Abstract:
Cauliflower dominates a major share in terms of total winter cropping area and production
in Bangladesh. It has many health benefits like decrease the risk of obesity, diabetes, heart
disease etc. It is a cultivated and winter crop and has huge demand in the country. But if
proper care is not taken many serious disease will effects on plants and will reduce
productivity, quantity and quality of cauliflower. Manually monitoring of plant disease is
very difficult as it requires tremendous amount of work and excessive time. Automatic
recognition of disease through computer vision approach is becoming more popular day by
day. So in this paper we introduced a modern technique to recognize diseases that occur on
plants in cauliflower. The most common disease in cauliflower disease is Bacteria Soft
Rot, Black Rot, Buttoning, Downy mildew in Bangladesh. Our proposed solution would
support agriculture industry of Bangladesh to grow cauliflower more effectively and will
increase its production by taking proper step after automated recognize of these diseases.
In our work, for image segmentation, k-means clustering is used after image preprocessing
method is applied, ten relevant features are extracted. For classification, we compared
various classification technique. Random Forest algorithm achieves overall 81.68%
accuracy
