Browsing by Author "Mamun, Md Al"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    PRECISION AGRICULTURE USING MACHINE LEARNING
    (Comilla University, 1-Feb-2025) Mia, Palash; Mamun, Md Al
    Agriculture is a key sector in Bangladesh, providing employment and sustenance to a significant portion of the population. However, many farmers struggle with selecting the most suitable crops for their land and applying the appropriate fertilizers, which leads to reduced productivity and economic losses. To address this challenge, precision agriculture offers a data-driven approach to improving decision-making in farming. Precision agriculture incorporates advanced techniques that analyze soil characteristics, soil types, and crop yield data to provide informed recommendations for farmers. By minimizing errors in crop and fertilizer selection, this method significantly enhances agricultural efficiency and output.This paper introduces a machine learning-based crop recommendation system that employs a majority voting technique, integrating algorithms such as Random Forest, Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression. These models are used to analyze site-specific parameters and predict the most suitable crop with high accuracy. Additionally, real-time testing is implemented through an IoT-based system. The fertilizer recommendation module is designed using Python logic, where the system compares user-input soil data with optimal nutrient values. The nutrient with the most substantial variation is classified as either HIGH or LOW, prompting tailored fertilizer suggestions
  • No Thumbnail Available
    Item
    Studies on the Effect of Low Glycemic Index for Multi-Whole Grain Formulated Flour Samples in Type 2 Diabetic Patients
    (2018-10-20) Hossain, Dr MD BELLAL; Inam, A K M Sarwar; Mamun, Md Al; Suzauddula, Md
    Different types of corns are used as staple food in Bangladesh, core source of nutrients from foodstuffs for metabolic energy. Ready to eat or processed food stuffs that contains totally different macro and micronutrients such as Tocopherol, Thiamine, Riboflavin, Pyroxene, Mg and Zn etc. within the recipes for sample preparation, were incorporated different whole grains of wheat, wheat bran, rye, maize, soya, barley, chickpeas and plantain husk in numerous ratios. Developed flours were subjected to nutritionally active diet for diabetic patients, the extent of glucose when consumption of diets could be an essential issue. Protein, fat, crude fiber and energy values of developed multi grains combined and market flours were MFS-1: (11.00%, 3.77%, 2.69%, 387.25); MFS-2:(14.16%, 3.71%,3.21%, 385.73); MFS-3:(12.40%, 3.33%, 2.87%,385.24); MFS-4: (11.31%, 2.16%, 2.45%, 382.83) and market flour samples CS-A: (12.04%, 2.05%,1.64%,358.37) and CS-B:(14.90%,2.06%,1.46%,350.82) respectively. Amongst, MFS-3 sample resulted the preferences in hedonic sensory evaluation. Glycemic index (GI) resolved mistreatment normal methodology for MFS-3 and normal sugar. The GI worth of MFS-3 sample (46.86) showed lowest postprandial aldohexose like compared to plain. once the analysis of all four mixed recipes compared to market samples, it showed that MFS-3 sample possessed the simplest preferences because the different useful diet for type-2 diabetic patients. Aims of the analysis works were recipe preparation of samples, product acceptances and determination of Glycemic Index (GI) for four mixed multi-wholegrain flours compared with 2 market multi-grain flours within the market.