The importance of monitoring and modelling the impact of climate change on crop phenology in a given ecosystem is ever-growing. For example, these procedures are useful when planning various processes that are important for plant protection. In order to proactively monitor the olive (Olea europaea)’s phenological response to changing environmental conditions, it is proposed to monitor the olive orchard with moving or stationary cameras, and to apply deep learning algorithms to track the timing of particular phenophases. The experiment conducted for this research showed that hardly perceivable transitions in phenophases can be accurately observed and detected, which is a presupposition for the effective implementation of integrated pest management (IPM). A number of different architectures and feature extraction approaches were compared. Ultimately, using a custom deep network and data augmentation technique during the deployment phase resulted in a fivefold cross-validation classification accuracy of 0.9720 ± 0.0057. This leads to the conclusion that a relatively simple custom network can prove to be the best solution for a specific problem, compared to more complex and very deep architectures.

中文翻译：

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深度学习架构在橄榄树开花期准确检测中的应用

在给定的生态系统中，监测和模拟气候变化对作物物候的影响的重要性日益增长。例如，这些程序在计划对植物保护很重要的各种过程时很有用。为了主动监测橄榄（油橄榄））对不断变化的环境条件的物候响应，建议使用移动或固定相机监控橄榄园，并应用深度学习算法来跟踪特定表相的时间。为进行这项研究而进行的实验表明，可以准确地观察和检测到很难观察到的表相过渡，这是有效实施病虫害综合治理（IPM）的前提。比较了许多不同的体系结构和特征提取方法。最终，在部署阶段使用定制的深层网络和数据增强技术可产生0.9720±0.0057的五倍交叉验证分类精度。