Yield mapping for citrus fruit is a challenging task due to factors such as varying illumination conditions, clustering, and occlusions. Mapping the yield for immature citrus fruit presents an additional challenge that is the colours of fruit and leaves are almost identical. Commonly used machine vision techniques using colour cameras become less effective for immature citrus fruit detection. This study explores a novel active thermal imaging method to tackle the problem of colour similarity between immature citrus fruit and leaves. In this study, a thermal camera was combined with a water spray system that applied water mist to citrus trees. The water mist caused temperatures of both the fruit and leaf surfaces to change but at different rates. Multiple parameters of the spray system were experimented with the goal to induce as much temperature differences as possible between fruit and leaf surfaces. The combined system was tested in a citrus grove for fruit detections. Deep learning models were built based on the active thermal imaging system and tracking and fruit counting algorithms were created to count fruit in thermal videos. A mean average precision of 87.2% was achieved by the models and an accuracy of 96% was achieved when comparing the number of fruit counted by the algorithms with the true number of fruit counted manually in the field.

中文翻译：

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用于未成熟柑橘类水果检测的主动热成像

由于光照条件不同、聚类和遮挡等因素，柑橘类水果的产量映射是一项具有挑战性的任务。绘制未成熟柑橘类水果的产量带来了额外的挑战，即水果和叶子的颜色几乎相同。使用彩色相机的常用机器视觉技术对未成熟柑橘类水果的检测效果较差。本研究探索了一种新的主​​动热成像方法来解决未成熟柑橘类水果和叶子之间的颜色相似性问题。在这项研究中，热像仪与喷水系统相结合，将水雾喷洒到柑橘树上。水雾导致果实和叶子表面的温度发生变化，但速度不同。对喷雾系统的多个参数进行了试验，目的是在果实和叶子表面之间引起尽可能多的温差。该组合系统在柑橘园中进行了水果检测测试。基于主动热成像系统构建深度学习模型，并创建跟踪和水果计数算法来计算热视频中的水果。模型实现了 87.2% 的平均精度，当将算法计数的水果数量与现场人工计数的真实水果数量进行比较时，准确度达到了 96%。基于主动热成像系统构建深度学习模型，并创建跟踪和水果计数算法来计算热视频中的水果。模型实现了 87.2% 的平均精度，当将算法计数的水果数量与现场人工计数的真实水果数量进行比较时，准确度达到了 96%。基于主动热成像系统构建深度学习模型，并创建跟踪和水果计数算法来计算热视频中的水果。模型实现了 87.2% 的平均精度，当将算法计数的水果数量与现场人工计数的真实水果数量进行比较时，准确度达到了 96%。