Feature enhancement guided network for yield estimation of high-density jujube.

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.

New results on adaptive fixed-time control for convex-delayed neural networks.

This paper studies the adaptive fixed-time synchronization issue for convex-delayed neural networks. First, the convex delay is introduced to address the state delay of neural networks in order to reflect the impacts of multiple delay components such as input transition time and switching communication. Then, a new fixed-time control method is presented to adaptively determine multi-control gains with a unified update law. Afterward, some sufficient criteria are figured out by using Lyapunov stability theorem to ensure that the delayed neural networks are fixed-timely stable. Finally, simulated examples are adopted to validate our theoretical results.