We describe the lessons learned from targeting agricultural detection problem-solving, when subject to low resolution input maps, by means of Machine Learning-based super-resolution approaches. The underlying domain is the so-called agro-detection class of problems, and the specific objective is to learn a complementary ensemble of sporadic input maps. While super-resolution algorithms are branded with the capacity to enhance various attractive features in generic photography, we argue that they must meet certain requirements, and more importantly, that their outcome does not necessarily guarantee an improvement in engineering detection problem-solving (unlike so-called aesthetics/artistic super-resolution in ImageNet-like datasets). By presenting specific data-driven case studies, we outline a set of limitations and recommendations for deploying super-resolution algorithms for agro-detection problems. Another conclusion states that super-resolution algorithms can be used for learning missing spectral channels, and that their usage may result in some desired side-effects such as channels' synchronization.

翻译：本文报告了针对农业检测问题解决中，当输入地图分辨率较低时，通过基于机器学习的超分辨率方法所获得的经验教训。该领域属于所谓的农业检测问题类别，具体目标是学习非连续输入地图的互补集成。尽管超分辨率算法被宣称能够增强通用摄影中各类吸引人的特征，但我们认为这些算法必须满足特定要求，且更重要的是其输出结果并不一定能保证工程检测问题解决能力的提升（这与ImageNet类数据集中的所谓美学/艺术超分辨率不同）。通过展示具体的数据驱动案例研究，我们概述了将超分辨率算法应用于农业检测问题时所面临的一系列局限性及建议。另一项结论表明，超分辨率算法可用于学习缺失的光谱通道，且其应用可能产生某些理想的副作用，如通道同步。