Mohammad Ashik Iqbal Khan

Work place: Bangladesh Rice Research Institute, Gazipur, Bangladesh



Research Interests: Machine Learning


Mohammad Ashik Iqbal Khan is working as Principal Scientific Officer at Plant Pathology Division, Bangladesh Rice Research Institute (BRRI), Bangladesh. He obtained his PhD from Saga University, Japan. During his PhD, he served as Teaching Assistant for five years. Thereafter, while working at Japan International Research Center of Agricultural Science (JIRCAS) as a Post Doc. Fellow, he was able to apply many of the skills on rice blast disease resistance studies. He is the focal person of Blast Network Project in Bangladesh and maintaining collaborative research with Japan, Bangladesh, West Africa, China, Vietnam, Cambodia and Indonesia. He has served as a Research Supervisor of ten MS and five PhD students of different national and international university. He received many national and international awards for the recognition of his excellence. He served as Principal Investigator of 13 national and international projects. He has already published 62 scientific papers in national and international reputed journal and more than 50 seminar papers. He is involved in several e-learning and machine learning projects.

Author Articles
Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN

By Anam Islam Redoun Islam S. M. Rafizul Haque S.M. Mohidul Islam Mohammad Ashik Iqbal Khan

DOI:, Pub. Date: 8 Oct. 2021

Timely detection of rice diseases can help farmers to take necessary action and thus reducing the yield loss substantially. Automatic recognition of rice diseases from the rice leaf images using computer vision and machine learning can be beneficial over the manual method of disease recognition through visual inspection. During the recent years, deep learning, a very popular and efficient machine learning algorithm, has shown great promise in image classification task. In this paper, a segmentation-based method using deep neural network for classifying rice diseases from leaf images has been proposed. Disease-affected regions of the rice leaves have been segmented using local segmentation method and the Convolutional Neural Network (CNN) has been trained with those images. Proposed method has been applied on three different datasets including the one created by us which consists of the rice leaf images collected from Bangladesh Rice Research Institute (BRRI). Three state-of-the-art CNN architectures VGG, ResNet and DenseNet, used in the proposed method, have been trained with these three datasets for classifying the diseases. Classification performance of the proposed method using the said three CNN architectures for the three datasets have been analyzed and compared. These results show that this model is quite promising in classifying rice leaf diseases. Outcome of this research is an enhancement in the performance of rice disease classification which is quite significant for the viability of this work to be transformed into a real-time application for the farmers.

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