  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.
