Abstract Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.

中文翻译：

---

预测小农玉米店的 Prostephanus truncatus（角）（鞘翅目：Bostrichidae）种群和相关的谷物损害：机器学习方法

摘要 Prostephanus truncatus 是一种臭名昭著的玉米贮藏害虫，它在整个撒哈拉以南非洲地区的传播导致了谷物贮藏损失的增加。当前的研究开发了模型来预测小农商店中玉米节蠵虫的侵染程度和相关的玉米损害。数据是从津巴布韦 Hwedza 和 Mbire 地区进行的谷物储存试验中收集的，并与每个地点的天气数据相关联。P. truncatus 和其他常见储粮害虫的昆虫数量与一年中的时间有很强的相关性，最高记录的数量是在 1 月至 5 月。相关性分析表明，昆虫产生的钻孔和取食活动产生的谷物粉尘是商店中 P. truncatus 存在的最佳指标 (r = 0.70)，而 P. truncatus 之间存在中等相关性 (r = 0.48)。truncatus 数量和贮藏昆虫寄生蜂，以及谷物损伤水平与 Tribolium castaneum 的存在显着相关 (r = 0.60)。使用参数选择算法和决策树机器学习算法和 10 倍交叉验证，开发了用于预测 P. truncatus 侵染和谷物损害的模型。由于害虫的采样和检测复杂以及采样事件之间的八周长周期，P. truncatus 种群规模预测模型性能较弱（r = 0.43）。粮害预测模型具有较强的相关系数（r = 0.93），是对原位贮粮虫害的良好估计。