Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices
Agriculture plays a key role in global food security. Agriculture is critical to global food security and economic development. Precision farming using machine learning (ML) and the Internet of Things (IoT) is a promising approach to increasing crop productivity and optimizing resource use. This pap...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/13/11/2141 |
| _version_ | 1797460554062757888 |
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| author | Christine Musanase Anthony Vodacek Damien Hanyurwimfura Alfred Uwitonze Innocent Kabandana |
| author_facet | Christine Musanase Anthony Vodacek Damien Hanyurwimfura Alfred Uwitonze Innocent Kabandana |
| author_sort | Christine Musanase |
| collection | DOAJ |
| description | Agriculture plays a key role in global food security. Agriculture is critical to global food security and economic development. Precision farming using machine learning (ML) and the Internet of Things (IoT) is a promising approach to increasing crop productivity and optimizing resource use. This paper presents an integrated crop and fertilizer recommendation system aimed at optimizing agricultural practices in Rwanda. The system is built on two predictive models: a machine learning model for crop recommendations and a rule-based fertilization recommendation model. The crop recommendation system is based on a neural network model trained on a dataset of major Rwandan crops and their key growth parameters such as nitrogen, phosphorus, potassium levels, and soil pH. The fertilizer recommendation system uses a rule-based approach to provide personalized fertilizer recommendations based on pre-compiled tables. The proposed prediction model achieves 97% accuracy. The study makes a significant contribution to the field of precision agriculture by providing decision support tools that combine artificial intelligence and domain knowledge. |
| first_indexed | 2024-03-09T17:06:40Z |
| format | Article |
| id | doaj.art-af1cd8103bf340a1a65d6b4e1df0a167 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T17:06:40Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-af1cd8103bf340a1a65d6b4e1df0a1672023-11-24T14:23:22ZengMDPI AGAgriculture2077-04722023-11-011311214110.3390/agriculture13112141Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming PracticesChristine Musanase0Anthony Vodacek1Damien Hanyurwimfura2Alfred Uwitonze3Innocent Kabandana4African Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, RwandaChester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USAAfrican Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, RwandaAfrican Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, RwandaAfrican Centre of Excellence in Internet of Things, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, RwandaAgriculture plays a key role in global food security. Agriculture is critical to global food security and economic development. Precision farming using machine learning (ML) and the Internet of Things (IoT) is a promising approach to increasing crop productivity and optimizing resource use. This paper presents an integrated crop and fertilizer recommendation system aimed at optimizing agricultural practices in Rwanda. The system is built on two predictive models: a machine learning model for crop recommendations and a rule-based fertilization recommendation model. The crop recommendation system is based on a neural network model trained on a dataset of major Rwandan crops and their key growth parameters such as nitrogen, phosphorus, potassium levels, and soil pH. The fertilizer recommendation system uses a rule-based approach to provide personalized fertilizer recommendations based on pre-compiled tables. The proposed prediction model achieves 97% accuracy. The study makes a significant contribution to the field of precision agriculture by providing decision support tools that combine artificial intelligence and domain knowledge.https://www.mdpi.com/2077-0472/13/11/2141precision agricultureInternet of Thingsartificial intelligencecrop recommendationfertilizer recommendation |
| spellingShingle | Christine Musanase Anthony Vodacek Damien Hanyurwimfura Alfred Uwitonze Innocent Kabandana Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices Agriculture precision agriculture Internet of Things artificial intelligence crop recommendation fertilizer recommendation |
| title | Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices |
| title_full | Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices |
| title_fullStr | Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices |
| title_full_unstemmed | Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices |
| title_short | Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices |
| title_sort | data driven analysis and machine learning based crop and fertilizer recommendation system for revolutionizing farming practices |
| topic | precision agriculture Internet of Things artificial intelligence crop recommendation fertilizer recommendation |
| url | https://www.mdpi.com/2077-0472/13/11/2141 |
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