Browsing by Author "Alam, Rabiul"

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Employing a Convolutional Neural Network Technique to Identify Plant Diseases through Machine Learning
(Daffodil International University, 2023-07-15) Alam, Rabiul
The agriculture industry is essential to both economic growth and food security. However, farmers face enormous obstacles as a result of the antiquated and ineffective management of plant diseases in agriculture, particularly in nations like Bangladesh. We provide a novel approach to resolve this problem by utilizing a Convolutional Neural Network (CNN) technology to recognize plant diseases using machine learning. Our suggested CNN model has extraordinary capability in precisely identifying and diagnosing plant illnesses from visual signs. We offer fast and accurate information to farmers for efficient disease control through the integration of machine learning algorithms. In order to improve model performance, the study technique entails gathering a varied collection of plant photos that includes images of various illnesses. The outcomes show how well our suggested strategy works to help farmers. We allow proactive actions like targeted treatments and preventative tactics by providing customers with an AI-based system for identifying plant diseases, thereby decreasing crop losses and maintaining the healthy growth of vegetable crops. The adoption of our suggested remedy would also have wider socioeconomic effects. By raising agricultural production, we support greater food security, higher farmer incomes, and the development of a resilient agricultural environment. Furthermore, the use of cutting-edge AI technology has the potential to inspire younger generations to pursue careers in agriculture, rejuvenating the industry with new ideas and viewpoints. With an attained accuracy of over 74.50% on the testing set, the results of our trials show a promising degree of accuracy. This shows how well the suggested CNN model performs at correctly recognizing and categorizing plant diseases. The model's capacity to identify distinguishing traits from plant photos and provide precise predictions is credited with the high accuracy.
Productivity and quality enhancement through implementation of Total Productive Maintenance (TPM) in a manufacturing plant
(IUT, MCE, 2016-11-20) Rashid, Md. Mynur; Alam, Rabiul
Plant and equipment maintenance is a vital issue in manufacturing. The cost of regular maintenance might be low but the major breakdown of a machine can halt the entire production line. On the other hand effective machine maintenance would enable a plant to increase productivity, efficiency, quality operations, various flexibility, and responsive to customer service. Most importantly, this may prolong the life of machines. In order to measure performance of a maintenance system, the overall equipment effectiveness (OEE) is a summarized metric of several performance measures used to determine a machine’s or plant’s performance and its impact on productivity. The purpose of OEE is to assist an organization with improved productivity, reliability, maintainability for both short term and long term effectiveness, and low cost yet quality product at the best value. To evaluate the OEE, research framework has been designed through interview-for initial assessment, observation of shop floor, collecting breakdown and repair data and analyzing those with statistical tools. After the documentation and analysis of data, findings are represented in graphical forms such as Pareto chart, cause effect diagram, and process flow diagram. Very little research is conducted about Total Productive Maintenance (TPM) implementation Bangladeshi manufacturing plant; so this thesis going to add value to that cause. Bangladesh spends significant portion of foreign currency to import industrial machineries. Understanding the reasons of machine breakdowns, failures, quality defects, reduced speed, setup and adjustment losses are vital for plants. Actual capacity of production line can be enhanced by understanding the big losses and evaluating OEE. Otherwise to meet the extended demand of a product, a plant has to spend more money on buying new equipment. Many Japanese and the USA manufacturing plant incorporated TPM few decades ago but this practice seems to be absent here. Practical implication of this study is that manufacturing plants needs to improve its maintenance policy.