Design, Synthesis and Proteomics-Based Analysis of Novel Triazinone Derivatives Containing Amide Structures as Safer Protoporphyrinogen IX Oxidase Inhibitors.

Resistant weeds severely threaten crop yields as they compete with crops for resources required for survival. Trifludimoxazin, a protoporphyrinogen IX oxidase (PPO) inhibitor, can effectively control resistant weeds. However, its crop safety record is unsatisfactory. Consequently, a scaffold-hopping strategy is employed in this study to develop a series of new triazinone derivatives featuring an amide structure. Most compounds depicted excellent herbicidal activity across a broad spectrum at 37.5-150 g ai/ha, among which (R)-I-5 was equivalent to flumioxazin. (R)-I-5 demonstrated significant crop tolerance to rice and wheat, even at 150 g ai/ha. (R)-I-5 exhibited superior pharmacokinetic features compared to flumioxazin and trifludimoxazin. This was depicted by the absorption, distribution, metabolism, excretion, and toxicity predictions. Notably, proteomics-based analysis was applied for the first time to investigate variations among plant proteins before and after herbicide application, shedding light on the conservative and divergent roles of PPO.

Organic farming practices increase weed density and diversity over conventional practices: A meta-analysis.

Population growth and climate change challenge our food and farming systems and provide arguments for an increased intensification of agriculture. Organic farming has been seen as a promising option due to its eco-friendly approaches during production. However, weeds are regarded as the major hindrance to effective crop production which varies depending on the type of crop and spacing. Their presence leads to reduced yield, increase in harvest cost and lower the qualities of some produce. Thus, weed management is a key priority for successful crop production. Therefore, we conducted a meta-analysis from published studies to quantify possible differences on weed density, diversity and evenness in organic and conventional farming systems and best intervention for weed management in organic farming system. Data included were obtained from 32 studies where 31 studies with 410 observations were obtained for weed density, 15 studies with 168 observations for diversity, and 5 studies with 104 observations for evenness. Standard deviation of mean was obtained from the studies, log transformed using natural logarithms and the effect size pooled using standardized mean difference (SMD). Publication bias was determined through funnel plot. Results showed that organic farming has significant higher weed density (P < 0.01), diversity (P = 0.01), and evenness (P < 0.05) compared to conventional farming. Despite so, diversified crop rotation has been proved to reduce weed density in organic farming by up to 49 % while maize-bean intercropping decrease densities of Amaranthus ssp, Cyperus ssp and Cammelina ssp compared with monocropping. Use of mulch after one hand weeding was found to control up to 98 % of weeds and use of cover crop between 24 % and 85 % depending on the type of the cover crop. The study results show that organic farming encourages high weed density, diversity and evenness but use of the integrated approaches can help to maintain weed density at a manageable level.

An intelligent spraying system for weeds in wheat fields based on a dynamic model of droplets impacting wheat leaves.

Introduction: Targeted herbicide application refers to precise application of herbicides in weed-infested areas according to the location and density of farmland weeds. At present, targeted herbicide application in wheat fields generally faces problems including the low herbicide adhesion rate, leading to omission and excessive loss of herbicides. Methods: To solve these problems, changes in the impact force of herbicide and the weed leaves in the operation process of a spraying system were studied from the interaction between weeds and herbicides applied. A dynamic model of weed leaves was established. On this basis, the research indicated that the herbicide adhesion rate is highest under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s when the spray height is 300 mm. To study the dynamic deformation of weed leaves and the distribution of liquid herbicides in the external flow field under weed-herbicide interaction, a dynamic simulation model of herbicide application was built using the finite element method. Results and Discussion: The results show that when the spray height is 300 mm, the maximum weed leaf deformation index (LDI) is 0.43 and the velocity in the external flow field is 0 m/s under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s. This finding indicates that the herbicide is not splashed elsewhere and the turbulence intensity in the weed area is 2%, implying steady flow of the herbicide, most of which can be retained on weed leaves. Field test results of application quality of the herbicide show that the maximum LDI is 0.41 and the coverage of the herbicide in the sheltered area below the leaves is 19.02% when the spraying pressure is 0.4 MPa, flow rate is 0.011 kg/s, and spray height is 300 mm. This solves the problem of a low rate of utilization of herbicides because the herbicide passes through weed plants, and achieves the precision herbicide application in wheat fields.

