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dc.contributor.authorSiva, Faith
dc.date.accessioned2019-10-28T10:05:18Z
dc.date.available2019-10-28T10:05:18Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11071/6702
dc.descriptionThesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore Universityen_US
dc.description.abstractAgricultural practices, tools and technologies have taken a new paradigm. Precision agriculture is essential to ensure that site-specific crop management is implemented, which includes soil nutrient remedies per crop requirement. Though fertilization is key in boosting productivity, there is need for analysis of the potentials and limitations of soil as a basis of recommending the correct type, quantities and application time of fertilizers to counter uncertainty in fertilizer use. The complexity of finding the optimal fertilization range greatly contributes to major inefficiencies like productivity losses, resource wastage and increased environmental pollution caused by farmers’ use of intuition, trial and error, guesswork and estimation. With these, farmers cannot accurately predict what impact their decisions will have on the resulting crop yields and the environment. Some solutions implemented for soil fertility management such as use of mobile laboratories or imported equipment have had their share of challenges such cost of implementation, ease of use and adaptation to the local environment. Other available solutions including taking soil to laboratories for testing is tedious, time consuming and inconsistent. This study proposed development of an ANN model that predicts NPK nutrient levels and recommends the best fertilizer remedy and application time based on the weather forecast. This involved use of IoT, machine learning techniques and a weather API through RAD methodology and experimental research design. Historical data of temperature, PH and NPK from KALRO Library was used to train and validate the model. The developed model achieved an RMSE 0.5 with 75% of data used for training and 25% used for testing. This is in effort to encourage precise fertilizer production for particular areas of need.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectNPKen_US
dc.subjectPHen_US
dc.subjectTemperatureen_US
dc.subjectSensorsen_US
dc.subjectMachine learningen_US
dc.titleSmart fertilizer recommendation through NPK analysis using Artificial Neural Networksen_US
dc.typeThesisen_US


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