Corporate environmental, social, and governance (ESG) performance has become an increasingly critical driver of sustainable development. Investigating the impact of ESG performance on corporate green development is of great significance for achieving green transformation and sustainability goals. This study examines the effects and underlying mechanisms of ESG performance on the green development of Chinese A-share listed companies in the agricultural and forestry sectors from 2013 to 2023. The empirical results show that higher ESG performance significantly promotes corporate green development. Further heterogeneity analysis reveals that this effect varies markedly across ownership structures, geographic regions, and levels of ESG rating uncertainty. Mechanism testing indicates that ESG performance fosters green development primarily through three pathways: stimulating green innovation, improving resource allocation efficiency, and enhancing the structure of human capital. In addition, by decomposing green total factor productivity, this study further quantifies the contribution of ESG performance to green growth. These findings offer new insights into the ESG–green development nexus and provide valuable policy implications for the green transformation and sustainable development of agricultural and forestry enterprises.
Peanut, as an important economic crop, is widely cultivated and rich in nutrients. Classifying peanuts based on the number of seeds helps assess yield and economic value, providing a basis for selection and breeding. However, traditional peanut grading relies on manual labor, which is inefficient and time-consuming. To improve detection efficiency and accuracy, this study proposes an improved BTM-YOLOv8 model and tests it on an independently designed pod detection device. In the backbone network, the BiFormer module is introduced, employing a dual-route attention mechanism with dynamic, content-aware, and query-adaptive sparse attention to extract features from densely packed peanuts. In addition, the Triple Attention mechanism is incorporated to strengthen the model’s multidimensional interaction and feature responsiveness. Finally, the original CIoU loss function is replaced with MPDIoU loss, simplifying distance metric computation and enabling more scale-focused optimization in bounding box regression. The results show that BTM-YOLOv8 has stronger detection performance for ‘Quan Hua 557’ peanut pods, with precision, recall, mAP50, and F1 score reaching 98.40%, 96.20%, 99.00%, and 97.29%, respectively. Compared to the original YOLOv8, these values improved by 3.9%, 2.4%, 1.2%, and 3.14%, respectively. Ablation experiments further validate the effectiveness of the introduced modules, showing reduced attention to irrelevant information, enhanced target feature capture, and lower false detection rates. Through comparisons with various mainstream deep learning models, it was further demonstrated that BTM-YOLOv8 performs well in detecting ‘Quan Hua 557’ peanut pods. When comparing the device’s detection results with manual counts, the R2 value was 0.999, and the RMSE value was 12.69, indicating high accuracy. This study improves the efficiency of ‘Quan Hua 557’ peanut pod detection, reduces labor costs, and provides quantifiable data support for breeding, offering a new technical reference for the detection of other crops.
Abstract Background As crucial pollinators sustaining agricultural ecosystem services and biodiversity, bees mediate pollination for approximately 35% of global insect-pollinated crops and generate multidimensional ecological value through apicultural products in the pharmaceutical and food industries. However, emerging viral pathogens pose escalating threats to bee health. Results To address the technical bottlenecks in pathogen detection for viral paralysis disease in bees, this study innovatively integrated multiplex RT-PCR amplification, lateral flow dipstick (LFD), and centrifugal microfluidic chip technology (MFCT) to develop an on-site quadruple detection platform capable of simultaneously identifying four viruses: Chronic Bee Paralysis Virus (CBPV), Black Queen Cell Virus (BQCV), Deformed Wing Virus (DWV), and Israeli Acute Paralysis Virus (IAPV). Through multiple sequence alignment, conserved genomic regions of the four viruses were identified, and systematic screening was performed to optimize primer combinations, with critical parameters such as primer concentration (10 µM) and annealing temperature (55 °C) determined. Building on this, a RT-PCR-LFD-MFCT integrated detection system was established by incorporating chemically modified downstream primers/probes and MFCT. Experimental results demonstrated a sensitivity of 10² copies/µL for single-virus detection, enabling precise identification of low viral loads. The method exhibited exceptional specificity with no cross-reactivity, and clinical sample validation achieved 100% concordance with conventional RT-qRT-PCR. Conclusions This system features simultaneous multi-target detection, high specificity, rapid processing, minimal instrumentation requirements, portability, and field applicability. It provides a robust tool for precise diagnosis and control of bee paralysis diseases, particularly suitable for resource-limited apiaries and outbreak scenarios, demonstrating significant practical value for safeguarding apicultural health.
