Plant stress recognition has witnessed significant improvements in recent years with the advent of deep learning. A large-scale and annotated training dataset is required to achieve decent performance; however, collecting it is frequently difficult and expensive. Therefore, deploying current deep learning-based methods in real-world applications may suffer primarily from limited and imperfect data. Embracing them is a promising strategy that has not received sufficient attention. From this perspective, a systematic survey was conducted in this study, with the ultimate objective of monitoring plant growth by implementing deep learning, which frees humans and potentially reduces the resultant losses from plant stress. We believe that our paper has highlighted the importance of embracing this limited and imperfect data and enhanced its relevant understanding.

翻译：近年来，随着深度学习的兴起，植物胁迫识别取得了显著进展。要实现良好的性能，通常需要大规模且带有标注的训练数据集；然而，收集此类数据往往困难且成本高昂。因此，在实际应用中部署当前基于深度学习的方法可能会主要受限于数据的有限性和不完善性。拥抱这些数据是一种有前景的策略，但尚未得到足够关注。基于这一视角，本研究进行了系统性综述，终极目标是通过实施深度学习来监测植物生长，从而解放人力并潜在地减少植物胁迫带来的损失。我们相信，本文强调了拥抱这些有限和不完美数据的重要性，并增进了对其相关理解。