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Rapeseed, weeds, and pathogens interact with each other. However, these interactions are not well understood. The aim of our work was to describe the relationships between weed vegetation and pathogen manifestations in rapeseed stands. Results from the four seasons show that different rapeseed stand structures produce different weed and pathogen responses. Eighteen weed species were identified in the rapeseed stands. The selected characteristics of rapeseed stands, pathogens, and weed manifestations were evaluated using redundancy analysis. Rapeseed stands with the highest levels of pathogens present (Alternaria brassiceae (Berk.) Sacc., Botrytis cinerea (De Bary) Whetzel, Sclerotinia sclerotiorum (Lib.) de Bary, Verticilium longisporum (C.Stark) Karapapa, Bainbr & Heale) had the lowest seed yield. There, the weeds Cirsium arvense (L.) Scop., Tripleurospermum inodorum (L.) Sch. Bip., Sonchus arvensis L. were more abundant in dense stands, and Phoma lingam (telomorph: Leptosphaeria maculans Ces. & De Not.) was more common. Mutual positive interactions may also include the relationship between weed species of the Asteraceae family and increased manifestations of Phoma lingam. A similar relationship can be expected for the weeds Capsella bursa-pastoris (L.) Medik., Descurainia sophia (L.) Prantl and Sclerotinia sclerotiorum symptoms. Full article

Pesticide use in crops is a severe problem in some countries. Each country has its legislation for use, but they differ in the degree of tolerance for these broadly toxic products. Several synthetic pesticides can cause air, soil, and water pollution, contaminating the human food chain and other living beings. In addition, some of them can accumulate in the environment for an indeterminate amount of time. The agriculture sector must guarantee healthy food with sustainable production using environmentally friendly methods. In this context, biological biopesticides from microbes and plants are a growing green solution for this segment. Several pests attack crops worldwide, including weeds, insects, nematodes, and microorganisms such as fungi, bacteria, and viruses, causing diseases and economic losses. The use of bioproducts from microorganisms, such as microbial biopesticides (MBPs) or microorganisms alone, is a practice and is growing due to the intense research in the world. Mainly, bacteria, fungi, and baculoviruses have been used as sources of biomolecules and secondary metabolites for biopesticide use. Different methods, such as direct soil application, spraying techniques with microorganisms, endotherapy, and seed treatment, are used. Adjuvants like surfactants, protective agents, and carriers improve the system in different formulations. In addition, microorganisms are a tool for the bioremediation of pesticides in the environment. This review summarizes these topics, focusing on the biopesticides of microbial origin. Full article

Ambrosia artemisiifolia has turned into a noxious weed species in agricultural fields and landscapes in Europe. Durable control options are still needed to limit the abundance of this species. Weed seed consumption by naturally occurring seed predators is a key ecosystem service in agricultural areas. Seed predation levels of common ragweed were examined in wheat and maize fields and adjacent semi-natural habitats (SNHs). To evaluate the weed seeds’ exposure to invertebrate seed predators, 20 cards each were set on the soil surface inside the crop field and in SNHs with four replications. Twenty seeds of ragweed were attached to sandpaper. Seed removal was assessed every 24 h of exposure for 5 days in June and November 2019, October 2020, and June 2021. The seed consumption level was measured according to the number of removed seeds from the seed cards. High consumption rates of ragweed seeds were found in all sampling rounds in both seasons and habitats. The seed predation rates in 2019 were stronger within crop fields in summer than in autumn with a slight difference between SNHs and inside fields. Our results demonstrate the possibility of seed predation contributing to Integrated Plant Protection (IPM) of common ragweed in rural areas. Full article

