Plant Disease Detection Using ML.pptx

: It is an End to End deep learning project to classify
disease in plants .I have built a web application in this project that can take a
picture of the plant and tell the farmer if the plant has a disease or not.

1. Plant Disease Detection Using Machine Learning A project Submitted in partial fulfillment of the requirements for the award of the Degree of MASTER OF COMPUTER APPLICATION By Guide : Prof. Manabendra Nath Kar Name of Student : KOUSHIK HAZRA Roll Num : 12021010010006 Name of Student : DIBAS KUMAR SHYAMAL Roll Num : 12021010010008 Name of Student : DEBODIP HAIT Roll Num : 12021010010050 Name of Student : SUBHA CHATTERJEE Roll Num :12021010010023 Name of Student : JINIYA MANDAL Roll Num : 12021010010035 Name of Student : ADREJA SAHA Roll Num : 12021010010030

2. TABLE OF CONTENTS INTRODUCTION PROPOSED METHODOLOGY IMAGE ACQUISITION DATASET PREPARATION IMAGE PREPROCESSING IMAGE SEGMENTATION FEATURE EXTRACTION TESTING & VALIDATION RESULT NECESSARY LIBRARIES FUTURE SCOPE CONCLUSION

3. INTRODUCTION In this project, we have created a convolutional neural network which will be able to predict whether a plant is suffering from a disease or not. We used different layers and other hyperperameters for building, training, and testing this classification model. We have used Tensorflow and Keras for this project.

4. PROPOSED METHODOLOGY Crops Image A cquisition Leaf image dataset Image pre-processing. Im age Segmentation Feature extraction Dataset splitting Training Set Validation Set Test Set Training & Validation datasets Training of Deep learning model Classification .

5. IMAGE ACQUISITION The first step in plant disease detection using machine learning is to acquire images of plant leaves that are both healthy and diseased. Images can be captured using cameras, smart phones, or drones, and can be taken in a controlled environment, such as a greenhouse, or in the field. It is important to ensure that the images are of high quality, with good resolution and color accuracy, to ensure accurate disease detection.

6. DATASET PREPARATION The Dataset collected from open source website “ Kaggle ”. Corn (Maize) – Common Rust Potato – Early Blight Tomato – Bacterial Spot

7. IMAGE PREPROCESSING Once the images are acquired, they need to be preprocessed to improve their quality and make them suitable for analysis. The goal of image preprocessing is to make the images more uniform and to remove any variability that could interfere with disease detection For normalize our dataset we will convert the images into a numpy array. Cropping Filtering Resizing  Normalization Image Preprocessed image

8. IMAGE SEGMENTATION After preprocessing, the images are segmented to separate the plant leaves from the background. Image segmentation involves dividing the image into multiple regions, each of which contains pixels with similar properties. This allows the machine learning algorithm to focus on the plant leaves and ignore the background, which can improve disease detection accuracy.

9. FEATURE EXTRACTION Once the plant leaves are segmented, the next step is to extract features from the image that can be used to train the machine learning model. These features might include color histograms, texture features, or shape descriptors.

10. DATASET SPLITTING The preprocessed images are labeled as healthy or diseased and used to create a dataset. The dataset is divided into training, validation, and testing sets. To split the dataset into testing and training data. Here we have taken test size as 0.2 so my data will be divided into 80% training and 20% testing data.

11. MODEL TRAINING Convolutional Neural Networks (CNNs) is chosen to train the model and extracted features to recognize the visual symptoms of plant diseases. We have used different types of layers according to their features namely Conv_2d (It is used to create a convolutional kernel that is convolved with the input layer to produce the output tensor ) max_pooling2d (It is a down sampling technique which takes out the maximum value over the window defined by poolsize ) flatten (It flattens the input and creates a 1D output) Dense (Dense layer produce the output as the dot product of input and kernel).

12. Convolution Neural Network: CNN

13. TESTING AND VALIDATION Fitting the model with the data and finding out the accuracy at each epoch to see how our model is learning. The trained model is then tested on a separate dataset of images to evaluate its accuracy in detecting plant diseases.

14. RESULT

15. FUTURE SCOPE OF PROJECT  Our project have shown pretty good accuracy, it can be implemented in real time mobile applications and web services, so that formers can identify diseases simply by taking photo of suspected leaves of plants . Other than plant leaf disease identification, it can also be used for identification and classification of nutrients deficiency of plant leaves .

16. THE STEPS TAKEN TO SOLVE THE PROBLEM We started with loading the dataset into googlecolab using Google drive and visualizing the images. Normalizing is an important step when working with any type of dataset. After that we created a CNN Model which is further used for predicting the plant diseases using the image supplied to model . This model is highly beneficial as it can be used by different agricultural firms and farmers to increase their yield and stop wastage of crops due to disease. CONCLUSION