fruit disease predict vvvvvvvvvvvvvvvvv ppt-1.pptx

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1. Fruit disease prediction using DEEP LEARNING GUIDED BY: TEAM MEMBERS: Tamilarasan s(20ucs055) BharathKanth (20ucs058) Kathish R(20ucs099) Dr.A.Meenakshi Professor and Head of the Department, Department of CSE.

2. PROBLEM STATEMENT Fruit crops are important for feeding the world, but diseases can cause significant damage. Detecting diseases early is crucial for minimizing damage, but traditional detection methods are difficult and time-consuming. With Deep Learning, we can create a fast and accurate fruit disease detection system that can help farmers and agronomists quickly detect diseases, prevent their spread, and optimize crop management. This will improve crop productivity and profitability, and ensure food security for generations.

3. Objectives of the project To predict an accurate disease. To reduce the need for manual inspection . OBJECTIVES

4. HARDWARE INTERFACE REQUIREMENTS: Processor speed of 0.5 Ghz or more for mobile gadgets Processor speed of 1.5ghz or more for desktop and computer gadgets Ram of 500mb and above for all devices SOFTWARE ITERFACE REQUIREMENTS: Windows/ android/ Linux/ mac/ chrome or any other operating system Mozilla Firefox / Google chrome / opera mini / UC browser or internet explorer REQUIREMENT SPECIFICATION

5. Image acquisition Disease classification Feature Extraction User Interface MODULES

6. Those who wish to check the quality of fruits at the site have to register at the site as customer In the customer module customer can view the disease of fruit , prevention,fertilizer for disease . auction winning report, etc. This module would involve classifying the fruit images based on the type of disease present, which can help farmers and agricultural experts to take the appropriate management steps. LOGIN/REGISTER MODULE: CUSTOMER MODULE : DISEASE CLASSIFICATION MODULE MODULE DETAILS:

7. This module would involve identifying key features of the fruit, such as color, texture, and shape, that are important for disease detection. This module would involve developing a user-friendly interface for the fruit disease detection system, allowing users to easily upload images, view results, and access information about the detected diseases.. FEATURE EXTRCTION MODULE : USER INTERFACE MODULE:

8. RESULTS:

9. Identification of the most common fruit diseases affecting a particular type of fruit in a specific region or country.Understanding of the causes of fruit diseases, such as environmental factors, pathogens, or pests. Development of strategies to prevent or minimize the occurrence of fruit diseases, such as cultural practices, biological control, or chemical treatments. Evaluation of treatment options for fruit diseases, such as the efficacy and safety of different chemical treatments or biological control methods. Ultimately, a fruit disease understanding project can provide valuable insights into the biology and epidemiology of fruit diseases, as well as strategies to prevent and manage these diseases to ensure the continued production of high-quality fruit crops. CONCLUSION:

10. [ 1] S. B. Ullagaddi, Dr. S.Vishwanadha Raju, “A Review of techniques for Automatic detection and diagnose of mango Pathologies”. [2] Hadha Afrisal , Muhammad Faris, Guntur Utomo P, Lafiona Grezelda , Indah Soesanti , Mochammad Andri F, “Portable Smart Sorting and Grading Machine for Fruits Using Computer Vision”. REFERENCES:

11. THANK YOU