Sesquiterpene lactones (SLs) are compounds that are highly produced in Cynara cardunculus leaves, known for their phytotoxic activity. This study aims to assess SL-enriched fractions’ (cynaropicrin, aguerin B, and grosheimin) phytotoxic potentials and putative modes of action, compared to an initial extract, using two approaches: first, against a panel of nine weed species in pre-emergence, and then on Portulaca oleracea L.’s post-emergency stage. The SL-enriched fractions demonstrated greater phytotoxic activity when compared with the C. cardunculus leaf initial extract. The SL-enriched fractions had higher activity at root growth inhibition over the panel tested, doubling the activity in five of them at 800 ppm. Regarding the post-emergence bioassay, the SL-enriched fractions had a higher influence on the plants’ growth inhibition (67% at 800 ppm). The SL-effects on the plants’ metabolisms were evidenced. The total chlorophyll content was reduced by 65% at 800 ppm. Oxidative stress induction was observed because of the enhancement in MDA levels at 800 ppm compared to control (52%) and the decrease in SOD-specific activity from 4.20 U/mg protein (400 ppm) to 1.74 U/mg protein (800 ppm). The phytotoxic effects of the SL-enriched fractions suggest that they could be used for a future bioherbicide development. Full article

The field cricket, Gryllus madagascarensis, is a sustainable and nutritious food resource that has the potential to mitigate global malnutrition. Feeds provided to this cricket can influence its growth parameters, nutritional content, and the cost of raising it for food. The current study aimed to evaluate the effects of feeds formulated from weeds, agro-byproducts, and chicken feed (control) on the growth parameters and nutritional content of G. madagascarensis. The formulated feeds included CFB (25.0% protein), CFC (24.5% protein), CFD (24.0% protein), CFE (23.5% protein), CFF (22.5% protein), CFG (21.5% protein), CFH (20.0% protein), CFI (14.5% protein), and CFJ (13.5% protein), and chicken feed (CFA) (28% protein) was used as the control. The formulation of the feeds was based on the acceptability and protein content of the 12 selected weeds and agro-byproducts. Proximate, mineral, and fatty acid analyses were conducted to determine the nutrient content of each feed, as well as the crickets raised on these feeds. The fastest development time was recorded with CFE and CFC. The highest survivorship (98%) was observed in CFG, CFE, and CFC. The highest body mass (1.15 g) and body length (26.80 mm) were observed in crickets fed CFG. By comparison, crickets fed control feed averaged a body mass of 0.81 g and a body length of 23.55 mm. The feed conversion ratio for G. madagascarensis fed CFG, CFE, and CFC was 1.71. Crickets raised on CFH and CFG had the lowest cost of feeding per kg live mass gain. Crickets fed on CFF had the highest quantity of protein (67%), followed by those fed CFG (65% protein); crickets with the lowest protein content (50%) were fed CFJ. Crickets fed on CFG had the highest mineral content. Linoleic acid, oleic acid, and palmitic acid were the major fatty acids. The findings indicate that formulated feeds from weeds and agro-byproducts have great potential to be used as an alternative feed source for crickets for two reasons: their capacity to positively influence the biology and nutrition of the cricket, and they can serve as an inexpensive replacement for chicken feed. Full article

The by-products of green coffee processing are rich in compounds that can be recycled for their possible use in the production of beverages, fertilizers and weed control in production areas. The objective of this work was to identify the organic and inorganic bioactive compounds of green coffee and the coffee by-products related to the production of origin, such as dried cascara (skin-pulp), parchment and silverskin (unroasted), in order to investigate the role their biomolecules may have in reuse through practices and local knowledge, not yet valued. The metabolomic profile by HPLC-ESI-HRMS of the aqueous extract of the dried cascara highlighted 93 non-volatile molecules, the highest number reported for dried cascara. They belong to groups of organic acids (12), alkaloids (5), sugars (5), fatty acids (2), diglycerides (1), amino acids (18), phospholipids (7), vitamins (5), phenolic acids (11), flavonoids (8), chlorogenic acids (17), flavones (1) and terpenes (1). For the first time, we report the use of direct analysis in real-time mass spectrometry (DART-MS) for the identification of metabolites in aqueous extracts of dried cascara, parchment, silverskin and green coffee. The DART analysis mainly showed the presence of caffeine and chlorogenic acids in all the extracts; additionally, sugar adducts and antioxidant compounds such as polyphenols were detected. The mineral content (K, Ca, P, S, Mg and Cl) by EDS spectrometry in the by-products and green coffee showed a relatively high content of K in the dried cascara and green coffee, while Ca was detected in double quantity in the silverskin. These metabolomic and mineral profile data allow enhancement of the link between the quality of green coffee and its by-products and the traditional local practices in the crop-growing area. This consolidates the community’s experience in reusing by-products, thereby minimizing the impact on the environment and generating additional income for coffee growers’ work, in accordance with the principles of circular economy and bioeconomy. Full article

