Empirical based irrigation model using predicted soil moisture for durian plantation
It is vital to the agricultural activities to have sufficient water supply for its operation and maintenance mainly for cultivation to keep it in good condition Therefore, it is important to determine the soil moisture levels existing while designing a precise irrigation system. Installing soil mois...
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| Main Authors: | , , , , , |
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| Format: | Book Section |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/100730/ http://dx.doi.org/10.1007/978-981-19-3923-5_23 |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | It is vital to the agricultural activities to have sufficient water supply for its operation and maintenance mainly for cultivation to keep it in good condition Therefore, it is important to determine the soil moisture levels existing while designing a precise irrigation system. Installing soil moisture sensors in each tree is complicated or excessively expensive. Forecasting the value using climate data is a viable solution in this scenario. Climate data are used to forecast soil moisture and then utilized in this irrigation model. This study uses an Artificial Neural Network (ANN) to forecast soil moisture values. The statistical method is used to determine the predicted values’ correctness. After the process, the irrigation volume and schedule are calculated based on the most accurate prediction findings. |
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