An artificial intelligence-enabled crop recommendation system
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2023
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10361-200142023-08-28T06:36:49Z An artificial intelligence-enabled crop recommendation system Uddin, Raiyan Barua, Mrinmoy Kabir, Mohammed Hossain Nufayel, Muhammed Sajid, Abu Sadman Alam, Dr. Md. Ashraful Department of Computer Science and Engineering, Brac University Nitrogen Phosphorus Potassium Sulfur Zinc Boron NPK SZB Machine learning. Artificial intelligence. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 49-51). Our country is overburdened with a population of more than 160 million. So fulfilling the need for food for the whole population can be overwhelming. This research has been conducted to ensure maximum efficiency in agriculture to overcome this problem. It is a known fact that Nitrogen (N), Phosphorus (P), and Potassium (K) are the three most essential micro-nutrients of any soil. Together it is called N.P.K. Soils also contain Sulfur (S), Zinc (Zn), and Boron (B). Together we can call it S.Zn.B., which are also important micro-nutrients. Different soils have different amounts of these essentials. Based on their values, an Automated system can suggest crops for a particular land to maximize production and profitability. We propose an AI-enabled crop recommendation system that will determine the best crops based on the soil type and its N.P.K and S, Zn, and B values through a Machine Learning Model. In our research, we use a comparative analysis among some existing Machine Learning Models to identify the most efficient model for our system. This system can effectively and accurately suggest the best suitable crops for a particular land. We used Random Forest which gave us 98%, Decision Tree which gave us 98%, Naive Bayes which gave us 89%, Ensemble Model which gave us 99% of accuracy and implemented Explainable-AI. As most farmers cannot select suitable crops for their land following their soil type, the agricultural sector is facing considerable losses. To minimize this, the efficiency of crop cultivation needs to be increased. Therefore, our system can revolutionize this sector by providing effective and suitable crops for land more accurately. Raiyan Uddin Mrinmoy Barua Mohammed Hossain Kabir Muhammed Nufayel Abu Sadman Sajid B. Computer Science and Engineering 2023-08-27T10:26:41Z 2023-08-27T10:26:41Z 2023 2023-01 Thesis ID: 18201172 ID: 18201208 ID: 19101099 ID: 19101341 ID: 19101528 http://hdl.handle.net/10361/20014 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. 51 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Nitrogen Phosphorus Potassium Sulfur Zinc Boron NPK SZB Machine learning. Artificial intelligence. |
| spellingShingle |
Nitrogen Phosphorus Potassium Sulfur Zinc Boron NPK SZB Machine learning. Artificial intelligence. Uddin, Raiyan Barua, Mrinmoy Kabir, Mohammed Hossain Nufayel, Muhammed Sajid, Abu Sadman An artificial intelligence-enabled crop recommendation system |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Alam, Dr. Md. Ashraful |
| author_facet |
Alam, Dr. Md. Ashraful Uddin, Raiyan Barua, Mrinmoy Kabir, Mohammed Hossain Nufayel, Muhammed Sajid, Abu Sadman |
| format |
Thesis |
| author |
Uddin, Raiyan Barua, Mrinmoy Kabir, Mohammed Hossain Nufayel, Muhammed Sajid, Abu Sadman |
| author_sort |
Uddin, Raiyan |
| title |
An artificial intelligence-enabled crop recommendation system |
| title_short |
An artificial intelligence-enabled crop recommendation system |
| title_full |
An artificial intelligence-enabled crop recommendation system |
| title_fullStr |
An artificial intelligence-enabled crop recommendation system |
| title_full_unstemmed |
An artificial intelligence-enabled crop recommendation system |
| title_sort |
artificial intelligence-enabled crop recommendation system |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/20014 |
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