Weed infestations pose significant challenges to global crop production, demanding effective and sustainable weed control methods. Traditional approaches, such as chemical herbicides, mechanical tillage, and plastic mulches, are not only associated with environmental concerns but also face challenges like herbicide resistance, soil health, erosion, moisture content, and organic matter depletion. Thermal methods like flaming, streaming, and hot foam distribution are emerging weed control technologies along with directed energy systems of electrical and laser weeding. This paper conducts a comprehensive review of laser weeding technology, comparing it with conventional methods and highlighting its potential environmental benefits. Laser weeding, known for its precision and targeted energy delivery, emerges as a promising alternative to conventional control methods. This review explores various laser weeding platforms, discussing their features, applications, and limitations, with a focus on critical areas for improvement, including dwell time reduction, automated navigation, energy efficiency, affordability, and safety standards. Comparative analyses underscore the advantages of laser weeding, such as reduced environmental impact, minimized soil disturbance, and the potential for sustainable agriculture. This paper concludes by outlining key areas for future research and development to enhance the effectiveness, accessibility, and affordability of laser weeding technology. In summary, laser weeding presents a transformative solution for weed control, aligning with the principles of sustainable and environmentally conscious agriculture, and addressing the limitations of traditional methods. Full article

Weeds have a detrimental effect on crop yield. However, the prevailing chemical weed control methods cause pollution of the ecosystem and land. Therefore, it has become a trend to reduce dependence on herbicides; realize a sustainable, intelligent weed control method; and protect the land. In order to realize intelligent weeding, efficient and accurate crop and weed recognition is necessary. Convolutional neural networks (CNNs) are widely applied for weed and crop recognition due to their high speed and efficiency. In this paper, a multi-path input skip-residual network (SkipResNet) was put forward to upgrade the classification function of weeds and crops. It improved the residual block in the ResNet model and combined three different path selection algorithms. Experiments showed that on the plant seedling dataset, our proposed network achieved an accuracy of 95.07%, which is 0.73%, 0.37%, and 4.75% better than that of ResNet18, VGG19, and MobileNetV2, respectively. The validation results on the weed–corn dataset also showed that the algorithm can provide more accurate identification of weeds and crops, thereby reducing land contamination during the weeding process. In addition, the algorithm is generalizable and can be used in image classification in agriculture and other fields. Full article

Field robots are an important tool when improving the efficiency and decreasing the climatic impact of food production. Although several commercial field robots are available, the advantages, limitations, and optimal utilization methods of this technology are still not well understood due to its novelty. This study aims to evaluate the performance of a commercial field robot for seeding and weeding tasks. The evaluation was carried out in a 2-hectare sugar beet field. The robot’s performance was assessed by counting plants and weeds using image processing. The YOLOv8 model was trained to detect sugar beets and weeds. The plant and weed densities were compared on a robotically weeded area of the field, a chemically weeded control area, and an untreated control area. The average weed density on the robotically treated area was about two times lower than that on the untreated area and about three times higher than on the chemically treated area. The testing robot in the specific testing environment and mode showed intermediate results, weeding a majority of the weeds between the rows; however, it left the most harmful weeds close to the plants. Software for robot performance assessment can be used for monitoring robot performance and plant conditions several times during plant growth according to the weeding frequency. Full article

