Plant leaf disease detection and classification

Work about leaf detection

1. FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning 1 CUI, Attock Real Time Grape Leaf Disease Detection And Classification Using Deep Learning Project Members 1 Ateeq Ur Rehman FA19-BCE-003 2 Zeeshan Mehmood FA19-BCE-015 3 Awais Mustafa FA19-BCE-025 Supervised By: Mr. Akbar Afridi Co-Supervised By Engr. Qazi Zia Ullah CE-03

2. Presentation Outline Introduction Motivation Objectives Literature Review Papers review Proposed System Model Initial Proposed Model Result & Discussion Software results FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 2 Conclusion & Future Work Future Work Miscellaneous Individual Teamwork UN Goals Timeline

3. Introduction Fruits are the primary need for every country. Plants infected by diseases Impacts country's agricultural production Economy Identification of leaf disease is hard. Leaf diseases detection is based on Computer system able to detect leaf disease. Easy to find out disease Detect the major plant scientifically Solution to disease FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 3

4. Motivation Diseases in leafs: Reduce growth, survivorship and reproduction Farmers do not know which disease it is. Time consuming Hard to diagnose without proper equipment Examination of automatic leaf disease detection in farms : Identify the plant with proper disease Time Saving Reliable Affordable The leaf disease detection in digital environment can help: Government Sector Agriculture research center Farmer Gardener FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 4

5. Objectives Detection of disease from a real time video Grape Leaf Disease detection To design such system that can detect crop disease accurately YOLO based object detection for grape leaf Mobile Application based system for leaf detection. Image transform to Recognition system System to convert the real time video to the disease detection. Predicting of leaf disease as soon it appears on plant leaves. 5500 leaf images Database collection Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 5

6. Research Questions How to train YOLO model ? Can it detect various types of grape leaf diseases in real-time? How reliable is the classification of grape leaf diseases? What is the performance of the YOLOv8 model for grape leaf disease detection? What are the benefits of using deep learning for grape leaf disease detection in farms? FYP- Real Time Grape Leaf Disease Detection and Classification Using Deep Learning CUI, Attock 6

7. Problem Statement In Fruit farming, humans are not always one hundred percent alert. A human eye cannot recognize every leaf disease. The symptoms of diseases are visible in different parts of a plant such as leaves, etc. Manual detection of grape disease using leaf images is a hard job Growing diseases cause affect on production of plant which is main source. Treating grape leaf problems: Leaf Blight Grape Black rot Esca (Black Measles) FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 7

8. Literature Review FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 8 Reference No Paper Title Algorithm Accuracy Dataset Training Testing [1] A Review of Machine Learning Approaches in Plant Leaf Disease Detection and Classification SVM classifier and K–Nearest Neighbor (KNN) 89.6% 95.9% 95.0% 4775 images 70% 78% 85% 30% 22% 15% [2] Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques Convolutional Neural Network CNN and Artificial Neural Network 94% 95% 20636 images 74% 26% [3] Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm Convolutional Neural Network(CNN) and Learning Vector Quantization (LVQ) 91.5% 4053 images 87% 13%

9. Literature Review FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 9 Reference No Paper Title Algorithm Accuracy Dataset Training Testing [4] Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms CNN Faster R-CNN 92.49% 96.76% 4980 images 90% 10% [5] State of Art Survey on Plant Leaf Disease Detection Yolo V3 CNN 95.6% 97.80% 5561 images 70% 30% [6] Performance of deep learning vs machine learning in plant leaf disease detection SVM Inception-v3 RF SGD 92.10% 95.89% 98.63% 96.81% 49441 image 90% 10%

10. Proposed System Model (Initial) FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 10

11. Limitations in Proposed System Model (If any) Use only Grape leaves for disease detection. Detect up to 3 diseases. Apply only Yolo model FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 11

12. Recommendations by FYP Committee Evaluators Use more than 5000 images Hardware deployment FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 12 Recommendations Addressed Evaluator 2: Sir Saad Zahid Evaluator 1 : Sir Babar Sattar

13. Mathematical Modeling FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 13

14. Dataset Collection FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 14

15. Dataset Labelling FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 15

16. Flow Chart FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 16

17. Updated System Model FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 17

18. Coding FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 18

19. Coding FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 19

20. Coding FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 20

21. Proposed Block Diagram FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 21

22. Proposed Schematic Diagram FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 22

23. Component Selection Android Mobile IOS Mobile Camera GPU (AMD Radeon RX ) Python Jupyter Notebook Colaboratory FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 23

24. Software and Hardware Implementation FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 24

25. HUB API Interference FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 25

26. Testing and Validation FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 26

27. Training Results FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 27

28. App Real Time Results FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 28

29. Short Video Demo FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 29

30. Conclusion System will detect disease in Real time. It can developed as practical tool for grape farms. It is based on the YOLOv8 model with high accuracy and real-time performance in detecting and classifying grape leaf diseases . Early detection and classification of diseases can lead to time saving. R educing reliance on manual inspection and enabling faster and more accurate identification I mproving crop health, increasing crops, and promoting sustainable agricultural practices. FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 30

31. Future Work Expansion of diseases of grape leaf Developing on user-friendly interface E asy to use for grape farmers. Exchange knowledge with experts about datasets, for the advancement of grape leaf disease detection and classification techniques . Addition with weather data, soil conditions, or historical disease patterns, to improve disease prediction for future. FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 31

32. Bibliography [1] Applalanaidu , M.V. and Kumaravelan , G., 2021, February. A review of machine learning approaches in plant leaf disease detection and classification. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 716-724). IEEE. [2] Jasim , Marwan Adnan, and Jamal Mustafa Al- Tuwaijari . "Plant leaf diseases detection and classification using image processing and deep learning techniques." 2020 International Conference on Computer Science and Software Engineering (CSASE). IEEE, 2020. [3] Sardogan , Melike , Adem Tuncer , and Yunus Ozen . "Plant leaf disease detection and classification based on CNN with LVQ algorithm." 2021 3rd international conference on computer science and engineering (UBMK). IEEE, 2021. [4] Ozguven , Mehmet Metin , and Kemal Adem . "Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms." Physica A: statistical mechanics and its applications 535 (2020): 122537.. [5] Sungheetha , Akey . "State of Art Survey on Plant Leaf Disease Detection." Journal of Innovative Image Processing 4.2 (2022): 93-102.. [6] Sujatha, Radhakrishnan , et al. "Performance of deep learning vs machine learning in plant leaf disease detection." Microprocessors and Microsystems 80 (2021): 103615.. FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 32

33. Impact on Society: UN Sustainable Goals FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 33

34. Modern Tool Usage FYP- Real Time Grape Leaf Disease Detection And Classification Using Deep Learning CUI, Attock 34