Predicting Potato Diseases in Smallholder Agricultural Areas of Nigeria Using Machine Learning and Remote Sensing-Based Climate Data

Crop disease management is crucial for sustainable food production. Although farmers in Nigeria continue to apply broad-spectrum fungicides to potato, potato diseases are still on the rise. Machine-learning methods have recently become more common as part of epidemiological early warning systems. They provide vital information on data-disease relations that may be useful for predisease management. In this study, we build on machine-learning methods to develop spatial early warning tools for Nigeria, using the Jos Plateau as a test case. Both remote sensing meteorological and field data were used to (i) predict disease incidence using field reference data and a random forest (RF) classifier and to (ii) identify local conditions conducive to potato diseases using multi-criteria classification (MCC) based on machine-learning results. The results of the RF for 2019, 2020, and 2021 showed similar spatial characteristics, whereas the MCC varied significantly. Both models predicted that between 72 and 96% of potato fields would be infested. The MCC model further revealed that spatiotemporal frequencies of vulnerability in June can serve as the indicator that informs degrees of infestation. A 5-day vulnerability window used in the context of the MCC proved to be the most useful tool for developing an efficient spraying regime, based on a combination of temperature, rainfall, and relative humidity thresholds. As a result, we were able to develop an operational early-warning system for potato disease in the tropical highlands of Africa. In particular, we introduced spatial risks, creating a more sustainable early-warning approach.Copyright (c) 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.