A field-based recommender system for crop disease detection using machine learning

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dc.contributor.authorOmara, Jonathan
dc.contributor.authorTalavera, Estefania
dc.contributor.authorOtim, Daniel
dc.contributor.authorTurcza, Dan
dc.contributor.authorOfumbi, Emmanuel
dc.contributor.authorOwomugisha, Godliver
dc.date.accessioned2026-03-06T10:04:00Z
dc.date.available2026-03-06T10:04:00Z
dc.date.issued2023
dc.descriptionJournal article
dc.description.abstractThis study investigates crop disease monitoring with real-time information feedback to smallholder farmers. Proper crop disease diagnosis tools and information about agricultural practices are key to growth and development in the agricultural sector. The research was piloted in a rural community of smallholder farmers having 100 farmers participating in a system that performs diagnosis on cassava diseases and provides advisory recommendation services with real-time information. Here, we present a field-based recommendation system that provides real-time feedback on crop disease diagnosis. Our recommender system is based on question–answer pairs, and it is built using machine learning and natural language processing techniques. We study and experiment with various algorithms that are considered state-of-the-art in the field. The best performance is achieved with the sentence BERT model (RetBERT), which obtains a BLEU score of 50.8%, which we think is limited by the limited amount of available data. The application tool integrates both online and online services since farmers come from remote areas where internet is limited. Success in this study will result in a large trial to validate its applicability for use in alleviating the food security problem in sub-Saharan Africa. KEYWORDS Crop disease monitoring, recommendation systems, natural language processing, smart farming, question-answer pairs, food security
dc.description.sponsorshipGoogle AI For Social Good (AI4SG) and ATPS, IDRC and AI4D Africa: AI4AFS/GA/AFS-0163245214 ; Busitema University
dc.identifier.citationOmara J, Talavera E, Otim D, Turcza D, Ofumbi E and Owomugisha G (2023) A field-based recommender system for crop disease detection using machine learning. Front. Artif. Intell. 6:1010804. doi: 10.3389/frai.2023.1010804
dc.identifier.urihttps://bdears.busitema.ac.ug/handle/123456789/701
dc.language.isoen
dc.publisherFrontiers in Artificial Intelligence
dc.relation.ispartofseriesFrontiers in Artificial Intelligence; Volume 6 - 2023
dc.titleA field-based recommender system for crop disease detection using machine learning
dc.typeArticle
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