Enhancing crops production based on environmental status using machine learning techniques

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.

Détails bibliographiques
Auteurs principaux:	Talukder, Shiyam, Jannat, Habiba, Saha, Sukanta, Sengupta, Katha
Autres auteurs:	Hossain, Muhammad Iqbal
Format:	Thèse
Langue:	English
Publié:	Brac University 2021
Sujets:	Agricultural productivity KNearest neighbor Collaborative filtering Fuzzy K-Nearest neighbor Machine learning
Accès en ligne:	http://hdl.handle.net/10361/14731

id	10361-14731
record_format	dspace
spelling	10361-147312022-01-26T10:08:21Z Enhancing crops production based on environmental status using machine learning techniques Talukder, Shiyam Jannat, Habiba Saha, Sukanta Sengupta, Katha Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Agricultural productivity KNearest neighbor Collaborative filtering Fuzzy K-Nearest neighbor Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 32-33). Bangladesh is an agricultural country. As the economy is based on agriculture highly, there should be progress in this sector. To make progress in agriculture the productivity must be increased. These days, productivity is low due to various factors. One of them is not nding suitable crops for a particular land. In this way, the crops are not produced at the maximum amount. Hence, productivity of agriculture depends on multiple parameters on the basis of location. The suitable crop for a particular location is necessary for agriculture to bring the most productivity. Here we have designed a model that predicts productivity with given parameters, and also recommends the suitable crop based on those parameters. In terms of Machine Learning for the prediction and the recommendation, we have applied multiple algorithms like k-nearest neighbor, support vector machines, random forest, na ve Bayes' classi er and logistic regression, collaborative ltering and fuzzy K-Nearest neighbor. After training the dataset and applying algorithms, for prediction we have made a comparison by analyzing the precision. On the other hand, for recommendation we have used collaborative ltering system and fuzzy k-nearest neighbor. These algorithms are mainly used to take users data as input and test with the trained data that is already in the system and will lter out the best 5 crops as output. Shiyam Talukder Habiba Jannat Sukanta Saha Katha Sengupta B. Computer Science 2021-07-03T19:03:39Z 2021-07-03T19:03:39Z 2020 2020-04 Thesis ID 16101243 ID 16101191 ID 20141019 ID 16101280 http://hdl.handle.net/10361/14731 en Brac University theses 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. 33 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Agricultural productivity KNearest neighbor Collaborative filtering Fuzzy K-Nearest neighbor Machine learning
spellingShingle	Agricultural productivity KNearest neighbor Collaborative filtering Fuzzy K-Nearest neighbor Machine learning Talukder, Shiyam Jannat, Habiba Saha, Sukanta Sengupta, Katha Enhancing crops production based on environmental status using machine learning techniques
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Talukder, Shiyam Jannat, Habiba Saha, Sukanta Sengupta, Katha
format	Thesis
author	Talukder, Shiyam Jannat, Habiba Saha, Sukanta Sengupta, Katha
author_sort	Talukder, Shiyam
title	Enhancing crops production based on environmental status using machine learning techniques
title_short	Enhancing crops production based on environmental status using machine learning techniques
title_full	Enhancing crops production based on environmental status using machine learning techniques
title_fullStr	Enhancing crops production based on environmental status using machine learning techniques
title_full_unstemmed	Enhancing crops production based on environmental status using machine learning techniques
title_sort	enhancing crops production based on environmental status using machine learning techniques
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/14731
work_keys_str_mv	AT talukdershiyam enhancingcropsproductionbasedonenvironmentalstatususingmachinelearningtechniques AT jannathabiba enhancingcropsproductionbasedonenvironmentalstatususingmachinelearningtechniques AT sahasukanta enhancingcropsproductionbasedonenvironmentalstatususingmachinelearningtechniques AT senguptakatha enhancingcropsproductionbasedonenvironmentalstatususingmachinelearningtechniques
_version_	1814307418719387648

Enhancing crops production based on environmental status using machine learning techniques

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