Ali Khakpour, Negar Ahmadi Shadmehri, Amir Sedaghati
et al.
Pectate lyase (Pel3), an enzyme derived from Clostridium bacteria, plays a significant role in the degradation of pectin and contributes to the spoilage of agricultural products. Pel3 can bind to pectin and break it down, a process that accelerates food decay. Aesculin, a natural compound extracted from walnut husk, has been recognized for its antibacterial and antifungal properties, making it a promising natural inhibitor. The aim of this study was to investigate the inhibitory mechanisms of Aesculin through molecular simulations and random accelerated molecular dynamics (RAMD). Molecular docking results showed that Aesculin may effectively bind to Pel3 and form a strong interaction. RMSD analysis revealed that Aesculin's binding to Pel3 reduced structural fluctuations, thereby enhancing the enzyme's structural stability. Slight changes in the radius of gyration (Rg) indicate a decrease in structural compactness in specific regions of the protein. Furthermore, SASA analysis revealed a modest increase in solvent accessibility. RAMD simulations, performed with 120 replicates, showed a short average residence time (∼0.015 ns), suggesting rapid unbinding and weak interaction at the active site. MM-PBSA analysis yielded a total binding free energy of −2.92 ± 0.44 kcal/mol, mainly driven by van der Waals and electrostatic contributions, confirming moderate and reversible binding. These findings suggest that Aesculin may form alternating interactions with Pel3 as an effective natural inhibitor and exhibit a short residence time in its active site. The molecular dynamics simulations and RAMD analysis suggest that Aesculin can enhance the structural stability of Pel3, presenting it as a potential anti-spoilage agent in the food and agricultural industries.
A conventional air-assisted sprayer was retrofitted with a laser-guided variable-rate spray control system, allowing operation in either constant-rate mode (CRM) or variable-rate mode (VRM) to improve spray efficiency. Field experiments in a fully foliaged ash tree nursery compared canopy and ground deposition between VRM and CRM. CRM applied a constant 470 L ha⁻¹, while VRM automatically adjusted 212–255 L ha⁻¹ based on tree size, shape, and foliage density. Spray deposits were measured with stainless-steel screens and plastic plates, and coverage was evaluated using water-sensitive papers. VRM resulted 40.8–52.4 % of spray volume reduction relative to CRM while achieving comparable canopy deposition (0.92 ± 0.57 vs. 1.13 ± 0.57 µL cm⁻²) and coverage (42 ± 3 % vs. 52 ± 6 %). Ground deposition was notably lower with VRM (0.17 ± 0.10 µL cm⁻², 4 ± 3 % coverage) than CRM (0.38 ± 0.18 µL cm⁻², 9 ± 6 % coverage), indicating reduced spray loss. Deposition was more uniform with VRM across canopy and ground locations. Pearson correlation and multiple regression analyses indicated that average wind speeds (0.55–1.04 m s⁻¹) and directions (130–229°) were not able to significantly influence on the variability of average spray deposition inside the canopy and on the ground while the airflow from the sprayer was the dominate factor to carry droplets. Thus, the conventional sprayer retrofitted with the laser-guided VRM system could potentially reduce chemical usage by 50 %, reduce ground spray loss by 55 %, while maintaining comparable level of spray deposition and coverage inside canopies. This new technology would be greatly beneficial to growers to minimize pesticide waste into the production field and the environment. Impact: This study represents an applied field validation of a laser-guided variable-rate intelligent sprayer under commercial nursery conditions. It demonstrates pesticide savings, deposition uniformity, and environmental benefits, providing practical evidence for the real-world adoption of a commercialized smart spraying system (SmartApply™ integrated into John Deere sprayers) and advancing sustainable precision agriculture.
Dimas Firmanda Al Riza, Lucky Candra Musahada, Romzi Izzudin Aufa
et al.
