![References
[1] S. S. Sannakki and V. S. Rajpurohit,” Classification of Pomegranate Diseases Based on Back
Propagation Neural Network,” International Research Journal of Engineering and Technology
(IRJET), Vol2 Issue: 02 | May-2015
[2] P. R. Rothe and R. V. Kshirsagar,” Cotton Leaf Disease Identification using Pattern
Recognition Techniques”, International Conference on Pervasive Computing (ICPC),2015.
[3] Aakanksha Rastogi, Ritika Arora and Shanu Sharma,” Leaf Disease Detection and Grading
using Computer Vision Technology &Fuzzy Logic” 2nd International Conference on Signal
Processing and Integrated Networks (SPIN)2015.
[4] Godliver Owomugisha, John A. Quinn, Ernest Mwebaze and James Lwasa,” Automated
Vision-Based Diagnosis of Banana Bacterial Wilt Disease and Black Sigatoka Disease “,
Preceding of the 1’st international conference on the use of mobile ICT in Africa ,2014.
[5]’Detection of Plant Leaf Disease Using Image Processing’ Approach ,Sushil R. Kamlapurkar
Department of Electronics & Telecommunications, Karmaveer Kakasaheb Wagh Institute of
Engineering Education & Research, Nashik, India sushilrkamlapurkar@gmail.com](https://image.slidesharecdn.com/finalppt-250725042039-67159214/85/plant-leaf-desease-detection-using-machine-learning-30-320.jpg)
plant leaf desease detection using machine learning
- 1. GOVERNMENT ENGINEERING COLLEGE KUSHALNAGAR DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING PROJECT PRESENTATION ON: “PLANT LEAF DISEASES DETECTION ” PRESENTED BY: MONISHA RAVI 4GL20CS014 NISHA P J 4GL20CS017 JEEVAN K D 4GL21CS405 ANIL KUMAR C B 4GL21CS416 UNDER THE GUIDANCE OF Dr. MAHENDRA G B.E,M. tech,PhD Dept. Of CS & E, GEC KUSHALNAGAR.
- 2. CONTENTS Abstract Introduction Literature survey Problem Statement Objectives Existing system Disadvantages Advantages Proposed System System requirements Design and Analysis Result Snap Shot Conclusion References
- 3. Abstract Due to various seasonal condition crops get affected by various kind of diseases . The plant disease detection can be done by observing spot of the leaf of the affected plant. The method we are adopting to detect the plant leaf disease using image processing,using convolution neural network. The django base web application, we used traine convolution neural network to identify disease present in leaf it consist of 41 classes of different healthy and diseased plant leaves.
- 4. Introduction Traditionally identification of plant disease has relied on human annotation by visual inspection and the agriculture production cost can be significantly increased. Plant disease has long been on of the major threats to food security because it dramatically reduces the crop yield and quantity of the crop. Hence in order to solve this problem we have developed the artificial intelligence based solution and the speed are the to main factor that will decide success of the automatic plant leaf disease detection and classification model.
- 5. Literature survey title of the paper Author Publication year Out Come Classification of pomegranate diseases based on back propagation neural neytwork S.S.Sannakki and V.S Raj purohit 2020 It mainly works on method of segment the defected area and colour and texture use the future Cotton leaf disease identification using pattern recognition technics P.R Rothee 2017 Active contour model use to limit vitality insert the infection ,BPNN classifier tsekles the numerous.
- 6. title of the paper Author Publication year Out Come Recognising the plant leaf the using classifier KNN Iftekhar Hossain et al. 2019 In this paper the system KNN classifier classified the diseases commonly found in plant like bacterial blite early blite of various plant species Plant leaf detection and disease recognition using deep learning Sammy V.militante 2019 In this paper author used nine different variety of leaf diseases of tomoto grapes corn apple and sugar cane
- 7. Problem Statement Agriculture is the backbone of the Indian economy. The massive commercialization of agriculture has the very negative effect on our environment. plant leaf disease detection using deep learning involves addressing the challenge of accurately and efficiently identifying diseases affecting plant leaves through the analysis of images. This includes developing robust deep learning models capable of detecting multiple types of diseases across various plant species with high accuracy and reliability.
- 8. Objectives The objective of this project is to detect and differentiate between healthy and infected leaves. This project is based on deep learning and image processing techniques. Data set contains images of cucumber leaves with different types of disease and also some healthy leaf images to train our system to efficiently detect healthy and infected diseases.
