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Real-time monitoring of seedling emergence is vital for vegetable crop management and yield estimation. Traditionally, crop seedling emergence monitoring relies on low-efficient and time-consuming manual counting. To address this issue, this research proposed an efficient, fast, and real-time cabbage seedling counting method (combining the improved YOLOv8n, tracking algorithm, and image processing) to accurately track cabbage seedlings in the field and implement counting with an unmanned aerial vehicle (UAV). The improved YOLOv8n replaced the C2f Block in the YOLO backbone with a Swin-conv block and incorporated ParNet attention modules in both the backbone and neck parts. This enhancement enables the YOLOv8n to surpass the base model's performance, achieving a mAP50–95 of 90.3 %, representing a 14.5 % improvement. The experiments demonstrated the superior capabilities of the counting method in terms of speed and accuracy. In field experiments, the proposed Tracking algorithms-Swin-conv blocks-ParNet attention-YOLOv8n (TSP-yolo) counting method demonstrated consistent and reliable accuracy in counting cabbage seedlings while demanding only one-seventh of the time needed compared to the manual counting method. In summary, based on TSP-yolo and implemented through an UAV, the developed seedling emergence counting method demonstrated an excellent capability of counting cabbage seedlings, resulting in significant savings in human resources for crop management. • This study effectively addressed the issue of crop seedling emergence monitoring. • The improved YOLOv8n showed superior performance for cabbage identification. • TSP-yolo-based real-time counting model achieved an impressive accuracy. • The proposed method can accurately monitor seedling densities or emergence rates. [ABSTRACT FROM AUTHOR]

Deep learning-based methods for weed detection and precise herbicide application are promising for reducing herbicide input and weed control costs. However, training the neural network to recognize weeds requires annotating a large number of training images, which is time-consuming and labor-intensive. In addition, in turf sod farms, sods need to be periodically harvested, leading to varying turfgrass and bare soil areas, which increase the complexity of weed detection. To solve this problem, this research explored semi-supervised learning (SSL) methods to train image classification neural networks. The experiments were conducted to compare the training results using different SSL strategies with 100 and 200 labeled images at three image sizes of 240 × 240, 360 × 360, or 480 × 480 pixels. The training dataset images mainly contained purple nutsedge (Cyperus rotundus L.) and green kyllinga (Kyllinga brevifolia) at the pre-flowering or seedhead stage. The F 1 score, precision, and recall were used to evaluate the performance of the trained neural networks. The results showed that the network based on the FixMatch SSL strategy trained with the input images of 240 × 240 pixels exhibited the highest F 1 score, reaching 98.1% when trained with 100 labeled images and 98.2% when trained with 200 labeled images. To summarize, these results suggest that SSL achieved a great training performance with a small number of annotations. FixMatch SSL was the most effective neural network training strategy evaluated. For the weed detection task, it was observed that neural networks trained using an input image size of 240 × 240 pixels exhibited superior performance compared to the networks trained with other image sizes. In addition, employing the SSL method with only 200 labeled images enhanced the performance of the neural network, surpassing that of fully supervised learning (FSL) approaches. • The SSL strategy proved to be effective for weed detection with a small amount of labeled data. • The SSL strategy employed in FixMatch outperformed FSL. • The optimal performance of the trained model was achieved when utilizing input images at a resolution of 240 × 240 pixels. • Varying turf coverage and bare soil area barely had any meaningful impact on the performance of SSL for weed detection. [ABSTRACT FROM AUTHOR]