Fruits detection and counting is an important task for yield prediction that could be achieved by computer vision. The ability to locate and count the fruits could also help the harvesting robot to do a picking task. YOLO is one of the deep learning models which is popular and widely used for object detection and has good performance in detection speed and precision. In the citrus counting task, the label could be set as a single label or multi-label which shows different citrus maturity. The performance of the deep learning model could be different with a different number of labels. Furthermore, there are several types of YOLOv7 models with different sizes and purposes which could also have different performances to do a similar task. This study aims to compare the performance of different kinds of YOLOv7-based deep learning models for citrus fruit detection and counting. Case study on the citrus cv. Batu 55 trees have been carried out. The results show that the original YOLOv7 achieved the best performance both on single and double labels compared to the tiny and X versions of YOLOv7. The YOLOv7 could reach mAP50 of 0.906, a precision of 0.85, a sensitivity of 0.825, and an F1-score of 0.837, while for the counting task, the model has a good performance with R2 of 0.966.
Simone Figorilli, Loredana Canfora, Andrea Manfredini
et al.
Soil plays a central role in delivering several ecosystem services. However, its complex nature, the spatial variability and the timescale of soil processes make it challenging to quantify shifts in soil quality as a result of agronomical practices. A comprehensive indicator that includes parameters from different categories of soil properties, allowing an easy interpretation of soil quality by farmers and land managers, is thus needed. In this context, a class-modelling approach based on the Data-Driven Soft Independent Model of Class Analogy (DD-SIMCA) was tested to develop a soil quality index based on physical, chemical and biological parameters. Three models were built on a dataset composed by physical, chemical and biological soil parameters, which was created basing on ranges of values common to agricultural soils. The algorithm was thus applied to a real dataset obtained from about 9800 soil samples. The models showed very high performance (sensitivity = 1), allowing to classify the samples into quality groups. The model output was incorporated into a coloured QR-code, which allowed to express the quality of a soil sample with a colorimetric scale based on a soil quality index. A preliminary version of the tool is available for further testing and validation through a web platform (https://agritechlab.crea.gov.it/model/ddsimcasoil/ddsimcasoil.html).
A robust maize yield prediction framework is proposed to counter major problems predominantly present in Agri-tech analytics, like scarcity of data, class imbalance, redundant features, and model interpretability. This research is motivated by the need for accurate crop forecasting to ensure food security amid climate change and population growth. The methodology integrates frontier methods to improve both accuracy and explainability through a structured five-stage process. For augmenting data to address the issue of data-scarcity, generative adversarial networks (GANs) with a 200-dimension latent space were used to synthetically generate 20,000 samples, which greatly boosted the dataset. Data preprocessing included IQR-based outlier removal and class balancing. Feature selection is carefully addressed via a combination of 14 statistical methods, tree-based methods, bio-inspired methods, and regularization methods so that only the most relevant features for modelling are chosen and included. The predictive framework is based on the ensemble of one-dimensional convolutional neural network (CNN) learning on the features selected, combining three parallel branches (processing features selected by Decision Tree, XGBoost, and Lasso methods), followed by a stacked refinement with residual connections. This two-stage approach reinforces both the accuracy and robustness of prediction. The focus on transparency and interpretability makes this work relevant. By the adoption of Explainable AI (XAI) tools such as SHAP and LIME, interpretable explanations are afforded to a model as to which features contribute to the prediction [21] [33]. The combination of stacked modelling methods and model interpretability is a significant enhancement in agricultural analytics, providing actionable insights for farmers with the aim of increasing crop production. The framework's effectiveness was validated on maize data from Sevur farm. The model outperformed baseline methods with an R2 of 0.9165 and mean squared error (MSE) of 0.6893, significantly outperforming conventional approaches for optimizing production in variable growing conditions.
