Browsing by Author "Sheikh, Md. Helal"

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Deep Learning Based Sponge Gourd Diseases Recognition for Commercial Cultivation in Bangladesh
(International Conference on Artificial Intelligence & Industrial Applications, Springer, 2020-09-02) Mim, Tahmina Tashrif; Sheikh, Md. Helal; Chowdhury, Sadia; Akter, Roksana; Khan, Md. Abbas Ali; Habib, Md. Tarek
Sponge gourd, called as “ Open image in new window ” in Bangladesh, is scientifically known as Luffa cylindrical that belongs to Cucurbitaceae family. Sponge gourd or luffa gourd is one of the most easily found vegetable in Bangladesh. It is an edible wild vegetable 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 sponge gourd 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 images as input and after examining them healthy or detected diseases will be shown as output which has diffrent true and false values for different diseases and reached to the average accuracy of 81.52%.
Detection of Maize and Peach Leaf Diseases Using Image Processing
(10th International Conference on Computing, Communication and Networking Technologies, IEEE, 2019-12-30) Sheikh, Md. Helal; Mim, Tahmina Tashrif; Reza, Md. Shamim; Rabby, AKM Shahariar Azad; Akhter, Syed
There are thousands of grains and fruits grown on this planet every day. In order to cope with the needs of the people, new farming practices are constantly being invented. In addition, the discovery and use of new technologies, which can easily and quickly detect plant diseases. In this research, we tried to detect the diseases and provided their remedies through the images of the infected leaf of a grain “Corn” along with a fruit “Peach” with the help of computer science by using Artificial Intelligence. Since corn is a very popular crop and food, it is cultivated throughout the world. In particular, the demand and popularity of corn in Bangladesh is a skyscraper. Even though peach is not as much popular as corn in Bangladesh but in recent time it is gaining attraction of people in the fruit market. But due to timely consultation and availability of available technology, maize farmers and the farmers who are interested in cultivating peach professionally, often face the attack of insects and diseases. As soon as the crop gets destroyed, the farmers' dividends also get spoiled which is a huge loss for all. So, this research will predict the diseases of corn leaf as well as peach plant leaf and pass down their remedy soon after the affected leaf's picture is given as an input. In this research we have used image processing, convolutional neural network (CNN) algorithm to train our dataset. In the end, our system successfully achieved validation accuracy of over 99.28%. This research is going to help numerous farmers all over the world especially the farmers of our country Bangladesh to increase the production rate of corn and the popularity rate of peach by reducing the attack of insects and diseases in time.
Leaf Diseases Detection for Commercial Cultivation of Obsolete Fruit in Bangladesh Using Image Processing System
(Proceedings of the 2019 8th International Conference on System Modeling and Advancement in Research Trends, SMART 2019, IEEE, 2020-06-16) Sheikh, Md. Helal; Mim, Tahmina Tashrif; Reza, Md. Shamim; Hena, Most. Hasna
Bangladesh is country of seasons. Naturally the people of Bangladesh are blessed with various native seasonal fruits to fulfill their needs and appetites. So, hundreds of local fruits are cultivated through the yearlong by the local farmers. But in recent times some obsolete fruits like apple, blueberry, cherry, grape, orange, peach, raspberry, strawberry etc. are getting popularity amongst local consumers of Bangladesh and that is the reason why local farmers are now contemplating towards the commercial cultivation of these obsolete fruits. But there are too many obstacles in that process of commercial cultivation including weather demand, quality of soil, cultivation technology, diseases, insects attack etc. Most of the time diseases of these fruits notice our eyes when fruits are attacked and we cannot help but let the fruits get rotten and suffer the financial loss too. Even though we might not handle every barrier, but we can use deep learning and image processing technology for the detection of diseases of these fruits and help the farmers taking necessary steps on time. To simplify the work, we have taken the images of these obsolete fruits leaves and implemented image processing algorithm and deep learning methods on them. After the completion of this research we have accomplished an accuracy of 92.56%. This research is going to help the farmers to cultivate and promote these obsolete fruits more in a broaden way by reducing the diseases. It is an eco-friendly system which detects diseases without much effort only by clicking attacked images and putting that in the system and the system will give an output of which disease the plant is attacked by.