Eriochloa villosa (Thunb.) Kunth, the main weed in corn fields, has gradually developed resistance to nicosulfuron due to continuous and extensive application. We identified a biotype showing resistance to ALS inhibitor nicosulfuron with a resistant index 13.83, but without any target spot mutation. Herein, transcriptome sequencing was used to analyze the differences in gene expression at the transcriptional level between nicosulfuron-resistant E. villosa HEK-40 varieties and sensitive E. villosa HEK-15 varieties. The resistant and sensitive varieties comparison revealed 9931 DEGs after nicosulfuron application, of which 5426 and 4505 genes were up-regulated and down-regulated, respectively. Some contigs related to metabolic resistance were identified based on differential expression via RNA-Seq, which includes ABC transporters (ko02010), glucosinolate biosynthesis (ko00966), 2-oxocarboxylic acid metabolism (ko01210), alanine, aspartate, and glutamate metabolism pathways (ko00250). Seven CYP450 genes, four GST genes, ten ABC transporter genes, and two GT genes related to metabolic resistance were identified. The 10 candidate genes screened were validated using q-PCR. This validation indicates that activities associated with P450 enzymes, ABC transporters, and glutathione S-transferases (GST) may play a role in conferring resistance, which is important for reducing the impact of weeds on corn fields and ensuring food security. Full article

Given the increasing food needs of humanity and the challenges cultivated species face in adapting to the climatic uncertainties we experience, it is urgent to cultivate new species. A highly relevant repertoire for this purpose is offered by the array of edible wild plants. We analyzed data from Murcia (Spain), involving 61 species and 59 informants, and the Global Database of Wild Food Plants, which includes 15,000 species, 500 localities, and nearly 700 references. Using local consensus, global distribution, and GBIF occurrence data, we built simple unimodal or bimodal models to explore their limitations. Our study highlights that approximately 15,000 wild or feral plant species are consumed as food, underlining the urgent need to support existing crops with new species due to current food crises and climate irregularities. We examined wild plant diversity from a horticultural perspective, considering their relationships with weeds and invasive species. Partial criteria, such as local consensus or global use, were found insufficient for selecting candidate species. We propose developing a specific artificial intelligence to integrate various factors—ecological, nutritional, toxicological, agronomic, biogeographical, ethnobotanical, economic, and physiological—to accurately model a species’ potential for domestication and cultivation. We propose the necessary tools and a protocol for developing this AI-based model. Full article

Urban and storm water retention ponds eventually become eutrophic after years of receiving runoff water. In 2020, a novel biological and chemical treatment was initiated to remove accumulated nutrients from an urban retention pond that had severe algae and weed growth. Our approach installed two 6.1 m × 6.1 m floating treatment wetlands (FTWs) and two airlift pumps that contained slow-release lanthanum composites, which facilitated phosphate precipitation. Four years of treatment (2020–2023) resulted in median nitrate-N concentrations decreasing from 23 µg L⁻¹ in 2020 to 1.3 µg L⁻¹ in 2023, while PO₄-P decreased from 42 µg L⁻¹ to 19 µg L⁻¹. The removal of N and P from the water column coincided with less algae, weeds, and pond muck (sediment), and greater dissolved oxygen (DO) concentrations and water clarity. To quantify the sustainability of this bio-chemical approach, we focused on quantifying nitrate removal rates beneath FTWs. By enclosing quarter sections (3.05 × 3.05 m) of the field-scale FTWs inside vinyl pool liners, nitrate removal rates were measured by spiking nitrate into the enclosed root zone. The first field experiment showed that DO concentrations inside the pool liners were well below the ambient values of the pond (<0.5 mg/L) and nitrate was quickly removed. The second field experiment quantified nitrate loss under a greater range of DO values (<0.5–7 mg/L) by including aeration as a treatment. Nitrate removal beneath FTWs was roughly one-third less when aerated versus unaerated. Extrapolating experimental removal rates to two full-sized FTWs installed in the pond, we estimate between 0.64 to 3.73 kg of nitrate-N could be removed over a growing season (May–September). Complementary laboratory mesocosm experiments using similar treatments to field experiments also exhibited varying nitrate removal rates that were dependent on DO concentrations. Using an average annual removal rate of 1.8 kg nitrate-N, we estimate the two full-size FTWs could counter 14 to 56% of the annual incoming nitrate load from the contributing watershed. Full article