The grain filling is the key period for quality formation in rice, which is greatly influenced by nitrogen (N) fertilizer and irrigation management. This study aimed to investigate effects of different N fertilizer rates and irrigation regimes at panicle stage on rice cooking and eating quality. A superior taste japonica variety Nanjing 9108 (NJ9108) was planted in the field. Under the condition of 150 kg N hm−2 base-tiller fertilizer, four panicle N fertilizer rates (0, 70, 140, and 210 kg hm−2) were applied, and two irrigation regimes, viz. conventional irrigation (CI) and alternate wetting and moderate drying irrigation (AWMD), were imposed during grain filling. The results showed that panicle N fertilizer rates and irrigation regimes interacted partially to regulate cooking and eating quality of rice. The lipid content in milled rice and the quality were reduced with increasing panicle N fertilizer rate, while the protein content was changed reversely. Compared to CI, AWMD regime increased lipid content and improved the quality as well as grain yield at four panicle N fertilizer rates. In two study years, the protein content was lower in the AWMD than in the CI when panicle N fertilizer rates were greater than 106.7 kg hm−2 and 131.0 kg hm−2, respectively. Correlation analysis indicated that lower protein content and higher lipid content were beneficial to the improvement of rice cooking and eating quality. The results also suggested that NJ9108 can obtain a win-win goal of superior quality and high yield when panicle N fertilizer rate is 106.7–131.0 kg hm−2 and the AWMD is adopted during grain filling. Such a study can provide references for the cultivation of superior taste japonica rice.
Plants on the land surface play a vital role in the hydrological water cycle as they transport soil water to the atmosphere through transpiration. Root water uptake (RWU) is considered a crucial step in this process as it is the first stage of transpiration, directly determining the actual transpiration (Ta) of plants. However, accurately measuring RWU or Ta in situ poses significant challenges. Here, we establish an overall approach of combining mathematical models and machine learning algorithms to obtain high-precision (500 m×500 m) regional-scale daily Ta maps for various future climate patterns. The Hydrus-1D and AquaCrop models were employed to calculate the total RWU fluxes across the entire root zone, aiming to achieve Ta at a point scale. A machine learning model was developed using the CatBoost algorithm and environmental covariates extracted from the Google Earth Engine (GEE) platform to upscale these point-scale Ta to the regional scale. Furthermore, a total of 22 CMIP6 Earth System Models (ESMs) were evaluated, and among them, ACCESS-CM2 and ACCESS-ESM1–5 were selected for simulating future climate scenarios. Based on the established machine learning model and selected ESMs, regional-scale Ta maps were generated from 2020 to 2100 for the SSP245 and SSP585 (Shared Socioeconomic Pathways) scenarios. The results indicate that near-surface specific humidity, mean near-surface air temperature, latitude, and surface downwelling shortwave radiation are the critical factors influencing regional-scale Ta. As greenhouse gas emissions intensify and temperatures rise, regional-scale Ta is enhanced, leading to an accelerated transfer of soil water to atmospheric water. Under the SSP245 scenario, Ta increases on average by 0.55–1.16 % every 20 years, with its incremental value ranging from 7.14∙10−4 to 8.65∙10−4 cm day−1, while under the SSP585 scenario, Ta increases more significantly, achieving an average increase of 0.64–1.81 % every 20 years, with its incremental value ranging from 1.595∙10−3 to 2.821∙10−3 cm day−1. This study provides a robust integrated approach to assess the future regional-scale Ta providing valuable insights into the underlying water cycle mechanisms and regional water requirements for future climate scenarios.
Miza Badriah Nazri, Azrina Azlan, Sharmin Sultana
et al.
This study aimed to assess and compare the antioxidant activity and content (total flavonoid levels and total phenolic) of mature and immature okra. The antioxidant activity of okra fruits was assayed using four methods, namely: Aluminium Chloride Colorimetric assay, Folin-Ciocalteu assay, 1, 1-diphenyl-2-picrylhydrazyl (DPPH), and Reducing Antioxidant Power assay (FRAP) assays. The immature, mature, and very mature okra samples (less than 8 days, 10-15 days, and more than 20 days, respectively) were extracted using two different solvents (65% ethanol and water). The sample that was extracted with mature ethanol had the highest Total Phenolic Content (TPC) at 21.564 ± 1.635 mg GAE/g, while the sample that was extracted with extremely mature ethanol had the highest TFC at 54.391 ± 8.224 mg QE/g. The mature 65% ethanolic extracted sample showed the lowest IC50 value of DPPH scavenging activity (0.920± 0.096 mg/ml), and the mature ethanol extracted sample had the highest FRAP value (232.018± 5.337 μmol Fe2+/g). These studies showed that ethanolic extracts of mature Abelmoschus esculentus had higher antioxidant content and activity than okra water extracts. Based on the DPPH Radical Scavenging Assay revealed favourable associations between TPC (r = 0.860), TFC (r = 0.742), and antioxidant activity as evaluated by FRAP, demonstrating that both phenolics and flavonoids contributed to the extract’s antioxidant properties. Both TPC and TFC showed negative correlations with IC50 values (r = -0.766, r = -0.650, respectively). In conclusion, the mature okra fruits extracted with 65% ethanol give higher antioxidant content than the water extracts of okra fruits and potentially be used as a source of antioxidants rather than be discarded.
