Yong Chen

Extensive research on path planning and automated navigation has been carried out for weeding robots in fields such as corn, soybean, wheat, and sugar beet, but until now, no literature reports relative studies in turfs that are not cultivated using row-crop methods. This paper proposes a practical solution that comprises path planning and path tracking to minimize the weeding robot’s travel distance in turfs for the first time. An inter-sub-region scheduling algorithm is developed using the Traveling Salesman Problem (TSP) model, followed by a boundary-shifting-based coverage path planning algorithm to achieve full coverage within each weed subregion. For path tracking, a Real-Time Kinematic Global Positioning System (RTK-GPS) fusion positioning method is developed and combined with a dynamic pure pursuit algorithm featuring a variable preview distance to enable precise path following. After path planning based on real-world site data, the weeding robot traverses all weed subregions via the shortest possible path. Field experiments showed that the robot traveled along the shortest path at speeds of 0.6, 0.8, and 1.0 m/s; the root mean square errors of autonomous navigation deviation were 0.35, 0.81, and 1.41 cm, respectively. The proposed autonomous navigation solution significantly reduces the robot’s travel distance while maintaining acceptable tracking accuracy.

This paper addresses deficiencies in existing spray carts and suspended sprayers regarding operational scenarios, spray coverage, versatility, and wall film thickness adjustment by designing a rail-mounted rotating nozzle application robot. Static analysis of the robot frame verifies compliance with strength and stiffness requirements. Motor torque calculations ensure stable and reliable nozzle rotation. Geometric modeling derives optimal link parameters for automated nozzle angle control. ANSYS Fluent simulations characterize static spray coverage, analyzing quantitative relationships between nozzle height, angle, and spray distance. SolidWorks Motion establishes a coupled model of nozzle rotation and cart translation to obtain spray trajectories under varying speeds. Coupled Fluent simulations further evaluate wall film thickness distribution patterns under dynamic spraying conditions. The findings provide a theoretical foundation and technical reference for structural optimization and precise spraying control in greenhouse spraying robot systems.

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image‐classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf.RESULTS: Weed‐detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F₁ scores (≥0.971) throughout the k‐fold cross‐validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F₁ scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F₁ scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation.CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable‐rate herbicide applications. The proposed method can be employed in a machine vision‐based autonomous site‐specific spraying system of smart sprayers. © 2024 Society of Chemical Industry.

Deep learning methods for weed detection typically focus on distinguishing weed species, but a variety of weed species with comparable plant morphological characteristics may be found in turfgrass. Thus, it is difficult for deep learning models to detect and distinguish every weed species with high accuracy. Training convolutional neural networks for detecting weeds susceptible to herbicides can offer a new strategy for implementing site-specific weed detection in turf. DenseNet, EfficientNet-v2, and ResNet showed high F₁ scores (≥0.986) and MCC values (≥0.984) to detect and distinguish the sub-images containing dollarweed, goosegrass, old world diamond-flower, purple nutsedge, or Virginia buttonweed growing in bermudagrass turf. However, they failed to reliably detect crabgrass and tropical signalgrass due to the similarity in plant morphology. When training the convolutional neural networks for detecting and distinguishing the sub-images containing weeds susceptible to ACCase-inhibitors, weeds susceptible to ALS-inhibitors, or weeds susceptible to synthetic auxin herbicides, all neural networks evaluated in this study achieved excellent F₁ scores (≥0.995) and MCC values (≥0.994) in the validation and testing datasets. ResNet demonstrated the fastest inference rate and outperformed the other convolutional neural networks on detection efficiency, while the slow inference of EfficientNet-v2 may limit its potential applications. Grouping different weed species growing in turf according to their susceptibility to herbicides and detecting and distinguishing weeds by herbicide categories enables the implementation of herbicide susceptibility-based precision herbicide application. We conclude that the proposed method is an effective strategy for site-specific weed detection in turf, which can be employed in a smart sprayer to achieve precision herbicide spraying.

Precision spraying can significantly reduce herbicide input for turf weed management. A major challenge for autonomous precision herbicide spraying is to accurately and reliably detect weeds growing in turf. Deep convolutional neural networks (DCNNs), an important artificial intelligent tool, demonstrated extraordinary capability to learn complex features from images. The feasibility of using DCNNs, including various image classification or object detection neural networks, has been investigated to detect weeds growing in turf. Due to the high level of performance of weed detection, DCNNs are suitable for the ground-based detection and discrimination of weeds growing in turf. However, reliable weed detection may be subject to the influence of weeds (e.g., biotypes, species, densities, and growth stages) and turf factors (e.g., turf quality, mowing height, and dormancy vs. non-dormancy). The present review article summarizes the previous research findings using DCNNs as the machine vision decision system of smart sprayers for precision herbicide spraying, with the aim of providing insights into future research.

Tea is one of the most common beverages in the world. Automated machinery that is suitable for plucking high-quality green tea is necessary for tea plantations and the identification of tender leaves is one of the key techniques. In this paper, we proposed a method that combines semi-supervised learning and image processing to identify tender leaves. Both in two-dimensional and three-dimensional space, the three R, G, and B components of tender leaves and their backgrounds were trained and tested. The gradient-descent method and the Adam algorithm were used to optimize the objective function, respectively. The results show that the average accuracy of tender leaf identification is 92.62% and the average misjudgment rate is 18.86%. Our experiments have shown that green tea tender leaves in early spring can be identified effectively using the model based on semi-supervised learning, which has strong versatility and perfect adaptability, so as to improve the problem of deep learning requiring a large number of labeled samples.

Background: Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides. Results: GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F₁ scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated. Conclusion: These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.