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Smart agricultural system for crop monitoring and soil analysis

dc.contributor.advisorMohsin, Dr. Abu S.M.
dc.contributor.authorDas, Bristy
dc.contributor.authorUl Hoq Sayor, Tahmid Zarif
dc.contributor.authorNijhum, Rubyat Jahan
dc.contributor.authorTishun, Mehnaz Tabassum
dc.contributor.departmentDepartment of Electrical and Electronic Engineering
dc.date.accessioned2024-02-19T06:03:40Z
dc.date.available2024-02-19T06:03:40Z
dc.date.copyright2022
dc.date.issued2022-12
dc.descriptionCataloged from PDF version of final year design project.
dc.descriptionIncludes bibliographical references (pages 115-116).
dc.descriptionThis final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2022.en_US
dc.description.abstractAgriculture is the base of the economy in Bangladesh, however, 90% of the farmers are not familiar with modern-technological tools. That is the reason why we see very-little usage of modern tools in farming in our country which limits crop production significantly. Hence to address this, a smart monitoring system is proposed to introduce farming robots to bring a strong revolution to the existing primitive systems. The proposed system will include a range of sensors, including cameras and probes, to detect ripe vegetables and harvest them, as well as analyze soil conditions and provide advice to farmers. The system will use image processing techniques and an NPK sensor. The primary objectives of the project are to reduce labor requirements and improve the efficiency of agricultural production. Advancements in technology will enable modern farms and agricultural businesses to utilize cutting-edge tools, robotic systems, and precision farming methods to become more successful, productive, safe, and environment friendly. Automated navigation and harvesting, crop growth monitoring, and crop disease detection can be achieved through the use of sensors, robotic arms, and machine learning algorithms, respectively. However, these advancements require sufficient data to train the machine learning models, making them a long-term goal for the future.en_US
dc.description.degreeB. Electrical and Electronic Engineering
dc.description.statementofresponsibilityBristy Das
dc.description.statementofresponsibilityTahmid Zarif Ul Hoq Sayor
dc.description.statementofresponsibilityRubyat Jahan Nijhum
dc.description.statementofresponsibilityMehnaz Tabassum Tishun
dc.format.extent140 pages
dc.identifier.otherID: 19121123
dc.identifier.otherID: 19121015
dc.identifier.otherID: 19121111
dc.identifier.otherID: 19121024
dc.identifier.urihttp://hdl.handle.net/10361/22439
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectRoveren_US
dc.subjectNPK sensoren_US
dc.subjectInverse kinematicsen_US
dc.subjectRobotic armen_US
dc.subjectHarvestingen_US
dc.subjectSoil analysisen_US
dc.subjectTomatoen_US
dc.subjectImage processingen_US
dc.subjectRipeen_US
dc.subjectNavigationen_US
dc.subject.lcshAgricultural innovations.
dc.subject.lcshAgriculture--Automation.
dc.titleSmart agricultural system for crop monitoring and soil analysisen_US
dc.typeProject Reporten_US

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