Decision support system for agricultural process management
This study was intended to help farmers in some farm processes such as irrigation, fertilizer application, and yield estimation. The decision support system (DSS) was responsible for determining when and how much irrigation to apply. The automated irrigation was simulated using the data from the yea...
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| Format: | text |
| Language: | English |
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Animo Repository
2009
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_masteral/3781 https://animorepository.dlsu.edu.ph/context/etd_masteral/article/10619/viewcontent/CDTG004586_P.pdf |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | This study was intended to help farmers in some farm processes such as irrigation, fertilizer application, and yield estimation. The decision support system (DSS) was responsible for determining when and how much irrigation to apply. The automated irrigation was simulated using the data from the year 2007. Results showed that the DSS worked efficiently at keeping the soil moisture above the predefined soil water content, as in the case of the simulation, it was at 50% of the field capacity. The DSS was also responsible for creating a fertilizer application schedule for the farmer. Using a database of recommended fertilizer application calendar, the DSS generates a list of dates of fertilizer application and queues them up in the task scheduler. Sample schedules were tested and they were all raised on the exact date and time as listed on the scheduler. A module was also developed to accommodate the manual scheduling of foliar fertilizer and pesticide application. This module permitted the automatic sending of control signals to activate the fertilizer and pesticide application. Sample schedules were tested and all were raised and the control signals were sent on the right time. The DSS also included a module for estimating the quantity of yield of crops (rice and corn) based on the weather during the flowering to anthesis stage. Results showed a relatively good approximation of the yield with an RMSE of 189.172 kg/ha and MARE of 3.684% for irrigated palay and RMSE of 362.527 kg/ha and MARE of 10.455% for yellow corn. This study also employed the GSM-SMS technology as a possible communications media between the DAS and the DSS. Keywords: Decision Support System, Neural Networks, Automated Irrigation, Yield Estimation. |
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