Does weed diversity mitigate yield losses?

While intensive control of weed populations plays a central role in current agriculture, numerous studies highlight the multifaceted contribution of weeds to the functionality and resilience of agroecosystems. Recent research indicates that increased evenness within weed communities may mitigate yield losses in contrast to communities characterized by lower diversity, since weed species that strongly affect crop yields, also dominate weed communities, with a concurrent reduction of evenness. If confirmed, this observation would suggest a paradigm shift in weed management towards promoting higher community diversity. To validate whether the evenness of weed communities is indeed linked to higher crop productivity, we conducted two field experiments: one analyzing the effects of a natural weed community in an intercrop of faba bean and oat, and the other analyzing the effects of artificially created weed communities, together with the individual sown weed species, in faba bean, oats and an intercrop of both crops. The evenness of the weed communities ranged from 0.2 to 0.9 in the natural weed community, from 0.2 to 0.7 in faba bean, from 0 to 0.8 in the intercrop and from 0.3 to 0.9 in oats. Neither the natural nor the artificial weed community showed significant effects of evenness on crop grain yield or crop biomass. The results of this study do not validate a positive relationship of crop productivity and weed evenness, possibly due to low weed pressure and the absence of competitive effects but suggest that also less diverse weed communities may be maintained without suffering yield losses. This is expected to have far reaching implications, since not only diverse weed communities, but also higher abundances of few weed species may contribute to ecosystem functions and may support faunal diversity associated with weeds.

An investigation of the pigments, antioxidants and free radical scavenging potential of twenty medicinal weeds found in the southern part of Bangladesh.

Despite their overlooked status, weeds are increasingly recognized for their therapeutic value, aligning with historical reliance on plants for medicine and nutrition. This study investigates the medicinal potential of native weed species in Bangladesh, specifically pigments, antioxidants, and free radical scavenging abilities. Twenty different medicinal weed species were collected from the vicinity of Khulna Agricultural University and processed in the Crop Botany Department Laboratory. Pigment levels were determined using spectrophotometer analysis, and phenolics, flavonoids, and DPPH were quantified accordingly. Chlorophyll levels in leaves ranged from 216.70 ± 9.41 to 371.14 ± 28.67 µg g-1 FW, and in stems from 51.98 ± 3.21 to 315.89 ± 17.19 µg g-1 FW. Flavonoid content also varied widely, from 1,624.62 ± 102.03 to 410.00 ± 115.58 mg CE 100 g-1 FW in leaves, and from 653.08 ± 32.42 to 80.00 ± 18.86 mg CE 100 g-1 FW in stems. In case of phenolics content Euphorbia hirta L. displaying the highest total phenolic content in leaves (1,722.33 ± 417.89 mg GAE 100 g-1 FW) and Ruellia tuberosa L. in stems (977.70 ± 145.58 mg GAE 100 g-1 FW). The lowest DPPH 2.505 ± 1.028 mg mL-1was found in Heliotropium indicum L. leaves. Hierarchical clustering links species with pigment, phenolic/flavonoid content, and antioxidant activity. PCA, involving 20 species and seven traits, explained 70.07% variability, with significant PC1 (14.82%) and PC2 (55.25%). Leaves were shown to be superior, and high-performing plants such as E. hirta and H. indicum stood out for their chemical composition and antioxidant activity. Thus, this research emphasizes the value of efficient selection while concentrating on the therapeutic potential of native weed species.

Microbial Bioherbicides Based on Cell-Free Phytotoxic Metabolites: Analysis and Perspectives on Their Application in Weed Control as an Innovative Sustainable Solution.

Weeds cause significant agricultural losses worldwide, and herbicides have traditionally been the main solution to this problem. However, the extensive use of herbicides has led to multiple cases of weed resistance, which could generate an increase in the application concentration and consequently a higher persistence in the environment, hindering natural degradation processes. Consequently, more environmentally friendly alternatives, such as microbial bioherbicides, have been sought. Although these bioherbicides are promising, their efficacy remains a challenge, as evidenced by their limited commercial and industrial production. This article reviews the current status of microbial-based bioherbicides and highlights the potential of cell-free metabolites to improve their efficacy and commercial attractiveness. Stirred tank bioreactors are identified as the most widely used for production-scale submerged fermentation. In addition, the use of alternative carbon and nitrogen sources, such as industrial waste, supports the circular economy. Furthermore, this article discusses the optimization of downstream processes using bioprospecting and in silico technologies to identify target metabolites, which leads to more precise and efficient production strategies. Bacterial bioherbicides, particularly those derived from Pseudomonas and Xanthomonas, and fungal bioherbicides from genera such as Alternaria, Colletotrichum, Trichoderma and Phoma, show significant potential. Nevertheless, limitations such as their restricted range of action, their persistence in the environment, and regulatory issues restrict their commercial availability. The utilization of cell-free microbial metabolites is proposed as a promising solution due to their simpler handling and application. In addition, modern technologies, including encapsulation and integrated management with chemical herbicides, are investigated to enhance the efficacy and sustainability of bioherbicides.

