Early Plant Disease Detection and Recommendation Using ML CNN

Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. Almost 70% people depend on it & shares major part of the GDP. Diseases in crops mostly on the leaves effects on the reduction of both quality and quantity of agricultural products. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases & quickly diagnosis can be carried out as per disease. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertize in the plant diseases, and also require the excessive processing time. Hence, Leaf image processing is used for the detection of plant diseases. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification Using CNN (Convolutional Neural Net) of Machine Learning to Detect and Diagnose also It Provide Graphical User Interface which is not web based System which class Disease type and Recommendation System for Each Disease

Table of Contents

Approval Sheet

Table of Contents

CHAPTER ONE

1.1     Introduction

1.1.1       Image Definition

1.1.2       Identifying Patterns

1.2     Statement of the Problem

1.3     PLANT DISEASE FUNDAMENTALS

1.3.1       Bacterial disease

1.3.2       Viral Disease

1.3.3       Fungal Disease

1.4     Objective

1.4.1       General Objectives

1.4.2       Specific Objectives

1.5     Scope and Limitation

1.5.1       Scope

1.5.2       Limitations

1.6     Significance of the Project

CHAPTER ONE

1.7     Literature Review

1.7.1       Deep learning for Image-Based Plant detection

1.7.2       Detection and Classification of leaf disease using Artificial Neural Network

1.7.3       Plant disease detection using CNN and GAN

1.7.4       Convolutional Neural Network based Inception v3 Model for Plant Classification

1.8     Plant Disease Detection using CNN Model and Image Processing

1.8.1       F. On-device Image Processing Approaches

1.9     Methodology

1.10        Image Acquirement

1.10.1          Fundamental Image Processing

1.10.2          System Architecture

1.10.3          Fig 6 Proposed Convolutional Network

1.10.4          Optimization and Learning Rate

1.11        Image Expansion

1.12        Training the Model

1.13        G. Layer Visualization

1.13.1          REMEDY:

1.14        Requirements and Analysis:

1.15        Non-Functional Requirements:

1.16        BASIC ARCHITECTURE

1.17        Block Diagram of basic architecture

1.18        Conclusion:

1.18.1          Summary:

1.19        4.2 IMAGE ACQUISITION

1.20        4.3 IMAGE PREPROCESSING AND ENHANCEMENT

1.20.1          Fig 10 Image Processing Original Image

1.21        IMAGE SEGMENTATION

1.22        FEATURE EXTRACTION

1.23        4.1. Architecture Design:

1.23.1          Fig 12 Algorithm Flow chart of CNN Architecture

1.24        Interface Design:

The Activity Diagram:

4.3. Choice of Methods/Algorithms:

2        Convolutional neural network?

3        Applications of deection

4        There are many practical applications of computer vision:

5        CHAPTER FIVE

5.1     Implementation

5.3     Technology Used:

5.4     Training the Dataset:

5.6     Summary:

5.7     RESULT AND DISCUSSION

5.7.1       Result Analysis

5.8     CONCLUSION

5.8.1       Result Analysis of Different Model

6        References

7       Appendix