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The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and these require accurate distinction between crops and weeds. In this paper, images of soybean seedlings and weeds in different growth areas are used as datasets. In the aspect of soybean recognition, this paper designs a YOLOv8nGP algorithm with a backbone network optimisation based on GhostNet and an unconstrained pruning method with a 60% pruning rate. Compared with the original YOLOv8n, the YOLOv8nGP improves the Precision (P), Recall (R), and F1 metrics by 1.1% each, reduces the model size by 3.6 mb, and the inference time was 2.2 ms, which could meet the real-time requirements of field operations. In terms of weed recognition, this study utilises an image segmentation method based on the Normalized Excess Green Index (NExG). After filtering the soybean seedlings, the green parts of the image are extracted for weed recognition, which reduces the dependence on the diversity of the weed datasets. This study combines deep learning with traditional algorithms, which provides a new solution for weed recognition of soybean seedlings. [ABSTRACT FROM AUTHOR]

Weeds compete with crops for water and nutrients and can adversely affect crop growth and yield, so it is important to research effective weed control methods. This paper provides an overview of the impact of weeds on crop yield and describes the current state of research on weed management in field herbaceous crops. Physical weed control mainly refers to thermal technologies represented by flame weed control and laser weed control, which can efficiently and accurately remove weeds. Mechanical weed control requires a combination of sensor technologies, machine vision technology, and high-precision navigation to improve weed control accuracy. Biological weed control relies heavily on plant extracts and pathogens to create herbicides, but it is costly, and some can be toxic to mammals. Chemical weed control is a common method, resulting in environmental pollution and weed resistance. To reduce the use of chemical herbicides, scholars have proposed integrated weed management strategies, which combine biological control, control of the seed bank, and improve crop competitiveness. Integrated weed management strategies are considered to be the future direction of weed management. In conclusion, physical, mechanical, biological, and chemical weed control methods are commonly used in weed management. Each method has its applicable scenarios, and the implementation of integrated weed management strategies can lead to better weed control, improving crop yield and quality. The main objective of this review is to organize the research progress on weed management methods for herbaceous crops in the field and to provide a reference for the agricultural sector to develop weed control strategies. Specifically, this paper categorizes weed management methods into four groups, discusses and presents the advantages and disadvantages of the aforementioned weed control methods, and discusses future research directions. [ABSTRACT FROM AUTHOR]

This paper focuses on the weed species Echinochloa crus-galli, commonly known as barnyardgrass, which is a persistent threat to crop yield and quality, especially in maize (Zea mays) cultivation. It is one of the most problematic weeds in agricultural fields due to its aggressive growth, adaptability to different environmental conditions and prolific seed production. The incidence of Echinochloa crus-galli in maize fields has increased in recent years. This study aims to provide a comprehensive understanding of the characteristics and behavior of Echinochloa crus-galli, and to suggest effective measures to control it. This research on sweetcorn was conducted from 1992 to 2019 at the Research and Education Center Gorzyń, Złotniki branch, which belongs to the Poznań University of Life Sciences. The evaluation of weed infestation was carried out in experiments focusing on chemical weed control in maize. The experiments were designed as a one-factor randomised block design with four field replications. The condition and the degree of weed infestation (number of weeds and fresh weight of weeds) in the control plots was assessed on an annual basis at the end of June and in July. The aim of the study was to evaluate the dynamic changes in the status and extent of barnyardgrass (Echinochloa crus-galli) infestation in maize grown after various other crops in the Wielkopolska region, with a focus on the weather conditions. The study found that barnyardgrass was most likely to occur when maize was sown after winter wheat in a dry and warm year, and least likely when maize was grown in rotation after winter wheat in a cold year with average rainfall. The proportion of barnyardgrass weed mass in the total weed mass was significantly lower after winter rye than after winter wheat, winter oilseed rape and winter triticale. Further research into the biology and ecology of barnyardgrass is key to effectively controlling this weed and safeguarding sweetcorn yields. [ABSTRACT FROM AUTHOR]

White lupine (Lupinus albus L.) is a well-known green manure crop in Hungary, but the production of seeds can be badly impacted by weeds. The sweet white lupine 'Nelly' was grown on acidic sandy soil, and experimental plots were treated with different herbicides. Flumioxazin (0.06 kg ha−1), pendimethalin (5 L ha−1), dimethenamid-P (1.4 L ha−1), pethoxamid (2 L ha−1), clomazone (0.2 L ha−1), metobromuron (3 L ha−1), and metribuzin (0.55 L ha−1) were applied pre-emergence (1–2 days after sowing). Imazamox was also tested and applied post-emergence (1 L ha−1) when some basal leaves were clearly distinct (BBCH 2.3). In this paper, the weed control efficiency and the phytotoxicity of herbicides applied to lupine are examined. Vegetation index datasets were collected 12 times using a manual device and 2 times using an unmanned aerial vehicle (UAV). The phytotoxicity caused by herbicides was visually assessed on several occasions throughout the breeding season. The frequency of weed occurrence per treatment was assessed. The harvested seed yields, in kg ha−1, were analyzed after the seeds were cleaned. The herbicides metribuzin and imazamox caused extensive damage to white lupine. While pendimethalin, dimethenamid-P, pethoxamid, and clomazone were outstanding in several measured indicators, the final ranking which summarizes all the variables showed that only the pethoxamid and clomazone treatments performed better than the control. Metribuzin and imazamox were highly phytotoxic to white lupine. In the future, it would be appropriate to integrate more post-emergence active substances into trials, and the pre-emergence herbicides involved in this study should be further tested. [ABSTRACT FROM AUTHOR]

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology's prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. [ABSTRACT FROM AUTHOR]