Weed detection and recognition in complex wheat fields based on an improved YOLOv7.

Introduction: The precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YOLOv7 based on an improved YOLOv7 architecture was proposed to identify five types of weeds in complex wheat fields. Methods: First, a dataset was constructed for five weeds that are commonly found, namely, Descurainia sophia, thistle, golden saxifrage, shepherd's purse herb, and Artemisia argyi. Second, a wheat weed detection model called CSCW-YOLOv7 was proposed to achieve the accurate identification and classification of wheat weeds. In the CSCW-YOLOv7, the CARAFE operator was introduced as an up-sampling algorithm to improve the recognition of small targets. Then, the Squeeze-and-Excitation (SE) network was added to the Extended Latent Attention Networks (ELAN) module in the backbone network and the concatenation layer in the feature fusion module to enhance important weed features and suppress irrelevant features. In addition, the contextual transformer (CoT) module, a transformer-based architectural design, was used to capture global information and enhance self-attention by mining contextual information between neighboring keys. Finally, the Wise Intersection over Union (WIoU) loss function introducing a dynamic nonmonotonic focusing mechanism was employed to better predict the bounding boxes of the occluded weed. Results and discussion: The ablation experiment results showed that the CSCW-YOLOv7 achieved the best performance among the other models. The accuracy, recall, and mean average precision (mAP) values of the CSCW-YOLOv7 were 97.7%, 98%, and 94.4%, respectively. Compared with the baseline YOLOv7, the improved CSCW-YOLOv7 obtained precision, recall, and mAP increases of 1.8%, 1%, and 2.1%, respectively. Meanwhile, the parameters were compressed by 10.7% with a 3.8-MB reduction, resulting in a 10% decrease in floating-point operations per second (FLOPs). The Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method suggested that the CSCW-YOLOv7 can learn a more representative set of features that can help better locate the weeds of different scales in complex field environments. In addition, the performance of the CSCW-YOLOv7 was compared to the widely used deep learning models, and results indicated that the CSCW-YOLOv7 exhibits a better ability to distinguish the overlapped weeds and small-scale weeds. The overall results suggest that the CSCW-YOLOv7 is a promising tool for the detection of weeds and has great potential for field applications.

Young adult retail purchases of cannabis, product category preferences and sales trends in California 2018-21: Differences compared with older adults.

AIMS: The aim of this study is to identify cannabis products according to their appeal among young adults and measure product sales trends. DESIGN, SETTING AND PARTICIPANTS: This was a retrospective comparative study using point-of-sale data from licensed recreational cannabis retailers that include buyer age with birth year entered by retailers, set in California, USA. Cannabis purchases by young adults (aged 21-24, GenZ) were compared with older adults (age 25+) over 4 years (2018-21). MEASUREMENTS: Sales for six cannabis product categories were analyzed using a commercial data set with imputations and a raw data set. Age-appeal metrics were dollar and unit sales to young adults, and dollar and unit share ratios (young adults/older adults), where a share ratio of 100 denotes age-appeal comparability. A product category was considered more young-adult appealing than others if its mean on a metric was at least one standard deviation above the grand mean across all product categories. FINDINGS: Flower (cannabis plant material) and vapor pen appealed to young adults based on absolute dollar sales, dominating young-adult spending compared with other cannabis products (37.24 and 31.83%, respectively). Vapor pen and concentrate appealed to young adults based on dollar share ratios of 152, meaning these products comprised a 52% greater share of young-adult cannabis spending relative to older-adult spending (31.83/20.97% and 10.47/6.88%, respectively). Less appealing to young adults were pre-roll, edible/beverage and absorbable products (tincture/sublingual, capsule and topical). Flower showed the largest dollar sales growth (B = +$3.50 million/month), next to vapor pen (B = +$1.55 million/month). Vapor pen tied for highest growth in the percent of product dollars from the largest package size (B = +0.85%/month) and showed the steepest price decline (B = -0.53 price per gram/month). CONCLUSIONS: In California, USA, from 2018 to 2021, relative to older adults, young adults spent a greater share of their cannabis dollars on vapor pen and concentrate (products with high potency of delta-9-tetrahydrocannabinol).

YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion.

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields.

A 4-year field study monitoring the evolution of Trp574Leu-resistant plants in an Echinochloa crus-galli population under different crop rotation and herbicide programs in maize.

BACKGROUND: A 4-year experiment evaluated the effects of different integrated weed management (IWM) programs on the evolution of a Echinochloa crus-galli population resistant to acetolactate synthase (ALS) inhibitors in a maize cropping system. The programs included the continued use of ALS inhibitors, mixing them with alternative herbicides, or without ALS-inhibitors, in all cases under maize monocrop or a biennial crop rotation. RESULTS: IWM programs that relied solely on non-ALS-inhibitors usually achieved high control levels across years (> 90%). Additionally, Trp574Leu-resistant plants became prevalent (> 90%) in programs only using ALS inhibitors, while in the rest the frequency of susceptible plants did not substantially decrease below 40%. Regarding the other monitored grass weeds, Digitaria sanguinalis and Panicum dichotomiflorum were effectively controlled in programs using ALS-inhibitors without soybean rotation or in programs without ALS-inhibitors altogether, excepting the program relying on an 4-hydroxyphenylpyruvate dioxygenase (HPPD)-inhibitor under maize monocrop for the latter species (0%). CONCLUSION: At the end of the experiment, the only IWM programs that reduced infestation levels were the one without ALS-inhibitors under soybean rotation, and the one with standard pre-emergence treatments. These findings highlight the effectiveness of crop rotation and alternative herbicides both pre- or post-emergence in controlling E. crus-galli. ALS-inhibitors, while challenged by resistance in E. crus-galli, remain valuable tools for managing other grass weed species in maize. It is crucial to adapt IWM strategies for herbicide-resistant E. crus-galli and other grass weed populations to mitigate the further evolution of resistance. © 2024 Corteva Agriscience. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Chitosan Coating as a Strategy to Increase Postemergent Herbicidal Efficiency and Alter the Interaction of Nanoatrazine with Bidens pilosa Plants.

The atrazine nanodelivery system, composed of poly(ε-caprolactone) (PCL+ATZ) nanocapsules (NCs), has demonstrated efficient delivery of the active ingredient to target plants in previous studies, leading to greater herbicide effectiveness than conventional formulations. Established nanosystems can be enhanced or modified to generate new biological activity patterns. Therefore, this study aimed to evaluate the effect of chitosan coating of PCL+ATZ NCs on herbicidal activity and interaction mechanisms with Bidens pilosa plants. Chitosan-coated NCs (PCL/CS+ATZ) were synthesized and characterized for size, zeta potential, polydispersity, and encapsulation efficiency. Herbicidal efficiency was assessed in postemergence greenhouse trials, comparing the effects of PCL/CS+ATZ NCs (coated), PCL+ATZ NCs (uncoated), and conventional atrazine (ATZ) on photosystem II (PSII) activity and weed control. Using a hydroponic system, we evaluated the root absorption and shoot translocation of fluorescently labeled NCs. PCL/CS+ATZ presented a positive zeta potential (25 mV), a size of 200 nm, and an efficiency of atrazine encapsulation higher than 90%. The postemergent herbicidal activity assay showed an efficiency gain of PSII activity inhibition of up to 58% compared to ATZ and PCL+ATZ at 96 h postapplication. The evaluation of weed control 14 days after application ratified the positive effect of chitosan coating on herbicidal activity, as the application of PCL/CS+ATZ at 1000 g of a.i. ha-1 resulted in better control than ATZ at 2000 g of a.i. ha-1 and PCL+ATZ at 1000 g of a.i. ha-1. In the hydroponic experiment, chitosan-coated NCs labeled with a fluorescent probe accumulated in the root cortex, with a small quantity reaching the vascular cylinder and leaves up to 72 h after exposure. This behavior resulted in lower leaf atrazine levels and PSII inhibition than ATZ. In summary, chitosan coating of nanoatrazine improved the herbicidal activity against B. pilosa plants when applied to the leaves but negatively affected the root-to-shoot translocation of the herbicide. This study opens avenues for further investigations to improve and modify established nanosystems, paving the way for developing novel biological activity patterns.

