Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations
Abstract Agriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditi...
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| Format: | Article |
| Language: | English |
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Springer Nature
2024-09-01
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| Series: | Human-Centric Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s44230-024-00081-3 |
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| author | Farida Siddiqi Prity MD. Mehadi Hasan Shakhawat Hossain Saif Md. Maruf Hossain Sazzad Hossain Bhuiyan Md. Ariful Islam Md Tousif Hasan Lavlu |
| author_facet | Farida Siddiqi Prity MD. Mehadi Hasan Shakhawat Hossain Saif Md. Maruf Hossain Sazzad Hossain Bhuiyan Md. Ariful Islam Md Tousif Hasan Lavlu |
| author_sort | Farida Siddiqi Prity |
| collection | DOAJ |
| description | Abstract Agriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural productivity, food security, and economic prosperity. |
| format | Article |
| id | doaj-art-b6b9ea356cf04fa8965cdd2519de1ae6 |
| institution | Kabale University |
| issn | 2667-1336 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj-art-b6b9ea356cf04fa8965cdd2519de1ae62025-01-12T12:26:37ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362024-09-014449751010.1007/s44230-024-00081-3Enhancing Agricultural Productivity: A Machine Learning Approach to Crop RecommendationsFarida Siddiqi Prity0MD. Mehadi Hasan1Shakhawat Hossain Saif2Md. Maruf Hossain3Sazzad Hossain Bhuiyan4Md. Ariful Islam5Md Tousif Hasan Lavlu6Department of Computer Science and Engineering, Shanto-Mariam University of Creative TechnologyDepartment of Computer Science and Engineering, Shanto-Mariam University of Creative TechnologyDepartment of Computer Science and Engineering, Shanto-Mariam University of Creative TechnologyDepartment of Computer Science and Engineering, Shanto-Mariam University of Creative TechnologyDepartment of Computer Science and Engineering, Shanto-Mariam University of Creative TechnologyDepartment of Computer Science and Engineering, Shanto-Mariam University of Creative TechnologyDepartment of Computer Science and Engineering, Shanto-Mariam University of Creative TechnologyAbstract Agriculture constitutes the foundational pillar of the global economy, engaging a substantial segment of the workforce and making a considerable contribution to the Gross Domestic Product (GDP). However, agricultural productivity faces numerous challenges, including varying climatic conditions, soil types, and limited access to modern farming practices. Developing intelligent agricultural systems becomes imperative to address these challenges and enhance agricultural productivity. Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. The proposed system utilizes historical data on climatic conditions, soil properties, crop yields, and farmer preferences to provide personalized crop recommendations. The goal of this study is to appraise the efficacy of nine distinct ML models—Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Bagging (BG), AdaBoost (AB), Gradient Boosting (GB), and Extra Trees (ET) to generate practical recommendations for crop selection. Numerous preprocessing methods are employed to cleanse and normalize the data, thereby ensuring its appropriateness for model training. The ML models are trained using historical data sets, including temperature, rainfall, humidity, soil pH, and nutrient levels, where crop yields are correlated with environmental and agronomic factors. The models undergo fine-tuning through methods such as cross-validation to enhance their performance and ensure robustness. Among those models, Radom Forest has achieved the highest accuracy (99.31%). The proposed Machine Learning-based crop recommendation system offers a promising approach to addressing the challenges faced by the farmers. By leveraging advanced data analytics and artificial intelligence techniques, the system empowers farmers with timely and personalized recommendations, ultimately leading to improved agricultural productivity, food security, and economic prosperity.https://doi.org/10.1007/s44230-024-00081-3Crop recommendationSoilMachine learningClimateDecision tree |
| spellingShingle | Farida Siddiqi Prity MD. Mehadi Hasan Shakhawat Hossain Saif Md. Maruf Hossain Sazzad Hossain Bhuiyan Md. Ariful Islam Md Tousif Hasan Lavlu Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations Human-Centric Intelligent Systems Crop recommendation Soil Machine learning Climate Decision tree |
| title | Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations |
| title_full | Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations |
| title_fullStr | Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations |
| title_full_unstemmed | Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations |
| title_short | Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations |
| title_sort | enhancing agricultural productivity a machine learning approach to crop recommendations |
| topic | Crop recommendation Soil Machine learning Climate Decision tree |
| url | https://doi.org/10.1007/s44230-024-00081-3 |
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