Abstract:
Sponge gourd or luffa gourd, called as “ধুন্দলু” in Bangladesh, is scientifically known as
Luffa cylindrical that belongs to Cucurbitaceae family. It is one of the most easily found
vegetables in Bangladesh. It is edible wild vegetables that the plant can be seen anywhere
around us during late summer till late autumn in Bangladesh. Cooked sponge gourd or
the curry is a bit sweetish in taste. Even though sponge gourd is kind of a wild vegetable
plant, in recent time a lot of people are cultivating it in the countryside thinking of profit
and commercial production since there is a market demand for it. Despite of having every
opportunity to commercial benefit most of the farmers neglect the issue of insects and
diseases attack on the plant resulting on huge loss in the business. Also lack of proper
knowledge of related diseases, advance technology and trustable source the farmers lag
behind the diseases detection process to use pesticides or different methods of reducing
diseases attack. If necessary steps can be taken to prevent the insects and diseases attack
at the very beginning of cultivation, then the profits will increase as the crop yields
increase. This research paper attempts to detect the leaf and flower diseases of
spongegourd using Convolutional Neural Network (CNN) and image processing
techniques. CNN and image processing are one of the most recently introduced
technologies using in the agriculture sector in Bangladesh ensuring highest accuracy.
This system will take leaf and flower images as input and after examining them healthy
or detected diseases will be shown as output which has reached to the accuracy of
66.63%.