[Display omitted] • YOLO series were evaluated for discrimination of alfalfa pollinating bee species. • YOLOv3 and -v5 outperformed YOLOv7 and -R regarding model precision, recall, F 1 score. • YOLOv3 and -v5 discriminated honeybee, bumblebee, and leafcutting bee with 100% accuracy. • Proposed method would help study pollinator behaviour and associated pollen flow in alfalfa. Identifying insect pollinators and their roles in mediating pollen flow is critical to understand the potential gene flow risks of insect pollination-dependent crop species, such as alfalfa. This study was conducted to evaluate and compare the feasibility of You Only Look Once (YOLO) version 3 (YOLOv3), YOLOv5, YOLOv7, and YOLO Representation (YOLOR) to discriminate the three most common alfalfa pollinating bee species, including honeybee, bumblebee, and leafcutting bee. The metrics comparison results showed YOLOv3 and YOLOv5 outperformed YOLOv7 and YOLOR regarding model precision, recall, F 1 score, and mAP50 values. YOLOv3 and YOLOv5 could successfully discriminate the three different bee species with an accuracy of almost 100% (99.9%, 99.8%, and 100% accuracy for honeybee, bumblebee, and leafcutting bee for the two models, respectively). Comparatively, YOLOv7 could discriminate honeybee with an accuracy of 95% but was more likely to mistakenly discriminate bumblebee and leafcutting bee due to the relatively lower discriminating accuracy (87.3% and 66.2%, respectively). While the values of determined parameters for YOLOR were lower than YOLOv3 and YOLOv5, the higher precision (0.99680) along with recall (0.98721), F 1 (0.99198), mAP50 (0.99323), and mAP50-100 (0.89076) values indicate that this model could be able to obtain a favorable performance in discriminating the three bee species. In summary, the proposed method in this study has the potential for identifying the alfalfa pollinating bee species, studying the bees' flower-visiting behaviors, evaluating the risks of insect-mediated pollen flow, and thus contributing to the management of genetically engineered (GE) alfalfa transgene flow. [ABSTRACT FROM AUTHOR]

Machine vision-based precision herbicide application in wheat (Triticum aestivum L.) can substantially reduce herbicide input. However, detecting newly emerged weeds in wheat fields remains a challenging task. Current deep learning-based weed detection methods require the annotation of a large amount of data, which is both time-consuming and labor-intensive. To address this issue, this research improved a semi-supervised learning (SSL) algorithm based on consistency regularization and pseudo-labeling, and incorporated an attention mechanism. Compared to fully supervised learning (FSL) algorithms, the proposed method increased the classification accuracy by 16.5%, 17.84%, and 19.67% on datasets with 200 × 200, 300 × 300, and 400 × 400 pixel images, respectively, when only 100 labeled data per class were used. Overall, the developed machine vision models using the proposed method achieved weed detection with high accuracy while requiring much fewer labeled training images, and thus is more time and labor-efficient compared to an FSL algorithm. • A semi-supervised learning method was used for detecting weeds in wheat. • The addition of attention mechanisms improved the performance of weed detection. • The semi-supervised learning method out-performed fully supervised learning. • 200×200 pixels out-performed larger image sizes for semi-supervised learning. [ABSTRACT FROM AUTHOR]

Precision herbicide application can substantially reduce herbicide input, thereby cutting chemical costs and minimizing adverse environmental impacts. A smart sprayer prototype was designed and developed for precision herbicide application in turf. This is the first study evaluating the performances of a precision sprayer for weed control in turf in field conditions. The objectives of this research were to 1) evaluate and compare the performances of the traditional broadcast application and a newly developed precision spraying technology for control of weeds in dormant bermudagrass turf, and 2) investigate the influence of weed coverage on the spray volume requirement when using the precision spraying technology developed here. DenseNet, GoogLeNet, and ResNet were evaluated for discriminating the grid cells containing weeds (spray) with the grid cells containing bermudagrass turf exclusively (nonspray). All three neural networks had an F 1 score above 0.989 in the validation datasets. ResNet outperformed DenseNet and GoogLeNet with the highest F 1 scores (≥0.992) in the testing datasets. Applying herbicide only to turf areas infested with weeds saved a significant amount of the herbicide, while achieving the same level of weed control compared to the broadcast application. The developed precision spraying technology performed well and effectively reduced the amount of herbicide input applied to the dormant bermudagrass turf, compared to the broadcast herbicide application. Overall, the smart sprayer prototype developed in this research can be used for precision weed control in dormant turf, although its design needs to be further optimized. • A custom software was developed to detect and locate weeds using image classification neural networks. • A smart sprayer prototype was designed and developed for precision herbicide application on dormant bermudagrass turf. • The developed precision spraying technology effectively reduced the amount of herbicide input applied to the turf. [ABSTRACT FROM AUTHOR]