Crofton weed derived isomers of ageraphorone as potent antifeedant against Plutella xylostella (L.).

Global agricultural production is significantly hampered by insect pests, and the demand for natural pragmatic pesticides with environmental concern remains unfulfilled. Ageratina adenophora (Spreng.) also known as Crofton weed, is an invasive perennial herbaceous plant that is known to possess multiple bioactive compounds. In our study, two isomers of ageraphorone metabolites i.e, 10 Hα-9-oxo-ageraphorone (10HA) and 10 Hß-9-oxo-ageraphorone (10HB), were identified from Crofton weed, exhibiting potent antifeedant and larvicidal activities against Plutella xylostella. For antifeedant activity, the median effective concentration (EC50) values for 10HA and 10HB in the choice method were 2279 mg/L and 3233 mg/L, respectively, and for the no choice method, EC50 values were 1721 mg/L and 2394 mg/L, respectively. For larvicidal activity, lethal concentration (LC50) values for 10HA and 10HB were 2421 mg/L and 4109 mg/L at 48 h and 2101 mg/L and 3550 mg/L at 72 h. Furthermore, both in- vivo and in-vitro studies revealed that the isomers 10HA and 10HB exhibited potent detoxifying enzymes inhibition activity such as carboxylesterase and glutathione S-transferases. Molecular docking and MD simulation analysis provide insight into the possible interaction between isomers of ageraphorone metabolites and Carboxylic Ester Hydrolase protein (Gene: pxCCE016b) of P. xylostella, which led to a finding that CarEH protein plays a significant role in the detoxification of the two compounds in P. xylostella. Finally, our findings show that the primary enzymes undergoing inhibition by isomers of ageraphorone metabolites, causing toxicity in insects, are Carboxylesterase and glutathione S-transferase.

Non-chemical weed management: Harnessing flame weeding for effective weed control.

The goal of the current study was to create and assess the effectiveness of a hand-pulled ergonomically designed flame weeder. The developed weeder was tested in the field at three operating pressures (20, 30 and 40 Psi) and forward speeds (1.00, 1.25 and 1.50 km/h) to study their effects on plant damage, survival rates, weight preservation rates, weed management effectiveness, soil temperatures, and gas and energy consumption. Thereafter, at optimized values of forward speed and operating pressure, a comparative assessment of flame weeding with traditional methods (mechanical and manual weeding) was done in terms of weed control effectiveness, operational time, energy consumption, and cost of operation. Results showed that the optimal performance of the designed flame weeder was achieved when operated at a speed of 1 km/h and an operating pressure of 40 psi. The survival rate, weight preservation rate, weed control efficiency, change in soil temperature, recovery rate, plant damage, gas consumption, and energy consumption were observed to be 27.3 %, 32.5 %, 91.1 %, 40.74 °C, 8.5 %, 2.2 %, 4.05 kg/h, and 2500.24 MJ/ha, respectively, at optimized values of forward speed (1.00 km/h) and operating pressure (40 Psi). The actual field capacity, field efficiency and operating cost of the flame weeder were 0.0755 ha/h, 94.94 %, and 3620.81 ₹/ha, respectively. Hand weeding had the best level of weed control effectiveness, but it was a laborious, time-consuming process. When compared to manual weeding, flame weeding was 50.42 % cheaper and 94.82 % faster.

Evaluating the effects of manual hoeing and selective herbicides on maize (Zea mays L.) productivity and profitability.

