Data processing of low-altitude remote sensing visible images from UAVs is one of the hot research topics in precision agriculture aviation. In order to solve the problems of large model size with slow detection speed that lead to the inability to process images in real time, this paper proposes a lightweight target detector CURI-YOLOv7 based on YOLOv7tiny which is suitable for individual citrus tree detection from UAV remote sensing imagery. This paper augmented the dataset with morphological changes and Mosica with Mixup. A backbone based on depthwise separable convolution and the MobileOne-block module was designed to replace the backbone of YOLOv7tiny. SPPF (spatial pyramid pooling fast) was used to replace the original spatial pyramid pooling structure. Additionally, we redesigned the neck by adding GSConv and depth-separable convolution and deleted its input layer from the backbone with a size of (80, 80) and its output layer from the head with a size of (80, 80). A new ELAN structure was designed, and the redundant convolutional layers were deleted. The experimental results show that the GFLOPs = 1.976, the parameters = 1.018 M, the weights = 3.98 MB, and the mAP = 90.34% for CURI-YOLOv7 in the UAV remote sensing imagery of the citrus trees dataset. The detection speed of a single image is 128.83 on computer and 27.01 on embedded devices. Therefore, the CURI-YOLOv7 model can basically achieve the function of individual tree detection in UAV remote sensing imagery on embedded devices. This forms a foundation for the subsequent UAV real-time identification of the citrus tree with its geographic coordinates positioning, which is conducive to the study of precise agricultural management of citrus orchards.

Obtaining the geographic coordinates of single fruit trees enables the variable rate application of agricultural production materials according to the growth differences of trees, which is of great significance to the precision management of citrus orchards. The traditional method of detecting and positioning fruit trees manually is time-consuming, labor-intensive, and inefficient. In order to obtain high-precision geographic coordinates of trees in a citrus orchard, this study proposes a method for citrus tree identification and coordinate extraction based on UAV remote sensing imagery and coordinate transformation. A high-precision orthophoto map of a citrus orchard was drawn from UAV remote sensing images. The YOLOv5 model was subsequently used to train the remote sensing dataset to efficiently identify the fruit trees and extract tree pixel coordinates from the orchard orthophoto map. According to the geographic information contained in the orthophoto map, the pixel coordinates were converted to UTM coordinates and the WGS84 coordinates of citrus trees were obtained using Gauss–Krüger inverse calculation. To simplify the coordinate conversion process and to improve the coordinate conversion efficiency, a coordinate conversion app was also developed to automatically implement the batch conversion of pixel coordinates to UTM coordinates and WGS84 coordinates. Results show that the Precision, Recall, and F1 Score for Scene 1 (after weeding) reach 0.89, 0.97, and 0.92, respectively; the Precision, Recall, and F1 Score for Scene 2 (before weeding) reach 0.91, 0.90 and 0.91, respectively. The accuracy of the orthophoto map generated using UAV remote sensing images is 0.15 m. The accuracy of converting pixel coordinates to UTM coordinates by the coordinate conversion app is reliable, and the accuracy of converting UTM coordinates to WGS84 coordinates is 0.01 m. The proposed method is capable of automatically obtaining the WGS84 coordinates of citrus trees with high precision.

To address issues such as the confusion of environmental feature points and significant pose information errors in chili fields, an autonomous navigation system based on multi-sensor data fusion and an optimized TEB (Timed Elastic Band) algorithm is proposed. The system’s positioning component integrates pose data from the GNSS and the IMU inertial navigation system, and corrects positioning errors caused by the clutter of LiDAR environmental feature points. To solve the problem of local optimization and excessive collision handling in the TEB algorithm during the path planning phase, the weight parameters are optimized based on environmental characteristics, thereby reducing errors in optimal path determination. Furthermore, considering the topographic inclination between rows (5–15°), 10 sets of comparison tests were conducted. The results show that the navigation system reduced the average path length by 0.58 m, shortened the average time consumption by 2.55 s, and decreased the average target position offset by 4.3 cm. In conclusion, the multi-sensor data fusion and optimized TEB algorithm demonstrate significant potential for realizing autonomous navigation in the narrow and complex environment of chili fields.