Leaves Diseases Detection of Tomato Using Image Processing
(Proceedings of the 2019 8th International Conference on System Modeling and Advancement in Research Trends, SMART 2019, IEEE, 2020-06) Mim, Tahmina Tashrif; Sheikh, Md. Helal; Shampa, Roksana Akter; Reza, Md. Shamim; Islam, Md. Sanzidul
Today's era is an era of Scientific Development. Where, technologies and new ways of solving real-life problems are being invented every day. With the increasing population of the world, basic need of food is increasing parallelly. That's why agriculture plays an important role all over the world. Throughout the year different crops, vegetables, fruits, fishes, animals are cultivated to fulfill the need of people as well as to gain profit for the people involving in those cultivation. But due to lack of proper cultivating knowledge, experience and sense of disease prediction, sometimes those cultivating crops and grains get damaged partially or even completely. Of course, that ends up with a huge loss for the farmers as well as for the economic growth of the country. So, this research paper tends to merge or combine a part of agricultural sector with science and technology to reduce the loss caused by insect's attack and diseases of plant leaves. More specifically, this research happens to combine agricultural sector with computer science. Since, agriculture is a vast sector to work on, to simplify the work, we are detecting vegetable plant diseases using Artificial Intelligence and computer science. To implement this idea, we have chosen “Tomato” as the core vegetable which's leaf diseases are to be predicted by using the algorithms of Artificial Intelligence, CNN and computer science. Tomato is a very popular vegetable in our country as well as in the world, the main motive is to solve the diseases detection problems that the “Tomato” growers are facing nowadays in their cultivable land especially in Bangladesh. And that is why we have chosen tomatoes leaf diseases prediction which is very important. This research tried to eradicate the harmful side effects of chemicals and pesticides with the help of Image Processing system. In this research 6 classification of tomato leaves disease have been detected including one healthy class. The farmers can input the symptom...
Mango Species Detection from Raw Leaves Using Image Processing System
(Springer, 2021) Hena, Most. Hasna; Sheikh, Md. Helal; Reza, Md. Shamim; Marouf, Ahmed Al
Mango is the national tree of Bangladesh which is one of the most popular fruits here during the hot summer enriching the highest quality of nutrition. Various species of mango cover the fruit market making the summer festivities. In recent times, different species of mango are also being exported to different countries of the world. So more and more people are entering into the commercial mango cultivation nowadays as new farmers. It is necessary for them to know which mango species they are cultivating and what is the market demand of that species. It is hard for the new farmers to find out the species just by asking and trusting the sapling seller. So, we plan to establish a system that can accurately ensure the species of the mango sapling. This research used convolutional neural network (CNN) and deep learning for training the dataset. This method can showcase the species of the mango sapling only by observing the image of a leaf holding an accuracy of 78.65%.
Plant Leaf Diseases Detection Using Image Processing System
(Daffodil International University, 2019-12) Sheikh, Md. Helal; Reza, Md. Shamim; Fatema, Kaniz
Bangladesh is country of seasons. Naturally the people of Bangladesh are blessed with various native seasonal fruits to fulfill their needs and appetites. So, hundreds of local fruits are cultivated through the yearlong by the local farmers. But in recent times some obsolete fruits like apple, blueberry, cherry, grape, orange, peach, raspberry, strawberry etc. are getting popularity amongst local consumers of Bangladesh and that is the reason why local farmers are now contemplating towards the commercial cultivation of these obsolete fruits. But there are too many obstacles in that process of commercial cultivation including weather demand, quality of soil, cultivation technology, diseases, insects attack etc. Most of the time diseases of these fruits notice our eyes when fruits are attacked and we cannot help but let the fruits get rotten and suffer the financial loss too. Even though we might not handle every barrier, but we can use deep learning and image processing technology for the detection of diseases of these fruits and help the farmers taking necessary steps on time. To simplify the work, we have taken the images of these obsolete fruits leaves and implemented image processing algorithm and deep learning methods on them. After the completion of this research we have accomplished an accuracy of 92.56%. This research is going to help the farmers to cultivate and promote these obsolete fruits more in a broaden way by reducing the diseases. It is an eco-friendly system which detects diseases without much effort only by clicking attacked images and putting that in the system and the system will give an output of which disease the plant is attacked by.