Huili Yin

Field positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientific management for agricultural production. Compared to conventional methods, which often struggle with the complexities of field conditions and suffer from insufficient accuracy, this study employs a novel approach using self-developed multi-sensor array hardware as a portable field topographic surveying device. This innovative setup effectively navigates challenging field conditions to collect raw data. Data fusion is carried out using the Unscented Kalman Filter (UKF) algorithm. Building on this, this study combines the good point set and Opposition-based Differential Evolution for a joint improvement of the Slime Mould Algorithm. This is linked with the UKF algorithm to establish loss value feedback, realizing the adaptive parameter adjustment of the UKF algorithm. This reduces the workload of parameter setting and enhances the precision of data fusion. The improved algorithm optimizes parameters with an efficiency increase of 40.43%. Combining professional, mapping-grade total stations for accuracy comparison, the final test results show an absolute error of less than 0.3857 m, achieving decimeter-level precision in field positioning. This provides a new application technology for better implementation of agricultural digitalization.

Maintaining robust communication between UAV(Unmanned aerial vehicle) and host computers is pivotal for ensuring the security and reliability of UAV in executing automated tasks. The advent of 5G, the latest generation of mobile communication technology, presents an opportunity to establish low-latency and high-reliability communication links between UAV and host computers. While automatic flight schemes of UAV are typically calculated through three-dimensional path planning, existing schemes often overlook the communication quality between UAV and the host computer during flight execution. This study proposes a novel approach to three-dimensional path planning that integrates considerations of 5G communication intensity into the traditional artificial potential field (APF) method. Utilizing ray tracing method, the average 5G communication intensity within a given region is computed, and areas with optimal average 5G communication quality are identified as 5G secondary gravitational points. These points guide the UAV’s three-dimensional path toward regions with superior 5G communication quality. To address the challenge of local minimum traps inherent in traditional APF methods, this study proposes employing a fuzzy control algorithm to generate auxiliary forces, enabling UAV to avoid such traps proactively. Simulation experiments conducted using MatlabR2023b validate the efficacy of the proposed approach. Results demonstrate that the enhanced APF method effectively mitigates local minimum problems, albeit with a marginal increase in average path length (13.7769%). Notably, the average path’s 5G communication intensity experiences a substantial improvement (20.7919%), indicating that the algorithm prioritizes enhancing communication quality at the expense of slightly longer paths. Moreover, in scenarios with severe signal masking at the transmitter, the algorithm exhibits even greater improvements in average path 5G communication intensity.

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R²) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R², RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

It is necessary to develop automatic picking technology to improve the efficiency of litchi picking, and the accurate segmentation of litchi branches is the key that allows robots to complete the picking task. To solve the problem of inaccurate segmentation of litchi branches under natural conditions, this paper proposes a segmentation method for litchi branches based on the improved DeepLabv3+, which replaced the backbone network of DeepLabv3+ and used the Dilated Residual Networks as the backbone network to enhance the model’s feature extraction capability. During the training process, a combination of Cross-Entropy loss and the dice coefficient loss was used as the loss function to cause the model to pay more attention to the litchi branch area, which could alleviate the negative impact of the imbalance between the litchi branches and the background. In addition, the Coordinate Attention module is added to the atrous spatial pyramid pooling, and the channel and location information of the multi-scale semantic features acquired by the network are simultaneously considered. The experimental results show that the model’s mean intersection over union and mean pixel accuracy are 90.28% and 94.95%, respectively, and the frames per second (FPS) is 19.83. Compared with the classical DeepLabv3+ network, the model’s mean intersection over union and mean pixel accuracy are improved by 13.57% and 15.78%, respectively. This method can accurately segment litchi branches, which provides powerful technical support to help litchi-picking robots find branches.