Practical Applications In Agriculture Research Articles

Leveraging Unmanned Aerial Vehicle Technologies to Facilitate Precision Water Management in Smallholder Farms: A Scoping Review and Bibliometric Analysis

While there is immense potential in using unmanned aerial vehicles (UAVs) to facilitate precision water management, there is currently no consensus on practical strategies to operationally implement these technologies to guide water resources management decisions, particularly within smallholder farming contexts. To address this gap, this study employs bibliometric techniques to assess the current state of UAV applications for evapotranspiration (ET) estimation in agricultural settings. The analysis of 49 peer-reviewed papers from Scopus was conducted using Biblioshiny and VOSviewer to enhance comprehension of this expanding research field. The study highlights a significant increase in scholarly research on utilising UAVs for precision water management over the past decade. The investigations indicate that UAVs in agriculture are gaining prominence and exhibit substantial potential for various precision agriculture (PA) applications. Significant cost reductions for UAV technology and remote sensing (RS) are anticipated soon, primarily driven by the availability of open-source platforms for processing tasks, such as Google Earth Engine. This research aims to inform smallholder farmers about the benefits of integrating UAVs into their farming practices, enhancing operational efficiency and productivity. Policymakers can use these findings to develop regulatory frameworks and incentive schemes that facilitate UAV adoption among smallholder farmers. Additionally, technology developers can leverage insights from this study to identify areas needing innovation and optimisation tailored to small-scale agriculture. Hence, this study seeks to bridge the gap between technological advancements and practical agricultural applications, promoting sustainable farming practices and enhancing the socioeconomic welfare of smallholder farmers.

Genome-based identification of phosphate-solubilizing capacities of soil bacterial isolates

Identifying genomic markers for phosphate-solubilizing bacteria (PSB) is vital for advancing agricultural sustainability. This study utilizes whole-genome sequencing and comprehensive bioinformatics analysis, examining the genomes of 76 PSB strains with the aid of specialized genomic databases and analytical tools. We have identified the pqq gene cluster, particularly the pqqC gene, as a key marker for (P) solubilization capabilities. The pqqC gene encodes an enzyme that catalyzes the conversion of precursors to 2-keto-d-gluconic acid, which significantly enhances P solubilization in soil. This gene’s importance lies not only in its biochemical function but also in its prevalence and effectiveness across various PSB strains, distinguishing it from other potential markers. Our study focuses on Burkholderia cepacia 51-Y1415, known for its potent solubilization activity, and demonstrates a direct correlation between the abundance of the pqqC gene, the quantitative release of P, and the production of 2-keto-d-gluconic acid over a standard 144-h cultivation period under standardized conditions. This research not only underscores the role of the pqqC gene as a universal marker for the rapid screening and functional annotation of PSB strains but also highlights its implications for enhancing soil fertility and crop yields, thereby contributing to more sustainable agricultural practices. Our findings provide a foundation for future research aimed at developing targeted strategies to optimize phosphate solubilization, suggesting areas for further investigation such as the integration of these genomic insights into practical agricultural applications to maximize the effectiveness of PSB strains in real-world soil environments.

Novel herbicide flusulfinam: absolute configuration, enantioseparation, enantioselective bioactivity, toxicity and degradation in paddy soils.

Flusulfinam, a novel chiral herbicide, effectively controls Echinochloa crusgalli and Digitaria sanguinalis in paddy fields, indicating significant potential for practical agricultural applications. However, limited information is available on flusulfinam from a chiral perspective. A comprehensive evaluation of the enantiomeric levels of flusulfinam was performed. Two enantiomers, R-(+)- and S-(-)-flusulfinam, were separately eluted using a Chiralcel OX-RH column. The bioactivity of R-flusulfinam against the two was 1.4-3.1 fold that of Rac-flusulfinam against two weed species. R-flusulfinam toxicity to Danio rerio larvae and Selenastrum capricornutumwere was 0.8- and 3.0-fold higher than Rac-flusulfinam, respectively. Degradation experiments were conducted using soil samples from four Chinese provinces. The findings indicated that S-flusulfinam (half-life T1/2 = 40.8 days) exhibits preferential degradation than R-flusulfinam (T1/2 = 46.2-57.8 days) in the soils of three provinces. Under anaerobic conditions, soil from Anhui exhibited preferential degradation of R-flusulfinam (T1/2 = 46.2 days) over S-flusulfinam (T1/2 = 63 days). Furthermore, two hydrolysis products of flusulfinam (M299 and M100) are proposed for the first time. The enantioselective bioactivity, toxicity and degradation of flusulfinam were investigated. Our findings indicate that R-flusulfinam is an extremely effective and low-toxicity enantiomer for the tested species. The soil degradation test indicated that the degradation of flusulfinam was accelerated by higher organic matter content and lower soil pH. Furthermore, microbial communities may play a crucial role in driving the enantioselective degradation processes. This study lays the groundwork for the systematic evaluation of flusulfinam from an enantiomeric perspective. © 2024 Society of Chemical Industry.