Annual ryegrass (Lolium rigidum) is a problematic weed in winter crops and fallows in the southeastern cropping region (SCR) of Australia. This weed has evolved resistance to multiple herbicide groups, globally. In Australia, L. rigidum is more prevalent in the western and southern regions than in SCR. To assess the herbicide resistance status of L. rigidum, the response of five L. rigidum populations (collected from the SCR) to glyphosate, glufosinate, paraquat, haloxyfop-P-ethyl, and clethodim is determined using dose–response curves. Three parametric logistic models are used to determine the herbicide dose required to achieve 50% survival (LD₅₀) and 50% growth reduction (GR₅₀). The LD₅₀ values for 50% survival at 28 days after treatment range from 1702 g a.e. ha⁻¹ to 8225 g a.e. ha⁻¹ for glyphosate, 1637 g a.i. ha⁻¹ to 1828 g a.i. ha⁻¹ for glufosinate, 141 g a.i. ha⁻¹ to 307 g a.i. ha⁻¹ for paraquat, 11 g a.i. ha⁻¹ to 107 g a.i. ha⁻¹ for haloxyfop-P-ethyl, and 17 g a.i. ha⁻¹ to 48 g a.i. ha⁻¹ for clethodim. The resistance factor, based on GR₅₀ value, is highest in the S7 population (2.2 times) for glyphosate, the S11 population (2.3 times) for glufosinate, the S11 population (2.0 time) for paraquat, the S7 population (3.9 times) for haloxyfop-P-ethyl, and the S3 population (3.1 times) for clethodim, compared with the susceptible or less tolerant population. The S11 population is found to be resistant to five tested herbicides, based on resistance factors. Similarly, the S3 population is highly resistant to glyphosate, haloxyfop-P-ethyl, and clethodim compared with the W4 population. These results suggest that L. rigidum populations in the SCR exhibit resistance to multiple herbicide groups at labelled field rates. The findings highlight the necessity of adopting an integrated management approach, including the use of residual herbicides, tank mixing herbicides with different modes of action, and rotating herbicides in conjunction with cultural and mechanical control methods. Full article

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R² is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding. Full article

Spontaneous vegetation within a managed green space is often regarded as unwelcoming and insignificant weeds. This perception is still deep-rooted among green-space managers and the general public worldwide; they are generally uncertain about the management needs after allowing these groups of flora to take root. The short-term growth dynamics of both spontaneous and planted vegetation should be analyzed, and a widely acceptable, feasible management plan to balance aesthetic and ecological functions should be formulated with the backing of data and analysis for such fast-growing flora in tropical and subtropical regions. A manicured, extensive green roof with only seven (two native, five exotic) plant species was transformed into a renaturalized biotope by replacing 15 native ferns and forb species over 15 months. After planting, a baseline plant survey was conducted, with 54 plant species representing spontaneous growth and 14 planted species alive (7 planted native species survived, plus 7 species planted prior to renaturalization revived). Three quarterly plant surveys recorded the cover-abundance of each species, and the growth dynamics of the planted and spontaneous plant species were evaluated over the first year of study. During each quarterly survey, the number of planted and spontaneous plant species remained stable (ranging from 14 to 16 species and 51 to 54 species, respectively), with a constant turnover of 11 to 12 die-out species and 11 to 12 newly colonized or revived species. Plant coverage of different plant forms fluctuated slightly (within 7%) in the quarterly surveys according to seasonal changes, except for ferns, which outperformed (12% increase in coverage in a year) all the other plant forms. The height of the planted vegetation fluctuated in a year, being shorter during the summer, while the height of spontaneous vegetation remained stable throughout the year, exhibiting resilience to scouring heat. The seasonal growth tendencies of both planted and spontaneous plants were illustrated in relation to their species ranks, and further hierarchical cluster analysis was conducted for the clustering of spontaneous species. Their differential growth patterns provided comprehensive information or supported decisions regarding plant selection and maintenance, which is a scientific novelty within this unexplored topic. Management recommendations based on the findings were suggested to fulfill both aesthetic and ecological needs. Species with stable and less stable growth patterns could be useful to meet maintenance efficiency and biodiversity enhancement needs, respectively. These findings provide insights to form guiding principles for choosing plant species for renaturalization projects. Full article