Precision application of specific herbicides to susceptible weeds can significantly save herbicide. This is the first study evaluating the performances of precision sprayer for weed control in turf based on the herbicide weed control spectrum in field conditions. The results showed that EfficientNet-v2 and ResNet never fall below 0.992 for discriminating and detecting the grid cells encompassing weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. MCPA, a synthetic auxin herbicide, is used to evaluate the performance of the developed smart sprayer for precision control of broadleaf weeds in dormant bermudagrass turf. The developed smart sprayer prototype detected and sprayed every grid cell containing broadleaf weeds in field experiments. Compared to the broadcast application, precision spraying of MCPA provided the same level of control of broadleaf weeds. By 18 days after treatment (DAT), the nontreated control had 13 weeds no. m−2, while the plots that received broadcast and precision spraying had 0 and 1 broadleaf weed plant no. m−2, respectively. Precision herbicide application according to the herbicide weed control spectrum (HWCS) with the developed smart sprayer provided the same level of broadleaf weed control and could save more herbicides compared to an approach without discriminating weed species. Overall, these findings clearly indicated that the developed smart sprayer prototype could effectively detect, discriminate, and spray herbicides onto the grid cells containing target weeds based on the HWCS. • Discriminating weed species based on their susceptibility to herbicides allows targeted herbicide application. • A smart sprayer prototype was designed and developed for precision herbicide application on turf. • Precision herbicide application with the smart sprayer achieved weed control equivalent to broadcast application. [ABSTRACT FROM AUTHOR]

Aiming at promoting the intelligent development of control technology for new energy vehicles and showing the outstanding advantages of deep reinforcement learning (DRL), this paper trained a VGG16-based road recognition convolutional neural network (CNN) at first. Lots of high-definition images of five typical roads are collected by the racing game Dust Rally 2.0, including dry asphalt, wet asphalt, snow, dry cobblestone, and wet cobblestone. Then, a time-varying driving environment model was established, involving driving images, road slope, longitudinal speed, and the number of passengers. Finally, a stereoscopic control network suitable for nine-dimensional state space and three-dimensional action space was built, and for parallel hybrid electric vehicles (HEVs) with the P3 structure, a deep q-network (DQN)-based energy management strategy (EMS) achieving multi-objective control was proposed, including the fine-tuning strategy of the motor speed to maintain the optimal slip rate during braking, the engine power control strategy and the continuously variable transmission (CVT) gear ratio control strategy. Simulation results show under the influence of some factors such as tree shade and image compression, the road recognition network has the highest accuracy for snow roads and wet asphalt roads. Three types of control strategies learned simultaneously by the stereoscopic control network not only maintain the near-optimal slip rate in the braking period but also achieve a fuel consumption of 4788.93 g, while dynamic programming (DP)-based EMS gets a fuel consumption of 4295.61 g. Moreover, even DP-based EMS only contains three states and two actions, the time consumed for DP-based EMS and DQN-based EMS to run the speed cycle of 3602s is about 4911s and 10s, respectively. Therefore, the optimization and real-time performance of DRL-based EMS can be guaranteed. • A VGG16-based neural network for road recognition is trained. • A complex and time-varying road model with idealized characteristics is established. • Aiming at achieving integrated control, a stereoscopic control network is modeled. • A DQN-based multi-objective control strategy is proposed. [ABSTRACT FROM AUTHOR]