Beneficial effects of Bacillus mojavensis strain MTC-8 on plant growth, immunity and disease resistance against Magnaporthe oryzae.

Rice blast, a prevalent and highly destructive rice disease that significantly impacts rice yield, is caused by the rice blast fungus. In the present study, a strain named MTC-8, identified as Bacillus mojavensis, was demonstrated has strong antagonistic activity against the rice blast fungus, Rhizoctonia solani, Ustilaginoidea virens, and Bipolaria maydis. The potential biocontrol agents were identified using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis and chromatography. Further investigations elucidated the inhibitory mechanism of the isolated compound and demonstrated its ability to suppress spore germination, alter hyphal morphology, disrupt cell membrane integrity, and induce defense-related gene expression in rice. MTC-8 promoted plant growth and may lead to the development of a biocontrol agent that meets agricultural standards. Overall, the Bacillus mojavensis MTC-8 strain exerted beneficial effects on plant growth, immunity and disease resistance against rice blast fungus. In this study, we isolated and purified a bioactive substance from fermentation broth, and the results provide a foundation for the development and application of biopesticides. Elucidation of the inhibitory mechanism against rice blast fungus provides theoretical support for the identification of molecular targets. The successful development of a biocontrol agent lays the groundwork for its practical application in agriculture.

Revealing the effects of amino acid, organic acid, and phytohormones on the germination of tomato seeds under salinity stress.

Salinity accumulation poses a threat to the production and productivity of economically important crops such as tomatoes (Solanum lycopersicum L.). Currently, salt tolerance breeding programs have been limited by insufficient genetic and physiological knowledge of tolerance-related traits and a lack of an efficient selection domain. For that purpose, we aimed to determine the ability of tomato cultivars to tolerate salt based on seed traits by multiple biochemical pathways. First, we tested three tomato cultivars according to their response to different sodium chloride (NaCl) concentrations (0, 6.3, 9.8, 13.0, and 15.8 dS m-1) and then we analysed their amino acids, organic acids, and phytohormones. Considering the results of germination traits, it is possible to conclude that cultivar H-2274 was more tolerant to salt stress than others. As a result, multivariate discriminant analysis including principal component analysis and two-way hierarchical clustering analyses were constructed and demonstrated that tomato cultivars were separated from each other by the amino acid, organic acid, and phytohormone contents. Considering germination traits of tomato seeds, cv. 'H-2274' was more tolerant to salinity than others depending on high proline (29 pmol µl-1) and citric acid (568 ng µl-1) assays. Biochemical variability offers a valuable tool for investigating salt tolerance mechanisms in tomatoes, and it will be appreciated to find high-tolerant tomato cultivar(s) to saline conditions. Also, the findings of this study have significant potential for practical applications in agriculture, particularly in developing salt-tolerant tomato cultivars to enhance productivity in saline environments and address socio-economic challenges.

Application of near-infrared spectroscopy to predict chemical properties in clay rich soil: A review

Proximal soil sensing is a highly advanced and rapidly evolving technique for predicting soil chemical properties. NIR spectroscopy is expected to offer an easier and more cost-effective alternative to traditional soil chemical analysis methods for broader agricultural applications. This review aims to address the challenges in high-clay content soils by 1) understanding the relationship between NIR spectroscopy and its predictive capabilities of chemical properties, and 2) proposing the application of advanced technologies to improve their practical applications. The high-clay content soils increase the adsorption of various cations, including Al3+, Ca2+, Mg2+, K+, Na+, and NH4+, and affect crop root density, absorption area, and nutrient uptake efficiency. However, predicting soil characteristics using NIR spectra can be difficult due to the complex nature of soil metrics. Under clay-rich soils, the high moisture content leads to a significant presence of O-H vibrations (1395 and 1415 nm), which overlaps with crucial function groups. Recent advancements using deep learning models have significantly improved prediction accuracy and efficiency for soil properties. The results showed that the R² values for predicting soil organic C, total N, and pH were 0.94, 0.89, and 0.87 for S-AlexNet; 0.95, 0.94, and 0.90 for GADF-Swin Transformer; and 0.88, 0.84, and 0.87 for PLSR, respectively. Additionally, integrating hyperspectral and multispectral sensors further improves prediction efficiency by providing higher spatial and spectral resolutions. In conclusion, this review suggests that adopting cutting-edge technologies can improve the predictions accuracy of chemical properties in the clay-rich soils. Nevertheless, overcoming the limitations of advanced technologies, such as high computational resource requirements, is necessary for practical application in agriculture.

Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models

We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.

A DNA demethylase reduces seed size by decreasing the DNA methylation of AT-rich transposable elements in soybean

Understanding how to increase soybean yield is crucial for global food security. The genetic and epigenetic factors influencing seed size, a major crop yield determinant, are not fully understood. We explore the role of DNA demethylase GmDMEa in soybean seed size. Our research indicates that GmDMEa negatively correlates with soybean seed size. Using CRISPR-Cas9, we edited GmDMEa in the Dongnong soybean cultivar, known for small seeds. Modified plants had larger seeds and greater yields without altering plant architecture or seed nutrition. GmDMEa preferentially demethylates AT-rich transposable elements, thus activating genes and transcription factors associated with the abscisic acid pathway, which typically decreases seed size. Chromosomal substitution lines confirm that these modifications are inheritable, suggesting a stable epigenetic method to boost seed size in future breeding. Our findings provide insights into epigenetic seed size control and suggest a strategy for improving crop yields through the epigenetic regulation of crucial genes. This work implies that targeted epigenetic modification has practical agricultural applications, potentially enhancing food production without compromising crop quality.

Detection Model of Tea Disease Severity under Low Light Intensity Based on YOLOv8 and EnlightenGAN

In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value.

MLP-based multimodal tomato detection in complex scenarios: Insights from task-specific analysis of feature fusion architectures

Accurate and efficient tomato detection is essential for the practical deployment of robotic picking in practical agricultural applications, but it still remains significantly challenging to detect tomatoes in complex scenarios with fluctuating light, overlapping fruits, and occlusion from branches and leaves when solely using RGB images. The recent development of RGB-D sensors has brought one promising opportunity to adopt multimodal fusion for implementing high-quality fruit detection. However, the feasibility of the existing multimodal fusion and feature extraction architectures for lightweight tomato detection tasks, especially in complex agricultural scenarios, raises questions that need to be explored. As a remedy, we proposed a multimodal fusion encoder that leveraged depth and near-infrared modalities to assist RGB images in making full use of multimodal data. Moreover, the encoder contained a plug-and-play structure capable of being implemented as MLP-based (Multi-Layer Perceptron), ViT-based (Vision Transformer), or CNN-based (Convolutional Neural Networks) architectures. Furthermore, we developed a lightweight experimental detection framework based on YOLOv7-tiny by means of integrating the multimodal fusion encoder, and YOLO-DNA (Depth and Near-infrared Assisted) was put forward based on the MLP-based architecture after conducting comprehensive analysis of the aforementioned three architectures. In addition, a tomato multimodal dataset containing visible, depth, and near-infrared images was established. Experimental results demonstrated that YOLO-DNA achieved mAP0.5 of 98.13% and mAP0.5:0.95 of 74.0%, an average increase of 5.01% in mAP0.5 and 14.55% in mAP0.5:0.95 over mainstream lightweight detection models, with a detection speed of 37.12 FPS, meeting the demand of real-time tomato detection. This finding has the potential to advance research on fruit detection in the field of intelligent agricultural harvesting.

Microbial inoculation alters rhizoplane bacterial community and correlates with increased rice yield

Microbial inoculants are recognized as environmentally friendly methods to promote plant growth and improve soil properties. However, the effects of inoculation on the rhizosphere and rhizoplane community structure of plants remain poorly documented and need further investigation. Rhodopseudomonas palustris (R. palustris) strain has nitrogen fixing ability and Bacillus subtilis (B. subtilis) strain is a plant growth-promoting rhizobacterium (PGPR). In this study, we investigated the effects of single and co-inoculation with R. palustris and B. subtilis on the increase of rice yield as well as on the microbial communities in the rhizosphere and rhizoplane of rice through potting experiments, respectively. The results showed that inoculation significantly increased rice yield and seed setting rate, with co-inoculation raising the yield by up to 13.7%. Inoculation influenced both rhizosphere and rhizoplane community structures and functions, amplifying the differences between them. The most significant changes were brought about by the combined inoculation treatment. Co-inoculation with R. palustris and B. subtilis had a synergistic effect. The profound alterations of rhizoplane bacterial community structures and functions were proved to be positively correlated with rice yield and seed setting rate (r = 0.59–0.76, p < 0.05). These results provide new ideas for the investigation of the potential microbiological mechanisms of microbial co-inoculation in practical agricultural applications.

