A Bayesian belief data mining approach applied to rice and shrimp aquaculture

• Published in: PloS one Vol. 17; no. 2; p. e0262402
• Main Authors: Randall, Marcus, Lewis, Andrew, Stewart-Koster, Ben, Anh, Nguyen Dieu, Burford, Michelle, Condon, Jason, Van Qui, Nguyen, Huu Hiep, Le, Van Bay, Doan, Van Sang, Nguyen, Sammut, Jes
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.02.2022; Public Library of Science (PLoS)
• Subjects: Agriculture; Analysis; Animals; Aquaculture; Aquaculture - methods; Automation; Bayes Theorem; Bayesian analysis; Bayesian statistical decision theory; Belief networks; Biology and Life Sciences; Cereal crops; Computer and Information Sciences; Crops; Data mining; Data Mining - methods; Decision analysis; Decision making; Decision support systems; Earth Sciences; Environmental conditions; Environmental impact; Evaluation; Farmers; Farmers - psychology; Farming; Farms; Fish-culture; Harvesting; Humans; Inspection; Intelligent systems; Oryza - growth & development; People and Places; Probability; Research and Analysis Methods; Rice; Rural communities; Smartphones; Soil sciences; Vietnam; Water quality; Vietnam; Queensland Australia; Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0262402

In many parts of the world, conditions for small scale agriculture are worsening, creating challenges in achieving consistent yields. The use of automated decision support tools, such as Bayesian Belief Networks (BBNs), can assist producers to respond to these factors. This paper describes a decision support system developed to assist farmers on the Mekong Delta, Vietnam, who grow both rice and shrimp crops in the same pond, based on an existing BBN. The BBN was previously developed in collaboration with local farmers and extension officers to represent their collective perceptions and understanding of their farming system and the risks to production that they face. This BBN can be used to provide insight into the probable consequences of farming decisions, given prevailing environmental conditions, however, it does not provide direct guidance on the optimal decision given those decisions. In this paper, the BBN is analysed using a novel, temporally-inspired data mining approach to systematically determine the agricultural decisions that farmers perceive as optimal at distinct periods in the growing and harvesting cycle, given the prevailing agricultural conditions. Using a novel form of data mining that combines with visual analytics, the results of this analysis allow the farmer to input the environmental conditions in a given growing period. They then receive recommendations that represent the collective view of the expert knowledge encoded in the BBN allowing them to maximise the probability of successful crops. Encoding the results of the data mining/inspection approach into the mobile Decision Support System helps farmers access explicit recommendations from the collective local farming community as to the optimal farming decisions, given the prevailing environmental conditions.