Weed control is a necessary practice to avoid crop yield losses. Therefore, farmers should answer the following question: when to start weed control? Currently, there are no learning models to assist the producer to answer this question. Thus, the objectives were to: 1) evaluate the ability of artificial neural networks (ANNs) to estimate the beginning of weed control for different classes of acceptable yield losses; 2) validate a new alternative for modeling and predicting competition between weeds and crops. ANNs determined the ideal moment to control weeds based on non-destructive and destructive variables. The inputs C3/C4 ratio, coexistence period, density of weeds, and crop (categorical variable to differentiate sesame and melon) provided accuracy and F-score values above 0.95 during training, validation, and testing steps for ANN in non-destructive method. When using the destructive variables, C3/C4 ratio plus coexistence period, fresh matter of weeds, and crop provided accuracy and F-score values above 0.90 during training, validation, and testing steps. The combination of non-destructive and destructive inputs also generated an ANN with high accuracy and F-score, above 0.95, during training, validation, and testing steps. Machine learning can be used in crop-weed competition modeling.

杂草控制是避免作物减产的必要措施。因此，农民应回答以下问题：何时开始除草？当前，没有学习模型可以帮助制作人回答这个问题。因此，目标是：1）评估人工神经网络（ANN）评估不同类别的可接受产量损失的杂草控制开始的能力；2）验证用于建模和预测杂草与农作物之间竞争的新方法。人工神经网络基于非破坏性和破坏性变量确定了控制杂草的理想时刻。输入的C3 / C4比率，共存期，杂草密度和农作物（用于区分芝麻和甜瓜的分类变量）在训练，验证，以及无损检测方法的ANN测试步骤。使用破坏性变量时，在训练，验证和测试步骤中，C3 / C4比率加上共存期，杂草新鲜物质和农作物的准确性和F得分均高于0.90。在训练，验证和测试步骤中，非破坏性输入和破坏性输入的组合还生成了具有0.95以上的F值和高精度的ANN。机器学习可用于作物杂草竞争建模。验证和测试步骤。机器学习可用于作物杂草竞争建模。验证和测试步骤。机器学习可用于作物杂草竞争建模。