Synthesis, characterization, and swelling properties of a novel tapioca-g-Poly(Acrylic acid−2−acrylamido−2−methylpropane sulfonic acid)/ammonium polyphosphate superabsorbent polymer

In this study, a novel superabsorbent polymer tapioca starch-g-poly(acrylic acid-2-acrylamido-2-methylpropane sulfonic acid)/ammonium polyphosphate (TS-g-AA-AMPS/APP) was synthesized based on the graft copolymerization of tapioca starch (TS) with acrylic acid (AA) and 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS) via free radical polymerization in aqueous solution with ammonium polyphosphate (APP) additive added for study. The synthesized superabsorbent polymer material was characterized by Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), energy-dispersive x-ray spectroscopy (EDS), and thermogravimetric analysis (TGA). Research investigating the material synthesis conditions to absorbance in distilled water and 0.9 wt% NaCl solution has also been studied. Under optimal synthesis conditions, the absorbance in distilled water and 0.9 wt% NaCl solution were 416 g g−1 and 61 g g−1, respectively, for the sample with 3 wt% APP content. The introduction of APP units has improved the absorption properties of the material such as the water retention capacity reaching 55.25% in 10 h at 60 °C. TS-AA-AMPS/APP exhibits reversible swelling ability, with the swelling level not being reduced compared to the initial after 5 swelling-drying cycles at 60 °C, and water absorption reaches swelling equilibrium after about 270 min. Additionally, TGA thermogravimetric analysis results showed an improvement in the thermal stability of TS-AA-AMPS/APP compared to the pure polymer. These results show that the TS-AA-AMPS/APP polymer with excellent swelling properties, low production cost, and environmentally friendly has the potential for practical applications in agriculture, gardening, and water retention materials.

EfficientNet architecture and attention mechanism-based wheat disease identification model

The image-based automatic plant disease identification has emerged as a prominent research area, driven by recent advancements in machine learning algorithms. Conventional disease detection methods rely on manual feature extraction from acquired images, involving extensive expertise from experts and pathologists. This labor-intensive practice is often time-consuming and impractical. With the recent advancements in digital technologies and computer vision, Deep neural networks address these challenges by learning the features automatically. This paper focuses on the EfficientNet B0 architecture-based deep neural network model integrated with a convolutional block attention mechanism module for identifying major wheat crop diseases. The attention module in the EfficientNet B0 model improves its performance by emphasizing the disease-infected regions in leaf images. To evaluate the proposed methodology, experiments were conducted on the WheatRust21 image dataset, comprising 6556 images of three major fungal diseases, namely, stripe rust, leaf rust, and stem rust, collected in natural field conditions from 2019 to 2021. Our developed EfficientNet B0-CBAM model performed well with a testing accuracy of 98.70% and an impressive F1 score of 98% on the dataset. The Grad-CAM visualization demonstrates that the attention module focuses more on the infected region and plays a crucial role in wheat disease identification. The results are quantitatively substantial, with the model achieving high accuracy and other evaluation metrics scores with an average of 98.5 %. These findings hold promise for practical applications in agriculture and sustainable crop management.

Nicotiana noctiflora Hook. Genome Contains Two Cellular T-DNAs with Functional Genes.

Agrobacterium (Rhizobium)-mediated transformation leads to the formation of crown galls or hairy roots on infected plants. These effects develop due to the activity of T-DNA genes, gathered on a big plasmid, acquired from agrobacteria during horizontal gene transfer. However, a lot of plant species are known to contain such sequences, called cellular T-DNAs (cT-DNAs), and maintain normal phenotypes. Some of the genes remain intact, which leads to the conclusion of their functional role in plants. In this study, we present a comprehensive analysis of the cT-DNAs in the Nicotiana noctiflora Hook. genome, including gene expression and opine identification. Deep sequencing of the Nicotiana noctiflora genome revealed the presence of two different cT-DNAs, NnT-DNA1 and NnT-DNA2, which contain the intact genes iaaM, iaaH, acs, orf13, orf13a, and orf14. According to the expression analysis results, all these genes are most active in roots in comparison with other organs, which is consistent with data on cT-DNA gene expression in other plant species. We also used genetic engineering approaches and HPTLC and HPLC-MS methods to investigate the product of the acs gene (agrocinopine synthase), which turned out to be similar to agrocinopine A. Overall, this study expands our knowledge of cT-DNAs in plants and brings us closer to understanding their possible functions. Further research of cT-DNAs in different species and their functional implications could contribute to advancements in plant genetics and potentially unveil novel traits with practical applications in agriculture and other fields.

