Article Per Year (5 Year)
p-Index From 2020 - 2025
1.235
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Public Health Science (IJPHS) International Journal of Integrated Health Sciences Journal of Maternal and Child Health Journal of Public Health and Pharmacy
Rahman, Md Mizanur
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Health Professions Medicine & Pharmacology Public Health

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Automated tomato leaf disease recognition using deep convolutional networks Sohel, Amir; Rahman, Md Mizanur; Hasan, Md Umaid; Islam, MD Kafiul; Rukhsara, Lamia; Rabeya, Tapasy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1850-1860

Abstract

Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This study aims to utilize a deep learning approach to accurately detect and classify damaged leaves and disease patterns in tomato leaf images. By employing a substantial quantity of deep convolutional network models, we achieved a high level of precision in diagnosing the condition. The dataset used in our study work is a self-contained dataset obtained by direct observation of tomato fields in rural areas of Bangladesh. It consists of four classes: healthy, black mold, grey mold, and powdery mildew. In this study work, we utilized various image pre-processing techniques and applied VGG16, InceptionV3, DenseNet121, and AlexNet models. Our results showed that the DenseNet121 model attained the higher accuracy of 97%. This discovery guarantees accurate detection of tomato diseases in a rapid manner, ushering in a new agricultural revolution.
Cucumber leaf disease identification in real-time via deep learning based algorithms Rahman, Md Mizanur; Nadim, Mahimul Islam; Akther, Mahinur; Ullah, Ahad; Ahmed, Jakaria; Ahmed, Muhammad Jalal Uddin; Jahan, Israt
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3127-3138

Abstract

Cucumber is a cash crop in Bangladesh as it is a side dish grown commercially in cultivable lands year-round. The early prediction of disease-prone crops could save grooming time and minimize losses. The conventional method of examining leaves just through observation of the human eye could only detect the diseases at an advanced stage without a concrete decision of which disease it might be and regular inspection is labour intensive, inaccurate and often unreliable. This study evaluates machine learning-based image analysis for classifying healthy and diseased cucumber leaves by training deep learning models to detect and identify observable traits. Total 1,629 images use as primary dataset and all the data collected from the cucumber field of Bangladesh. To fulfill this purpose, convolutional neural network (CNN), InceptionV3, and EfficientNetB4 are the models implemented in this paper to improve the classification of objects. The dataset was optimized by pre-processing techniques and the leaves are classified into four categories, namely angular leaf spot, downy mildew, powdery mildew, and good leaf. The EfficienNetB4 model achieved the highest train and test accuracy respectively 95% and 87%. A comparative examination of the available models was conducted in this paper to reach a solid decision.
Co-Authors Adenan, Abg Safuan Ahmad, Sk Akhtar Ahmed, Jakaria Ahmed, Muhammad Jalal Uddin Ajeng , Rudy Ngau Ajeng, Rudy Ngau Akther, Mahinur Aren, Merikan Bin Chen, Yoke Yong Daud, Muhammad Siddiq Gahamat , Mohd Faiz Gahamat, Mohd Faiz Hasan, Md Umaid Islam, Md Kafiul Jahan, Israt Kana, Kamarudin Bin Lukas, Sabrina Binti Mizan, Sharmin Muhammad , Siddiq Nadim, Mahimul Islam Puteh, Sharifa Ezat Wan Rabeya, Tapasy Rukhsara, Lamia Safii, Razitasham Safii, Razitasham binti Saimon, Rosalia Sohel, Amir Ting, Chuong Hock Ullah, Ahad