The objective of this study was to evaluate maize production and the economic profitability of weed management techniques. Field trials were conducted at the Kasapa farm during the 2021/22 growing seasons using a split-plot design with three repetitions. The main factor was the herbicides applied in pre-emergence alone (2L ha-1: acetochlor, bentazon, imazethapyr and 60 g ha-1 chlorimuron-ethyl), then mixed (1L ha-1: acetochlor plus bentazon plus imazethapyr plus 30g ha-1chlorimuron-ethyl), manual hoeing (3-5WAS) including the non-weeding. The secondary factor: maize varieties (GV672A, GV673A, GV664A and Sam4vita). The highest maize dry grain yield (7.66 t ha-1) was associated with imazethapyr, while those of acetochlor and chlorimuron-ethyl (6.86 and 6.92 t ha-1) compared to manual hoeing (7.62 t ha-1, respectively) were low, but much higher than no weeding (1.21 t ha-1). The yields of varieties GV672A and GV664A were higher (6.87 and 6.77 t ha-1), compared to Sam4vita (5.64 t ha-1). The total dry weight of weeds was negatively correlated with all crop parameters, with its maximum value (127.56 g m-2) characterizing non-weeding, and the minimum for manual hoeing (18.83 g m-2). The Ratio Cost Value showed that all treatments were profitable: imazethapyr > bentazon > chlorimuron-ethyl > combination > acetochlor > manual hoeing. However, imazethapyr was economically more profitable and could replace manual hoeing when the field to be weeded increases and labor is scarce.

Can genomic signatures guide the selection of host-specific agents for weed biological control?

Biological control of weeds involves deliberate introduction of host-specific natural enemies into invaded range to reduce the negative impacts of invasive species. Assessing the specificity is a crucial step, as introduction of generalist natural enemies into a new territory may pose risks to the recipient communities. A mechanistic understanding of host use can provide valuable insights for the selection of specialist natural enemies, bolster confidence in non-target risk assessment and potentially accelerate the host specificity testing process in biological control. We conducted a comprehensive analysis of studies on the genomics of host specialization with a view to examine if genomic signatures can help predict host specificity in insects. Focusing on phytophagous Lepidoptera, Coleoptera and Diptera, we compared chemosensory receptors and enzymes between "specialist" (insects with narrow host range) and "generalist" (insects with wide host range) insects. The availability of genomic data for biological control agents (natural enemies of weeds) is limited thus our analyses utilized data from pest insects and model organisms for which genomic data are available. Our findings revealed that specialists generally exhibit a lower number of chemosensory receptors and enzymes compared with their generalist counterparts. This pattern was more prominent in Coleoptera and Diptera relative to Lepidoptera. This information can be used to reject agents with large gene repertoires to potentially accelerate the risk assessment process. Similarly, confirming smaller gene repertoires in specialists could further strengthen the risk evaluation. Despite the distinctive signatures between specialists and generalists, challenges such as finite genomic data for biological control agents, ad hoc comparisons, and fewer comparative studies among congeners limit our ability to use genomic signatures to predict host specificity. A few studies have empirically compared phylogenetically closely related species, enhancing the resolution and the predictive power of genomics signatures thus suggesting the need for more targeted studies comparing congeneric specialists and generalists.

Improvement of the YOLOv8 Model in the Optimization of the Weed Recognition Algorithm in Cotton Field.

Due to the existence of cotton weeds in a complex cotton field environment with many different species, dense distribution, partial occlusion, and small target phenomena, the use of the YOLO algorithm is prone to problems such as low detection accuracy, serious misdetection, etc. In this study, we propose a YOLOv8-DMAS model for the detection of cotton weeds in complex environments based on the YOLOv8 detection algorithm. To enhance the ability of the model to capture multi-scale features of different weeds, all the BottleNeck are replaced by the Dilation-wise Residual Module (DWR) in the C2f network, and the Multi-Scale module (MSBlock) is added in the last layer of the backbone. Additionally, a small-target detection layer is added to the head structure to avoid the omission of small-target weed detection, and the Adaptively Spatial Feature Fusion mechanism (ASFF) is used to improve the detection head to solve the spatial inconsistency problem of feature fusion. Finally, the original Non-maximum suppression (NMS) method is replaced with SoftNMS to improve the accuracy under dense weed detection. In comparison to YOLO v8s, the experimental results show that the improved YOLOv8-DMAS improves accuracy, recall, mAP0.5, and mAP0.5:0.95 by 1.7%, 3.8%, 2.1%, and 3.7%, respectively. Furthermore, compared to the mature target detection algorithms YOLOv5s, YOLOv7, and SSD, it improves 4.8%, 4.5%, and 5.9% on mAP0.5:0.95, respectively. The results show that the improved model could accurately detect cotton weeds in complex field environments in real time and provide technical support for intelligent weeding research.

GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat.

Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.

Multifunctional Adjuvants Affect Sulfonylureas with Synthetic Auxin Mixture in Weed and Maize Grain Yield.