Bacillus velezensis BV01 Has Broad-Spectrum Biocontrol Potential and the Ability to Promote Plant Growth.

To evaluate the potential of a bacterial strain as a fungal disease control agent and plant growth promoter, its inhibitory effects on phytopathogens such as Bipolaris sorokiniana, Botrytis cinerea, Colletotrichum capsici, Fusarium graminearum, F. oxysporum, Neocosmospora rubicola, Rhizoctonia solani, and Verticillium dahliae were investigated. The results showed that the inhibitory rates in dual-culture and sterile filtrate assays against these eight phytopathogens ranged from 57% to 83% and from 36% to 92%. The strain was identified as Bacillus velezensis based on morphological and physiological characterization as well as phylogenetic analyses of 16S rRNA and the gyrase subunit A protein (gyrA) regions. The results demonstrated that B. velezensis was able to produce fungal cell-wall-degrading enzymes, namely, protease, cellulase, and β-1,3-glucanase, and the growth-promotion substances indole-3-acetic acid (IAA) and siderophore. Furthermore, B. velezensis BV01 had significant control effects on wheat root rot and pepper Fusarium wilt in a greenhouse. Potted growth-promotion experiments displayed that BV01 significantly increased the height, stem diameter, and aboveground fresh and dry weights of wheat and pepper. The results imply that B. velezensis BV01, a broad-spectrum biocontrol bacterium, is worth further investigation regarding its practical applications in agriculture.

Enhancing crop productivity and sustainability through disease identification in maize leaves: Exploiting a large dataset with an advanced vision transformer model

The timely identification of diseases in maize leaf offers several benefits such as increased crop productivity, reduced reliance on harmful chemicals, and improved production of healthy crops, resulting in enhanced economic returns. Computer-aided systems (CAD) play a crucial role in agriculture by enabling timely and efficient disease identification in plant leaves. Deep learning-based CAD systems facilitate accurate and rapid diagnosis of maize leaf diseases. In this research, we introduce an advanced vision transformer model that achieves exceptional accuracy and inference speed in detecting diseases in maize leaves. To begin with, we adapt the Multi-axis vision transformer (MaxViT) model to a 4-class maize dataset, creating a lightweight structure that offers improved accuracy and inference speed. Furthermore, we enhance the accuracy by replacing the conventional convolutional structure in the MaxViT architecture's Stem with a Squeeze-and-Excitation (SE) block. In addition, to boost accuracy further, we employ the Global Response Normalization (GRN)-based MLP from the ConvNexTv2 architecture instead of the MLP in the MaxViT architecture. Notably, we combine the PlantVillage, PlantDoc, and CD&S datasets from the literature, resulting in the creation of the most extensive dataset available. This dataset is then divided into three sets: training, validation, and testing, enabling the evaluation of the generalization abilities of the deep learning models. Our study goes beyond previous research by offering a comprehensive comparison of the performance of over 28 CNN models and more than 36 vision transformer models on the newly created dataset. By achieving a remarkable accuracy rate of 99.24% and a high inference speed, the proposed method outperforms all existing deep learning models in the literature. Therefore, it has been demonstrated that this advanced vision transformer model, based on MaxViT, is exceedingly effective for practical applications in agriculture.

Uracil hydrazones: design, synthesis, antimicrobial activities, and putative mode of action.

