image_list)
                    
                    #Transform Image Labels uisng Scikit Learn's LabelBinarizer
                    label_binarizer = LabelBinarizer()
                    image_labels = label_binarizer.fit_transform(label_list)
                    pickle.dump(label_binarizer,open('label_transform.pkl', 'wb'))
                    n_classes = len(label_binarizer.classes_)
                    
                    #Print the classes
                    print(label_binarizer.classes_)
                    
                    #load saved pickle model
                    loaded_model = pickle.load(open('cnn_model.pkl', 'rb'))
                    model_disease=loaded_model
                    
                    
                    #load plant leaf image
                    image_dir="plantdisease/Validation_Set/Potato___Early_blight/1d301622-e359-49d5-b4ca-6837f254fd1b___RS_Early.B 6719.JPG"
                    
                    #convert leaf image to arrays
                    im=convert_image_to_array(image_dir)
                    np_image_li = np.array(im, dtype=np.float16) / 225.0
                    npp_image = np.expand_dims(np_image_li, axis=0)
                    
                    result=model_disease.predict(npp_image)
                    print(result)
                    
                    #printing result
                    itemindex = np.where(result==np.max(result))
                    print("probability:"+str(np.max(result))+"\n"+label_binarizer.classes_[itemindex[1][0]])
                    
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