Estimation of phenology distribution in horticultural crops is very important as it governs the timing of chemical thinning in order to produce good quality fruit. This paper presents a novel phenology distribution estimation method named DeepPhenology for apple flowers based on CNNs using RGB images, which is able to efficiently map the flower distribution on an image-level, row-level, and block-level. The image classification model VGG-16 was directly trained with relative phenology distributions calculated from manual counts of flowers in the field and acquired imagery. The proposed method removes the need to label images, which overcomes difficulties in distinguishing overlapping flower clusters or identifying hidden flower clusters when using 2D imagery. DeepPhenology was tested on both daytime and night-time images captured using an RGB camera mounted on a ground vehicle in both Gala and Pink Lady varieties in an Australian orchard. An average Kullback-Leibler (KL) divergence value of 0.23 over all validation sets and an average KL value of 0.27 over all test sets was achieved. Further evaluation has been done by comparing the proposed model with YOLOv5 and shown to outperform this state-of-the-art object detection model for this task. By combining relative phenology distributions from a single image to a row-level or block-level distribution, we are able to give farmers a precise and high-level overview of block performance to form the basis for decisions on chemical thinning applications.

园艺作物物候分布的估计非常重要，因为它决定了化学疏化的时间，从而生产出优质的水果。本文提出了一种使用RGB图像的基于CNN的苹果花物候分布估计新方法，称为DeepPhenology，它能够有效地在图像级别，行级别和块级别上映射花朵分布。图像分类模型VGG-16是直接训练的，具有相对应的物候分布，这些分布是根据田间花朵的人工计数和获取的图像计算得出的。所提出的方法消除了标记图像的需要，从而克服了在使用2D图像时区分重叠的花簇或识别隐藏的花簇的困难。深度现象学在澳大利亚果园的Gala和Pink Lady品种中，使用安装在地面车辆上的RGB摄像头对白天和晚上拍摄的图像进行了测试。所有验证集的平均Kullback-Leibler（KL）差异值为0.23，所有测试集的平均KL值为0.27。通过将建议的模型与YOLOv5进行比较，已经进行了进一步的评估，结果表明该模型优于此最新技术的目标检测模型。通过将从单个图像到行级或块级分布的相对物候分布相结合，我们能够为农民提供块级性能的精确而高层次的概览，从而为决定化学稀释应用的基础。