Kiobia Denis O, Mwitta Canicius J, Ngimbwa Peter C, Schmidt Jason M, Lu Guoyu, Rains Glen C
College of Engineering, University of Georgia, Tifton, GA, USA.
Department of Entomology, University of Georgia, Tifton, GA, USA.
J Econ Entomol. 2025 Apr 26;118(2):732-745. doi: 10.1093/jee/toaf035.
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.
在大多数作物系统中，针对特定植物的昆虫监测和预测仍然具有挑战性。在本文中，提出了一种机器学习算法，用于在粉虱（烟粉虱，半翅目；粉虱科）监测期间预测其种群数量，并协助确定棉花植株冠层中成年粉虱的种群分布。该研究调查了成年粉虱相对于植物节点（叶子或树枝出现的茎点）的主要位置、冠层内部和之间的种群变化、田间粉虱密度的变异性、密集节点对整个冠层种群的影响以及使用机器学习进行预测的可行性。对64株未施农药的棉花植株进行了每日监测，重点关注粉虱数量最多的节点的所有叶片。线性混合效应模型评估了随时间的分布，机器学习模型选择确定了适合整个冠层粉虱种群的预测模型。研究结果表明，顶部3至5个节点是关键栖息地，单个节点可能占整个冠层粉虱种群的44.4%。Bagging集成人工神经网络回归模型准确预测了冠层种群数量（R² = 85.57），实际数量与预测数量之间具有一致性（P值>0.05）。当在田间进行少量样本或样带采集时，对顶部节点进行策略性采样可以估计整个植株的种群数量。所建议的机器学习模型可以集成到计算设备和自动传感器中，以在监测操作期间预测整个植物冠层内粉虱的实时种群密度。