- 9. Existing system • In developing countries, farming land can be much larger and farmers cannot observe each and every plant, every day. Farmers are unaware of non-native diseases. Consultation of experts for this might be time consuming & costly. • Also unnecessary use of pesticides might be dangerous for natural resources such as water, soil, air, food chain etc. as well as it is expected that there need to be less contamination of food products with pesticides
- 10. Disadvantages • Farmers cannot afford so much money for persons who visit the crop for disease prediction. • Speed and accuracy of getting result is delayed. • As the cultivational fields are quite large and have very large number of plants in that, hence it becomes very difficult for the human eye to properly detect and classify each and every plant.
- 11. Advantages • Farmer can predict the diseases so that can use the right cultivation and fertilizers method. So that they can improve the product quality and crop yield prediction. • Based on our proposed system we achieved the best model for prediction of diseases in variety of crops.
- 12. Proposed System • We proposed a model to detect and classify the infected plant leaves consists of 4 phases. The phases are • Dataset Collection • Image Preprocessing • Segmentation • Selection of Classifier
- 13. System requirements Software requirements • Python programming language • Visual studio code editor • Django framework for web application Hardware requirements • Hard disk: 1Tb • Ram : 4GB • Processor: intel i3 • GPU : 2GB
- 15. Image collection • In this project we collected data's of various crops. • The data’s undergoes different process to identify the defects in it. • data collection
- 16. Image processing • In this step images are resized to smaller pixel size in order to speed up the computation. • The noise is removed using some filter technique like gaussain blur. • After the images are present in RGB format which is not appropriate for further work as RGB format does not separate image instantly.
- 17. segmentation In this step ,segmentation of image is done in order to separate the leaves from the background. segmentation is performed using k-means clustering with 2 cluster center. Segmentation process is dividing image in to small segments to identify the disease. Image after k-means clustering
- 18. segmentation
- 19. Selection of Classifier • This is the classification problem as we have to classify the type of disease on the leaf of the plant. So, we have plenty of machine learning as well as deep learning algorithms that we can apply on this dataset.
- 20. python •Python is an interpreted high-level •programming language for general-purpose programming. •In python, OpenCV is to be installed. •‘Open source computer vision library' initiated some enthusiast coders in ‘ 1999' to incorporate Image •Processing into a wide variety of coding languages. It has C++, C and Python interfaces running on Windows, Linux, Android, and Mac.
- 21. Python server connecting data from data base
- 22. Image captured from data base
- 23. Result Data base collected from different websites Captured image is uploaded to the python server with the help of visual studio code Image undergoes various image processing algorithum to determine the disease The determined disease is sent to the interface to show the output
- 24. Home Page
- 25. Login Page
- 26. SNAP SHOT Detected plant Leaf desease
- 27. Remedies
- 28. Tamato Accuracy Table shows Accuracy of two diseases Disease name TR FR Accuracy Bacterial spot 20 1 95 Early blight 20 0 100 Bar graph result accuracy
- 29. Conclusion • Plant growth and health play a significant role in the economy in courtesies like India, where a huge sector of the population is dependent on agriculture. Due to many reasons like droughts, floods. Improper rain and other, a large part of crops got wasted. Since we can’t control the natural factors we have to take proper care of our agriculture to minimize the losses and hence the plant health issue can’t be ignored. In conclusion, the project demonstrated the effectiveness of deep learning techniques in detecting plant leaf diseases.
- 30. References [1] S. S. Sannakki and V. S. Rajpurohit,” Classification of Pomegranate Diseases Based on Back Propagation Neural Network,” International Research Journal of Engineering and Technology (IRJET), Vol2 Issue: 02 | May-2015 [2] P. R. Rothe and R. V. Kshirsagar,” Cotton Leaf Disease Identification using Pattern Recognition Techniques”, International Conference on Pervasive Computing (ICPC),2015. [3] Aakanksha Rastogi, Ritika Arora and Shanu Sharma,” Leaf Disease Detection and Grading using Computer Vision Technology &Fuzzy Logic” 2nd International Conference on Signal Processing and Integrated Networks (SPIN)2015. [4] Godliver Owomugisha, John A. Quinn, Ernest Mwebaze and James Lwasa,” Automated Vision-Based Diagnosis of Banana Bacterial Wilt Disease and Black Sigatoka Disease “, Preceding of the 1’st international conference on the use of mobile ICT in Africa ,2014. [5]’Detection of Plant Leaf Disease Using Image Processing’ Approach ,Sushil R. Kamlapurkar Department of Electronics & Telecommunications, Karmaveer Kakasaheb Wagh Institute of Engineering Education & Research, Nashik, India sushilrkamlapurkar@gmail.com