Browsing by Author "Noor, Sheak Rashed Haider"

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A Comprehensive Cotton Leaf Disease Dataset for Enhanced Detection and Classification
(Elsevier, 2024-09-10) Bishshash, Prayma; Nirob, Asraful Sharker; Sarower, Afjal Hossan; Bhuiyan, Touhid; Noor, Sheak Rashed Haider
The creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management. This dataset enables the development of accurate machine learning models for early disease detection, reducing manual inspections and facilitating timely interventions. It serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. This leads to targeted interventions, reduced chemical use, and improved crop management. Global collaboration is fostered, contributing to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. Field surveys conducted from October 2023 to January 2024 ensured meticulous image capture under diverse conditions. The images are categorized into eight classes, representing specific disease manifestations, pests, or environmental stress in cotton plants. The dataset comprises 2137 original images and 7000 augmented images, enhancing deep learning model training. The Inception V3 model demonstrated high performance, with an overall accuracy of 96.03 %. This underscores the dataset's potential in advancing automated disease detection in cotton agriculture.
A Novel Automated Feature Selection Based Approach To Recognize Cauliflower Disease
(Institute of Electrical and Electronics Engineers Inc., 2023-04-17) Shakil, Rashiduzzaman; Akter, Bonna; Shamrat, F M Javed Mehedi; Noor, Sheak Rashed Haider
Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).