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(searched for: doi:10.3126/ajn.v3i0.9013)
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, Dinesh Panday,
Published: 15 October 2021
The Soils of India pp 15-27; https://doi.org/10.1007/978-3-030-80999-7_3

The publisher has not yet granted permission to display this abstract.
Published: 13 June 2021
by MDPI
AgriEngineering, Volume 3, pp 403-422; https://doi.org/10.3390/agriengineering3020027

Abstract:
Jute is the golden fiber of Bangladesh, but its production is declining due to the involvement of higher production and processing costs, where a major portion of the cost is needed for fiber extraction. Labor unavailability and increasing labor cost have led to higher jute fiber production cost. To address these issues, this study looks at the development of a power-operated and cost-effective fiber extraction machine aiming at reducing the production cost. The study was conducted at the Rangpur regional office premises of Practical Action in Bangladesh, and the developed machine was branded as “Aashkol”, which had the following major parts: a feeding tray, a primary extraction roller, a secondary extraction roller, grabbing rollers, fiber collection stand, base frame, protection cover, and a spring-loaded tray under the primary extraction roller. The Aashkol can extract green ribbon from the jute stem, but jute sticks were broken down into smaller pieces (3–6 cm). The performance evaluation of the machine was conducted using different types of jute (Deshi, Kenaf, and Tossa) and compared with another jute extraction machine (KP model, introduced by Karupannya Rangpur Ltd.). The Aashkol-based extraction and improved retting systems were also evaluated and compared with traditional jute extraction systems. The jute stem input capacity (4.99 t h−1) of the Aashkol was 47.6% higher than the KP model (3.38 t h−1). Compared with the traditional system, across jute types, the Aashkol produced a 9% higher fiber yield and saved 46% retting time. Overall, the Aashkol reduced 90% of the labor requirement and saved 11.6 USD t−1 in jute fiber extraction and retting than the traditional method.
Australian Journal of Engineering and Innovative Technology pp 7-15; https://doi.org/10.34104/ajeit.020.07015

Abstract:
As Bangladesh is an agricultural country, the economy, as well as the food security of this country, mostly depends on the production level of different crops over the year. Therefore, there exists immense pressure on exaggerated crop production due to the fast growth of the population. But, the average production level is being hampered by the bad nature of the weather. We have conducted a survey on near about 100 farmers of two northern districts of Bangladesh: Pabna and Rajshahi and assessed the impact of rough nature on production. According to farmers and agriculturalists, it is noticed that rough weather causes about 30% to 70% production shortage than expectation with all other factors remaining constant. In this study, we have adopted Human-computer interaction (HCI) based approach (Soft System Methodology-SSM) to this aspect for efficacious collaboration with root-level farmers and agricultural trainers providing ease for understanding weather-related issues on the production of crops. Finally, some machine learning algorithms were also implemented on the obtained dataset to accurately classify the range of production level of rice and a comparison is made among the algorithms based on performance metrics. Moreover, an android based application is created to depict the summary of the study.
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