Crop diseases caused by plant pathogenic fungi and bacteria have led to substantial losses in global food production. Chemical pesticides have been widely used as a primary means to mitigate these issues. Nevertheless, the persistent and excessive use of pesticides has resulted in the emergence of microbial resistance. Moreover, the improper application and excessive utilization of pesticides can contribute to environmental pollution and the persistence of pesticide residues. Consequently, the development of novel and highly effective bactericides and fungicides to combat plant pathogens holds immense practical importance. A series of uracil hydrazones IV-B was deliberately designed and evaluated for their antimicrobial efficacy. The results of bioassays indicated that most IV-B exhibited >80% inhibition against the fungal species Monilia fructigena and Sclerotium rolfsii, as well as the bacterial species Clavibacter michiganensis subsp. michiganensis, Xanthomonas oryzae pv. oryzae, and Ralstonia solanacearum, at 50 μg/mL in vitro. In vivo, IV-B20 showed 89.9% of curative and 71.8% of protective activities against C. michiganensis subsp. michiganensis at 100 μg/mL superior to thiodiazole copper and copper hydroxide. IV-B20 also showed excellent protective activity against M. fructigena (96.3% at 200 μg/mL) and S. rolfsii (80.4% at 1000 μg/mL), which were greater than chlorothalonil and equivalent to thifluzamide. Mechanistic studies revealed that IV-B20 induced oxidative damage in pathogenic bacteria and promoted the leakage of cellular contents. This study suggests that IV-B20 with uracil hydrazone skeleton has great potential as an antimicrobial candidate. These findings lay a foundation for practical application in agriculture. © 2023 Society of Chemical Industry.

Recycling of spent Oyster mushroom (Pleurotus ostreatus) sawdust waste for the production of Straw mushroom (Volvariella volvacea)

The cultivation of straw mushroom Volvariella volvacea has a long history and has undergone continuous development. Nevertheless, ongoing research aims to utilize various agricultural wastes to improve biological efficiency. In this study, we investigate a sustainable strategy that promotes the conversion to a circular economy by recycling spent mushroom substrate in new mushroom crops. Our research provides insight into reducing spent mushroom sawdust waste (SMSW) of Pleurotus ostreatus for application in Volvariella volvacea cultivation. We found that the optimal conditions for cultivating V. volvacea were at a temperature of 35.0℃ a humidity of 80-85%, resulting in an average growth rate of 10.60 cm and a total weight of fruit bodies of 0.77 kg, with a biological efficiency of approximately 15.47% within 15 days of cultivation. We discovered that the SMSW could be an alternative substrate for V. volvacea cultivation. Our findings can be applied to improve V. volvacea cultivation and practical applications in agriculture. By involving innovative waste management practices, this article can foster economic growth while preserving the environment for future generations. Therefore, this study contributes to significant socio-economic development and supports green economic policies by discussing the improvements from using SMSW.

Prospects of Computer-Aided Molecular Design of Coumarins as Ecotoxicologically Safe Plant Protection Agents

Coumarins are secondary plant metabolites widely distributed in higher plants, bacteria, fungi, and sponges. This great structural diversity of these natural compounds and their synthesized derivatives enables their wide range of pharmacological activities, such as antioxidant; antibacterial; antifungal; anti-human immunodeficiency infection; anti-tubercular; and anti-cancer activities, which were very well reviewed previously. There are also many reports about their effectiveness against plant pathogenic pests (phytopathogenic fungi, bacteria, nematodes, and insects). These secondary metabolites protect environmental enemies and competing plants. However, there is still limited literature on coumarins’ practical applications in agriculture, as well as their effects on beneficial populations of soil organisms. This review summarizes recent knowledge about the effects of natural and synthesized coumarins on phytopathogens and beneficial populations of soil organisms. A separate section is dedicated to poorly reviewed computer-aided molecular design (CAMD) methods of agrochemicals. It also reviewed CAMD techniques to develop low-toxicity and environmentally safe pesticides. Despite the many positive effects of coumarins related to plant protection, they do possess properties harmful to the environment and health. These properties are described in the last section. Prediction of coumarin hazardous properties using a quantitative structure-activity relationship (QSAR) approach was also reviewed.

Specialized Metabolites Produced by Phytotopatogen Fungi to Control Weeds and Parasite Plants.

Weeds such as parasite plants are one of the most serious pests that farmers are forced to combat since the development of agriculture using different methods including mechanic and agronomy strategies. These pests have generated significant losses of agrarian and herding production, constituting a serious impediment for agricultural activities in reforestation practices and in important infrastructures. All these serious problems have induced the expansive and massive use of synthetic herbicides, which represents one of the main cause of environmental pollution, as well as serious risks for human and animal health. An alternative environmental friendly control method could be the use of bioherbicides based on suitably bioformulated natural products, of which the main ones are fungal phytotoxins. This review covers the literature from 1980 to the present (2022) and concerns fungal phytotoxins with potential herbicidal activity in order to obtain their efficacy as bioherbicides for practical application in agriculture. Furthermore, some bioherbicides based on microbial toxic metabolites are commercially available, and their application in field, mode of action and future perspectives are also discussed.