SEGMENTATION OF UNHEALTHY REGION OF PLANT LEAF USING IMAGE PROCESSING TECHNIQUES: A SURVEY
---
paper_title: IMAGE SEGMENTATION BY FUZZY C-MEANS CLUSTERING ALGORITHM WITH A NOVEL PENALTY TERM

paper_content:

To overcome the noise sensitiveness of conventional fuzzy c-means (FCM) clustering algorithm, a novel extended FCM algorithm for image segmentation is presented in this paper. The algorithm is developed by modifying the objective function of the standard FCM algorithm with a penalty term that takes into account the influence of the neighboring pixels on the centre pixels. The penalty term acts as a regularizer in this algorithm, which is inspired from the neighborhood expectation maximization algorithm and is modified in order to satisfy the criterion of the FCM algorithm. The performance of our algorithm is discussed and compared to those of many derivatives of FCM algorithm. Experimental results on segmentation of synthetic and real images demonstrate that the proposed algorithm is effective and robust.
---

Title: SEGMENTATION OF UNHEALTHY REGION OF PLANT LEAF USING IMAGE PROCESSING TECHNIQUES: A SURVEY

Section 1: INTRODUCTION
Description 1: Provide an overview of the importance of plant leaf disease detection in agriculture and the challenges of detecting diseases manually. Mention the use of image processing for automatic detection and identification of leaf diseases.

Section 2: LITERATURE SURVEY
Description 2: Summarize existing research and methodologies for detecting and classifying plant leaf diseases using various image processing techniques.

Section 3: SEGMENTATION TECHNIQUES
Description 3: Discuss the concept of segmentation, its importance, and the general principles and approaches used in image segmentation.

Section 4: CLUSTERING
Description 4: Explain the concept of clustering in image segmentation and the different clustering techniques used for segmenting infected areas of plant leaves.

Section 5: K-Means Clustering
Description 5: Describe the K-Means clustering algorithm, its procedure, advantages, and disadvantages.

Section 6: Fuzzy C-Means Clustering
Description 6: Explain the Fuzzy C-Means clustering algorithm, its approach to dealing with vagueness and imprecision, and how it is used in image segmentation.

Section 7: Penalized Fuzzy C-Means
Description 7: Discuss the Penalized Fuzzy C-Means algorithm, how it incorporates spatial information into the clustering process, and its objective function.

Section 8: Image Processing Method
Description 8: Outline the image processing method for calculating the infected area of a leaf by using pixel number statistics and subtracting green leaf area from the total leaf area.

Section 9: Unsupervised Fuzzy Clustering Algorithm
Description 9: Describe the Unsupervised Fuzzy Clustering algorithm, including its iterative process to determine the optimal number of clusters and cluster centers.

Section 10: CONCLUSION
Description 10: Summarize the survey findings, highlighting the effectiveness of different segmentation techniques and clustering algorithms for segmenting diseased parts of plant leaves.