Mango leaf disease diagnosis using deep learning.pptx

Mango leaf diasease diagnosis using deep learning techniques

1. Development of Deep Learning based automated system for Mango Leaf Disease Diagnosis and Classification Presented by: Sana Saeed

2. Introduction Mango cultivation plays a vital role in agriculture and global economy. However, mango crops are susceptible to various diseases that significantly reduce quality and quantity if not diagnosed and treated properly. This research highlight the importance of agriculture and the impact of diseases on mango plants, emphasizing the need for early detection and classification of diseases in mango plant leaves . This research propose deep learning based approach Convolution Neural Network (CNN) that extract deep image qualities and improve classification performance. .

3. Deep Neural Network for identification of mango leaf diseases Literature Review Proposed Methodology Mango disease dataset Rescaling and center alignment Contrast enhancement Residual Neural Network

4. CNN Architecture Convolutional+ReLU pooling Convolutional+ReLU pooling

5. Research Questions Which machine learning algorithms are most effective for mango leaf disease detection? 01 What is the computational resource and processing time required for the proposed model to diagnose mango leaf disease, and how can efficiency be improved? What is the impact of different image preprocessing techniques e.g.resizing , and data augmentation on the accuracy of mango leaf disease classification? 02 03

6. Continued What are the key challenges encountered in classification of mango leaf diseases in existing literature? Which statistical algorithms and techniques can be effectively employed to optimize precision and recall in the detection of mango leaf diseases ? 04 05

7. Objectives To analyze the most effective algorithm and their parameter settings, that can enhance the accuracy of detection system, reduce false positive. To reduce the consumption of computational resources and processing time while maintaining the accuracy of the system. To evaluate the influence of image preprocessing techniques on enhancing the performance of classifying mango leaf diseases. The primary objective of this study is :