背景:自动,精确的枣树产量预测对于果园的管理和资源分配具有重要意义。传统的产量预测技术基于目标检测,预测一个盒子来实现目标统计,但通常用于稀疏目标设置。这些技术,然而,在现实世界中使用特别浓密的枣具有挑战性。盒子标签是劳动力和时间密集的,并且系统的鲁棒性受到严重闭塞的不利影响。因此,迫切需要开发一种基于图像的枣树产量预测方法。但是除了极端的闭塞,由于规模不同,它也具有挑战性,复杂的背景,和照明变化。
结果:在这项工作中,我们开发了一个简单有效的特征增强引导网络,用于高密度枣的产量估计。它有两个关键的设计:首先,提出了一种基于均匀分布的标签表示方法,与基于高斯核的方法相比,它提供了更好的对象外观表征。这种新方法更易于实现,并显示出更大的成功。其次,我们引入了一个用于红枣计数的特征增强引导网络,包括三个主要组成部分:骨干,密度回归模块,和功能增强模块。特征增强模块在有效感知感兴趣目标和指导密度回归模块做出准确预测方面起着至关重要的作用。值得注意的是,我们的方法利用这个模块来提高我们网络的整体性能。为了验证我们方法的有效性,我们对收集的数据集进行了实验,该数据集包含692张图像,总共包含40344只大枣。结果表明,我们的方法在估计枣数方面具有很高的准确性,平均绝对误差(MAE)为9.62,均方误差(MSE)为22.47。重要的是,我们的方法在很大程度上优于其他最先进的方法,突出了其在枣产量估算中的优越性。
结论:所提出的方法为预测枣树的产量提供了一种有效的基于图像的技术。该研究将推进人工智能在农林高密度目标识别中的应用。通过利用这种技术,我们的目标是提高种植自动化水平,优化资源配置。
BACKGROUND: Automatic and precise jujube yield prediction is important for the management of orchards and the allocation of resources. Traditional yield prediction techniques are based on object detection, which predicts a box to achieve target statistics, but are often used in sparse target settings. Those techniques, however, are challenging to use in real-world situations with particularly dense jujubes. The box labeling is labor- and time-intensive, and the robustness of the system is adversely impacted by severe occlusions. Therefore, there is an urgent need to develop a robust method for predicting jujube yield based on images. But in addition to the extreme occlusions, it is also challenging due to varying scales, complex backgrounds, and illumination variations.
RESULTS: In this work, we developed a simple and effective feature enhancement guided network for yield estimation of high-density jujube. It has two key designs: Firstly, we proposed a novel label representation method based on uniform distribution, which provides a better characterization of object appearance compared to the Gaussian-kernel-based method. This new method is simpler to implement and has shown greater success. Secondly, we introduced a feature enhancement guided network for jujube counting, comprising three main components: backbone, density regression module, and feature enhancement module. The feature enhancement module plays a crucial role in perceiving the target of interest effectively and guiding the density regression module to make accurate predictions. Notably, our method takes advantage of this module to improve the overall performance of our network. To validate the effectiveness of our method, we conducted experiments on a collected dataset consisting of 692 images containing a total of 40,344 jujubes. The results demonstrate the high accuracy of our method in estimating the number of jujubes, with a mean absolute error (MAE) of 9.62 and a mean squared error (MSE) of 22.47. Importantly, our method outperforms other state-of-the-art methods by a significant margin, highlighting its superiority in jujube yield estimation.
CONCLUSIONS: The proposed method provides an efficient image-based technique for predicting the yield of jujubes. The study will advance the application of artificial intelligence for high-density target recognition in agriculture and forestry. By leveraging this technique, we aim to enhance the level of planting automation and optimize resource allocation.