Plant And Soil Nutrient Monitoring In Precision Agriculture: A Review Of IoT And Machine Learning
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866901664687980544 |
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| author | Priyanka Mehta Dr. Mona Shah |
| author_facet | Priyanka Mehta Dr. Mona Shah |
| contents | The paper provides a comprehensive review of modern technologies in Agriculture —particularly the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) and how they are transforming the agricultural sector. It also focuses on the issues faced in the current traditional farming methods and technologies like climate variations, reduced soil fertility, water scarcity, and the lack of real-time data for decision-making. The review explains the use of technology like IoT devices such as Sensors for soil, drones, remote-sensing satellites, and weather stations, which are used for continuous monitoring and data collection for soil nutrients, pH, moisture, temperature, and crop health. When the data is processed through ML algorithms like Random Forest, SVM, XGBoost, and ANN, it enables accurate crop prediction, fertilizer recommendation, yield forecasting, disease detection, and soil quality assessment, etc. The paper also intends to provide brief about the important parameters of soil like NPK, Texture, PH level, Moisture level, CEC, organic carbon and micronutrients. It also explains previous research which mentioned use of IoT based sensing, spectroscopy, image processing, etc. how they are useful to monitor these indicators. By a detailed literature review, this paper shows intends to explain how multisensory data fusion and ML-driven decision systems can result in providing faster cheaper and accurate analysis for precision agriculture in comparison to conventional laboratory methods. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17950545 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Plant And Soil Nutrient Monitoring In Precision Agriculture: A Review Of IoT And Machine Learning Priyanka Mehta Dr. Mona Shah The paper provides a comprehensive review of modern technologies in Agriculture —particularly the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) and how they are transforming the agricultural sector. It also focuses on the issues faced in the current traditional farming methods and technologies like climate variations, reduced soil fertility, water scarcity, and the lack of real-time data for decision-making. The review explains the use of technology like IoT devices such as Sensors for soil, drones, remote-sensing satellites, and weather stations, which are used for continuous monitoring and data collection for soil nutrients, pH, moisture, temperature, and crop health. When the data is processed through ML algorithms like Random Forest, SVM, XGBoost, and ANN, it enables accurate crop prediction, fertilizer recommendation, yield forecasting, disease detection, and soil quality assessment, etc. The paper also intends to provide brief about the important parameters of soil like NPK, Texture, PH level, Moisture level, CEC, organic carbon and micronutrients. It also explains previous research which mentioned use of IoT based sensing, spectroscopy, image processing, etc. how they are useful to monitor these indicators. By a detailed literature review, this paper shows intends to explain how multisensory data fusion and ML-driven decision systems can result in providing faster cheaper and accurate analysis for precision agriculture in comparison to conventional laboratory methods. |
| title | Plant And Soil Nutrient Monitoring In Precision Agriculture: A Review Of IoT And Machine Learning |
| url | https://doi.org/10.5281/zenodo.17950545 |