Real-time monitoring model for early detection of crop diseases

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dc.contributor.authorToroitich, Patrick K.
dc.contributor.authorOrero, Joseph
dc.date.accessioned2017-07-22T10:09:38Z
dc.date.available2017-07-22T10:09:38Z
dc.date.issued2017
dc.descriptionThe conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.en_US
dc.description.abstractThe agricultural sector has been a key backbone to Kenya’s economy. Agriculture has played a key role in the economy through agricultural farm produce exports and job creation hence improving and maintaining good farming practices is critical in ensuring agricultural yields. Potato (Solanum tuberosum L.) is a major food and cash crop in the Kenyan highlands, widely grown by small-scale farmers. However, early detection of potato diseases still remains a challenge for both farmers and agricultural extension officers. Consequently agricultural extension officers who play a critical role in training and creating awareness on sound agricultural practices are few and often lack sufficient knowledge and tools. Current techniques used for determining and detecting of crop diseases have relied upon use human vision systems that try to examine physical and phenotypic characteristics such as leaf and stem color. This technique is indeed important for diagnosis of crop diseases, however the use of this technique is not efficient to support early detection of diseases. This study proposed the use of internet of things technology and machine learning techniques for the prediction of potato late blight disease. Temperature and humidity sensor probes placed on the potato were instrumental in monitoring conditions for potato late blight disease on a farm. These parameters constituted abiotic factors that favor the development and growth of Phytophthora infestants. Back propagation neural network model was suitable for the prediction of potato late blight disease. In designing the potato late blight prediction model, historical weather data, potato variety tolerance on late blight disease was used to build an artificial neural network disease prediction model. Incoming data streams from the sensors was used to determine level and risk of blight. This study focused on a moderate susceptible cultivator of potato in developing the model. The algorithm was preferred due to its strengths in adaptive learning. The developed model achieved an accuracy of 94%.en_US
dc.description.sponsorshipStrathmore University; Institute of Electrical and Electronics Engineers (IEEE)en_US
dc.identifier.citationToroitich, P. K., & Orero, J. (2017). Real-time monitoring model for early detection of crop diseases. In Pan African Conference on Science, Computing and Telecommunications (PACT). Nairobi: Strathmore University. Retrieved from https://su-plus.strathmore.eduen_US
dc.identifier.urihttp://hdl.handle.net/11071/5188
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectMachine learningen_US
dc.subjectInternet of thingsen_US
dc.subjectAndroiden_US
dc.subjectData miningen_US
dc.subjectPotato farmingen_US
dc.subjectCrop disease predictionen_US
dc.subjectWeather forecasten_US
dc.titleReal-time monitoring model for early detection of crop diseasesen_US
dc.typeConference Paperen_US
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Pan African Conference on Science, Computing and Telecommunications (PACT) 2017