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

Abstract

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. [Figure: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Keywords

• classification
• epidemiology
• modelling
• prediction
• random forest
• spatial sensing