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dc.contributor.authorAteya, Shantal Musungu
dc.date.accessioned2018-10-23T10:08:43Z
dc.date.available2018-10-23T10:08:43Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/11071/5995
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.abstractIn March 2017, the agricultural sector in Kenya experienced a FAW pest infestation that resulted in the loss of agricultural yields amounting to millions of shillings. The fall armyworm pest caught farmers and agricultural organizations by surprise when it hit most major maize farming regions in Kenya. Currently, both large-scale and small-scale farmers rely on manual observation of the maize crop for detection of the FAW. This comes weeks after the pest has fully matured and began causing damage to crops. The late detection of the FAW in turn results to delays in administering effective pest control measures which forces farmers to incur high costs in administering appropriate control measures. With the ineffectiveness of late manual observations, there is need for an early technology-based solution that will allow farmers to prepare in advance for possible FAW infestations This study proposes the development of a prediction model of a FAW invasion using Internet of Things and machine learning techniques. We suggest the development of a model that automatically predicts a possible invasion by the FAW based on several factors. The key parameters used in the study will be soil temperature and humidity collected through sensors placed in the maize fields. These factors favour the development of the pupa stage of the FAW which later matures into moths that fly to different fields. Based on the parameters, the model will be able to detect the presence of FAW pupa in the soil and issue early warnings to farmers thus allowing for preparation and appropriate counter measures. The study will provide performance evaluation of the model based on the accuracy of the classification, the precision and recall ratio of the collected parameters. The developed model achieved an accuracy of 82.06%.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectFall army-wormen_US
dc.subjectMachine learningen_US
dc.subjectInternet of thingsen_US
dc.subjectSensorsen_US
dc.subjectPredictionen_US
dc.titleFall army-worm prediction model on the maize crop in Kenya: an internet of things based approachen_US
dc.typeThesisen_US


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