Cheng Fengna, Wei Juntao, Jiang Shengqin, Chen Qing, Ru Yu, Zhou Hongping
College of Energy and Power Engineering, Nanjing Forestry University, Nanjing, 210037, China.
School of Computer, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Plant Methods. 2023 Aug 16;19(1):85. doi: 10.1186/s13007-023-01066-2.
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.
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.
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.
自动且精确的枣树产量预测对于果园管理和资源分配至关重要。传统的产量预测技术基于目标检测，通过预测一个边界框来获取目标统计信息，但通常用于稀疏目标场景。然而，在枣树特别密集的实际场景中，这些技术使用起来具有挑战性。边界框标注既耗费人力又耗时，并且系统的鲁棒性会受到严重遮挡的不利影响。因此，迫切需要开发一种基于图像的稳健枣树产量预测方法。但除了极端遮挡外，由于尺度变化、背景复杂和光照变化，这也具有挑战性。
在这项工作中，我们开发了一种简单有效的特征增强引导网络，用于高密度枣树的产量估计。它有两个关键设计：首先，我们提出了一种基于均匀分布的新颖标签表示方法，与基于高斯核的方法相比，它能更好地表征目标外观。这种新方法实现起来更简单，并且已显示出更大的成功。其次，我们引入了一个用于枣树计数的特征增强引导网络，它由三个主要部分组成：主干网络、密度回归模块和特征增强模块。特征增强模块在有效感知感兴趣目标并引导密度回归模块进行准确预测方面起着关键作用。值得注意的是，我们的方法利用这个模块提高了网络的整体性能。为了验证我们方法的有效性，我们在一个收集的数据集上进行了实验，该数据集由692张图像组成，总共包含40344颗枣树。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，我们的方法显著优于其他现有最先进的方法，突出了其在枣树产量估计方面的优越性。
所提出的方法提供了一种有效的基于图像的枣树产量预测技术。该研究将推动人工智能在农林高密度目标识别中的应用。通过利用这项技术，我们旨在提高种植自动化水平并优化资源分配。
