Machine-learning approach facilitates prediction of whitefly spatiotemporal dynamics in a plant canopy.

Abstract

Plant-specific insect scouting and prediction are still challenging in most crop systems. In this article, a machine-learning algorithm is proposed to predict populations during whiteflies (Bemisia tabaci, Hemiptera; Gennadius Aleyrodidae) scouting and aid in determining the population distribution of adult whiteflies in cotton plant canopies. The study investigated the main location of adult whiteflies relative to plant nodes (stem points where leaves or branches emerge), population variation within and between canopies, whitefly density variability across fields, the impact of dense nodes on overall canopy populations, and the feasibility of using machine learning for prediction. Daily scouting was conducted on 64 non-pesticide cotton plants, focusing on all leaves of a node with the highest whitefly counts. A linear mixed-effect model assessed distribution over time, and machine-learning model selection identified a suitable forecasting model for the entire canopy whitefly population. Findings showed that the top 3 to 5 nodes are key habitats, with a single node potentially accounting for 44.4% of the full canopy whitefly population. The Bagging Ensemble Artificial Neural Network Regression model accurately predicted canopy populations (R² = 85.57), with consistency between actual and predicted counts (P-value > 0.05). Strategic sampling of the top nodes could estimate overall plant populations when taking a few samples or transects across a field. The suggested machine-learning model could be integrated into computing devices and automated sensors to predict real-time whitefly population density within the entire plant canopy during scouting operations.