As deep learning predictive models become an integral part of a large
spectrum of precision agricultural systems, a barrier to the adoption of such
automated solutions is the lack of user trust in these highly complex, opaque
and uncertain models. Indeed, deep neural networks are not equipped with any
explicit guarantees that can be used to certify the system's performance,
especially in highly varying uncontrolled environments such as the ones
typically faced in computer vision for agriculture.Fortunately, certain methods
developed in other communities can prove to be important for agricultural
applications. This article presents the conformal prediction framework that
provides valid statistical guarantees on the predictive performance of any
black box prediction machine, with almost no assumptions, applied to the
problem of deep visual classification of weeds and crops in real-world
conditions. The framework is exposed with a focus on its practical aspects and
special attention accorded to the Adaptive Prediction Sets (APS) approach that
delivers marginal guarantees on the model's coverage. Marginal results are then
shown to be insufficient to guarantee performance on all groups of individuals
in the population as characterized by their environmental and pedo-climatic
auxiliary data gathered during image acquisition.To tackle this shortcoming,
group-conditional conformal approaches are presented: the ''classical'' method
that consists of iteratively applying the APS procedure on all groups, and a
proposed elegant reformulation and implementation of the procedure using
quantile regression on group membership indicators. Empirical results showing
the validity of the proposed approach are presented and compared to the
marginal APS then discussed.

基于深度学习的精准农业系统中，缺乏对高度复杂、不透明和不确定的模型的用户信任是自动化解决方案被采纳的一大障碍。本文介绍了一种提供对任何黑盒预测机器的预测性能进行有效统计保证的符合预测框架，应用于实际环境中的深度视觉分类问题。同时介绍了针对不同群体条件的符合预测方法，通过经验证实证了该方法的有效性并与现有方法进行了比较和讨论。