Orchard machinery autonomous navigation is helpful for improving the efficiency of fruit production and reducing labor costs. Path planning is one of the core technologies of autonomous navigation for orchard machinery. As normally planted in straight and parallel rows, fruit trees are natural landmarks that can provide suitable cues for orchard intelligent machinery. This paper presents a novel method to realize path planning based on computer vision technologies. We combine deep learning and the least-square (DL-LS) algorithm to carry out a new navigation line extraction algorithm for orchard scenarios. First, a large number of actual orchard images are collected and processed for training the YOLO V3 model. After the training, the mean average precision (MAP) of the model for trunk and tree detection can reach 92.11%. Secondly, the reference point coordinates of the fruit trees are calculated with the coordinates of the bounding box of trunks. Thirdly, the reference lines of fruit trees growing on both sides are fitted by the least-square method and the navigation line for the orchard machinery is determined by the two reference lines. Experimental results show that the trained YOLO V3 network can identify the tree trunk and the fruit tree accurately and that the new navigation line of fruit tree rows can be extracted effectively. The accuracy of orchard centerline extraction is 90.00%.

Phosphorus (P) is an important indicator for evaluating eutrophication in water bodies. Laser-induced breakdown spectroscopy (LIBS), as a total elemental analysis technique, is widely used in various fields. This work proposes a new application for this technique based on the adsorption of P using iron hydroxide. Using this method, a calibration curve with an R² of 0.996 was obtained along with an optimal limit of detection (LoD) of P in water of 0.1 mg L⁻¹. The analysis of spiked river and pond water samples showed P recoveries ranging from 94% to 113%. This adsorption-based analytical method of P allows the highly accurate assessment of actual water samples and could have practical applications.