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Various pests which diminish the quality of the fruit have a big influence on the organic banana production in the Piura region of Peru (and not only) and prevent it from being sold on the international market. In this study, a framework for facilitating the prediction of the pest incidence in organic banana crops is developed. To achieve this, a data acquisition system with smart sensors is implemented to monitor the meteorological variables that influence the growth of the pests. The proposed framework is utilised for the assessment of various mathematical representations of the pest incidence. These models are adapted from population growth functions and built in such way as to predict the behaviour of the insects at non-regular time intervals. A hybrid approach, combining mechanistic and data-based methods is utilised for the development of the models. Both linear and nonlinear dynamic relationships with the temperature are assumed. The results show that nonlinear model representations have greater accuracy (a fit index of more than 70%), which provides a basis for improving pest management actions on the organic banana farms.
This contribution introduces a data acquisition and modelling framework for the prediction of banana pests’ incidence. An IoT sensors-based system collects weather and micro-climate variables, such as temperature, relative humidity, and wind speed, which are uploaded in real time to a cloud storage space. The incidence of the red rust thrips (Chaetanaphothrips signipennis) is collected “manually” by periodic inspection. The mathematical model is adapted from population growth functions and a model of insect species development and allows predictions to be made at various time intervals with an accuracy greater than 80%, improving decision-making capacity for agro-producers and enabling the improvement of pest management actions.