Identification of the plant diseases is the key to prevent the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plants is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually.
Step 1: Image acquisition is the very first step that requires capturing an image
with the help of a digital camera.
Step 2: Pre-processing of input image to improve the quality of image and to
remove the undesired distortion from the image. Clipping of the leaf image is
performed to get the interested image region and then image smoothing is
done using the smoothing filter. To increase the contrast, Image enhancement
is also done.
Step 3: Mostly green colored pixels, in this step, are masked. In this, we
computed a threshold value that is used for these pixels. Then in the following
way mostly green pixels are masked: if pixel intensity of the green component
is less than the pre-computed threshold value, then zero value is assigned to
the red, green, and blue components of this pixel.
Step 4: In the infected clusters, inside the boundaries, remove the masked
cells.
Step 5: Obtain the useful segments to classify the leaf diseases. Segment the
components using the ML algorithm
