Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

TL;DR

This paper presents image processing techniques, specifically histogram comparison and k-means clustering, to detect citrus leaf canker disease by analyzing subtle pattern changes on leaf surfaces, aiding in early diagnosis and treatment.

Contribution

It introduces a combined approach using histogram comparison and k-means clustering for detecting citrus leaf canker disease from digital images.

Findings

Effective detection of citrus leaf canker using histogram analysis.

K-means clustering successfully segmented diseased areas.

Potential for aiding agricultural disease management.

Abstract

The detection of leaf diseases in plants generally involves visual observation of patterns appearing on the leaf surface. However, there are many diseases that are distinguished based on very subtle changes in these visually observable patterns. This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease. Canker is a bacterial infection of leaves. Symptoms of citrus cankers include brown spots on the leaves, often with a watery or oily appearance. The spots (called lesions in botany) are usually yellow. It is surrounded by a halo of the leaves and is found on both the top and bottom of the leaf. This paper describes various methods that have been used to detect citrus leaf canker disease. The methods used are histogram comparison and k-means clustering. Using these methods, citrus canker development was detected based on histograms generated based on leaf patterns. The results thus obtained can be used, after consultation with experts in the field of agriculture, to identify suitable treatments for the processes used.