A field study in the years 2017-2019 was carried out to evaluate the impact of novel adjuvant formulations on the efficacy of sulfonylurea and synthetic auxin herbicides. Treatments included nicosulfuron + rimsulfuron + dicamba (N+R+D) at full and reduced rates with three multicomponent (TEST-1, TEST-2, TEST-3) as well as standard (MSO, S) adjuvants. In this greenhouse study, Echinochloa crus-galli seeds were planted and treated with N+R+D at 2-3 leaf stages. The water with the desired pH (4, 7, and 9) for the preparation of the spray liquid was prepared by incorporating citric acid or K3PO4 to either lower or raise the pH of the water. Adjuvant TEST-1 added to the spray liquid at pH 4 increased the effectiveness to 68%, TEST-2 to 81%, and TEST-3 to 80%, compared to 73% and 66% with the MSO and S. The efficacy of N+R+D at pH 7 with TEST-1 increased to 83%, TEST-2 to 82%, and TEST-3 to 77%, but with MSO, it increased to 81%, and 71% with S. Adjuvants TEST-1, TEST-2, and TEST-3 in the liquid at pH 9 increased efficacy to 76 and 80%, compared to 79 and 63% with MSO or S adjuvants. N+R+D applied with TEST-1, TEST-2, and TEST-3 provided greater weed control than herbicides with surfactant (S) and similar or even better than with standard methylated seed oil (MSO) adjuvants. Maize grain yield after herbicide-with-tested-adjuvant application was higher than from an untreated check, and comparable to yield from herbicide-with-MSO treatment, but higher than from S treatment.

Response of Tahitian Bridal Veil (Gibasis pellucida) and Small-Leaf Spiderwort (Tradescantia fluminensis) to Postemergence Herbicides under Greenhouse Conditions.

Tahitian bridal veil (Gibasis pellucida) and small-leaf spiderwort (Tradescantia fluminensis) are both invasive species in natural areas throughout Florida. However, very little is known regarding herbicide control. To provide land managers with herbicidal control options for both species, postemergence herbicides were evaluated for efficacy in a greenhouse to identify herbicide options that control both species under similar settings. Four herbicides, including triclopyr acid, triclopyr amine + 2,4-D amine, triclopyr amine, and glufosinate were applied at standard label rates and compared to a non-treated control group for efficacy. Visual control ratings were taken at 2, 4, and 8 weeks after treatment (WAT), and shoot dry weights (WAT 8) and regrowth dry weights (WAT 12) were determined. Triclopyr (acid and amine) generally provided the most consistent control of both species as evidenced by the visual control ratings and shoot dry weight data which showed reductions of 76% to 89% in shoot biomass at trial conclusion. Triclopyr + 2,4-D reduced shoot dry weights by 52% to 54% and was the least effective when considering the control of both species.

Twindemic Threats of Weeds Coinfected with Tomato Yellow Leaf Curl Virus and Tomato Spotted Wilt Virus as Viral Reservoirs in Tomato Greenhouses.

Tomato yellow leaf curl virus (TYLCV) and tomato spotted wilt virus (TSWV) are well-known examples of the begomovirus and orthotospovirus genera, respectively. These viruses cause significant economic damage to tomato crops worldwide. Weeds play an important role in the ongoing presence and spread of several plant viruses, such as TYLCV and TSWV, and are recognized as reservoirs for these infections. This work applies a comprehensive approach, encompassing field surveys and molecular techniques, to acquire an in-depth understanding of the interactions between viruses and their weed hosts. A total of 60 tomato samples exhibiting typical symptoms of TYLCV and TSWV were collected from a tomato greenhouse farm in Nonsan, South Korea. In addition, 130 samples of 16 different weed species in the immediate surroundings of the greenhouse were collected for viral detection. PCR and reverse transcription-PCR methodologies and specific primers for TYLCV and TSWV were used, which showed that 15 tomato samples were coinfected by both viruses. Interestingly, both viruses were also detected in perennial weeds, such as Rumex crispus, which highlights their function as viral reservoirs. Our study provides significant insights into the co-occurrence of TYLCV and TSWV in weed reservoirs, and their subsequent transmission under tomato greenhouse conditions. This project builds long-term strategies for integrated pest management to prevent and manage simultaneous virus outbreaks, known as twindemics, in agricultural systems.