Automated farming prediction
Cataloged from PDF version of thesis report.
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BRAC University
2017
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| Dostęp online: | http://hdl.handle.net/10361/7626 |
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10361-76262022-01-26T10:08:23Z Automated farming prediction Siddique, Talha Barua, Dipro Ferdous, Zannatul Chakrabarty, Dr. Amitabha Department of Computer Science and Engineering, BRAC University Multiple Linear Regression Analysis (MLR) Prediction KNNR Android application Fertilizer suggestion Dependent variable Independent variables Cataloged from PDF version of thesis report. Includes bibliographical references (page 65-66). This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016. Farming in Bangladesh is mostly done manually. The automated way of farming here is still not introduced. This research is trying to apply a fundamental approach to inaugurate the automated process in farming in our country. It is an automated farming system designed in android application, which has been implemented to choose the best crop before starting the cultivation process according to the area of the cultivating land. Here, the best crop signifies the crop which will be the most cost effective for that particular land. In this case, the six major crops of Bangladesh – Aus, Aman, Boro, Potato, Wheat and Jute will be considered. This system is also able to prepare a schedule of total cultivation process e.g. the correct time of fertilization and irrigation according to the kind of crop types. The total system is focused on the climate and geographical condition of different areas of Bangladesh. It predicts the best cost effective crop using a prediction based algorithm. The algorithm are aimed to use is multiple linear regression with the association of some independent variables i.e. rainfall, average maximum temperature and average minimum temperature of certain location and give prediction based on yield rate per unit area. Later, KNNR algorithm was used to compare the accuracy and error rate of the predicted yield rate. To describe the functionality of this system; at first, farmer gives the perimeter of land in input area and the district from dropdown menu if he wants the suggestion of best crop. Then best crop name will be shown in the screen. If the suggestive crop is chosen, the entire steps of cultivation will be shown to him. Then the notification of irrigation, fertilization will be shown up timely or in a calendar form. The crop zone is divided according to the division and districts. The data of crops of total seven regions – Bogra, Comilla, Dinajpur, Sylhet, Dhaka, Barisal, Faridpur, Khulna, Rajshahi and Rangpur will be stored in database system. The dataset consists of information on six major crops of Bangladesh; their yield rate, maximum temperature, minimum temperature, year range, region and rainfall. The past twelve years (2000-2011) of Bangladesh have been considered making this dataset to ensure learning and training of the algorithm and increasing the accuracy rate of the prediction and for testing we used three years (2012-2014) for computing accuracy. Talha Siddique Dipro Barua Zannatul Ferdous B. Computer Science and Engineering 2017-01-19T10:06:03Z 2017-01-19T10:06:03Z 2016 12/14/2016 Thesis ID 16241001 ID 12201029 ID 12101045 http://hdl.handle.net/10361/7626 en BRAC University thesis 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. 66 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Multiple Linear Regression Analysis (MLR) Prediction KNNR Android application Fertilizer suggestion Dependent variable Independent variables |
| spellingShingle |
Multiple Linear Regression Analysis (MLR) Prediction KNNR Android application Fertilizer suggestion Dependent variable Independent variables Siddique, Talha Barua, Dipro Ferdous, Zannatul Automated farming prediction |
| description |
Cataloged from PDF version of thesis report. |
| author2 |
Chakrabarty, Dr. Amitabha |
| author_facet |
Chakrabarty, Dr. Amitabha Siddique, Talha Barua, Dipro Ferdous, Zannatul |
| format |
Thesis |
| author |
Siddique, Talha Barua, Dipro Ferdous, Zannatul |
| author_sort |
Siddique, Talha |
| title |
Automated farming prediction |
| title_short |
Automated farming prediction |
| title_full |
Automated farming prediction |
| title_fullStr |
Automated farming prediction |
| title_full_unstemmed |
Automated farming prediction |
| title_sort |
automated farming prediction |
| publisher |
BRAC University |
| publishDate |
2017 |
| url |
http://hdl.handle.net/10361/7626 |
| work_keys_str_mv |
AT siddiquetalha automatedfarmingprediction AT baruadipro automatedfarmingprediction AT ferdouszannatul automatedfarmingprediction |
| _version_ |
1814307532589498368 |