Kedar Surendranath Ghag, Amirhossein Ahrari, Anandharuban Panchanathan
et al.
This research study investigates the impact of air temperature and precipitation on annual potato crop yield in Finland region and a local area in northern Finland. For this, the annual crop yield data of regional and local case study area is processed using Z-score normalization technique. Classification of computed z-score values was carried out into different classes ranging between the most beneficial crop yield year and the most vulnerable crop yield year. Later, the detection of feasible potato cropping season at monthly and daily scale was carried out using different weather parameters. Further, long-term trend analysis using Mann-Kendall's approach at annual, seasonal, and monthly scales was carried out using 60 years of dataset of both case study areas. Then after, the comparative analysis of annual crop yield and the characteristics obtained within climate data using different statistical approaches at annual, seasonal, and monthly scale. Finally, multivariate analysis was carried out to find most influential climate variables obtained using different statistical signatures. The results shows that, over the 100 years of period the annual potato crop yield of regional case study area shows a rising trend with declining area under potato crop. Classification approach found 16 % annual yield scored lowest Z-score while, 18% the highest. Likewise, 20 years of data for the local case study area found that 25 % of the dataset scored the highest Z-score and 15 % of the dataset values scored the lowest. Long-term trend analysis of air temperature shows significant increasing trend annually, seasonally and for monthly during the months between July-September. Whereas, similar procedure with the precipitation data showed increasing trend only at annual scale with the precipitation data analysis. During the comparative analysis between the annual crop yield data based on z-score classification and different statistical results obtained from weather parameters such as variation of precipitation and temperature sum, long-term climate data maximum-minimum totals seasonally as well as monthly during crop growing season; noticeable anomalies were observed in the weather parameters in line with the crop yield years grouped into the lowest and the highest Z-scores. The Multivariate analysis emphasized the impact of air temperature over precipitation on potato crop yield. Present study provides quantitative assessment on usefulness of long-term climate signatures for region specific either event-based or crop-based modelling development efforts to provide efficient decision support for enhancing farm-level crop productivity and sustainability of local and regional agriculture.
Reduced irrigation in combination with biochar application can improve stomatal anatomy, water relations and intrinsic water use efficiency (WUEi), thus having a positive effect on the alleviation of salinity and drought stresses. A split-root pot experiment was executed to explore the effects of two biochar applications (WSP: wheat straw biochar; SWP: soft wood biochar) in combination with three irrigation strategies (FI: full irrigation; DI: deficit irrigation, PRD: partial root-zone drying irrigation) on stomatal anatomy, water relations and WUEi of cotton plants under normal soil (S0, EC=0.36 dS m-1) and saline soil (S1, EC=16.55 dS m-1). The results revealed that both salinity and drought stresses negatively affected plant water relations and reduced stomatal conductance, carbon isotope discrimination (Δ13C), stomatal size (SS) and hydraulic conductance (Kl) while increased leaf abscisic acid (ABA) concentration ([ABA]leaf). However, biochar amendment under salt stress significantly decreased [ABA]leaf while improved leaf water relations, increased Kl, stomatal density (SD), SS, Δ13C and maximum stomatal conductance (gsmax). Meanwhile, compared with FI and DI, PRD plants had greater SD, gsmax, and WUEi but lowered stomatal conductance (gs). Among all treatments, the combined application of WSP and PRD significantly increased SD and gsmax, improved leaf water relations, Kl and [ABA]leaf and WUEi. It is concluded that the altered stomatal features caused by the biochar and irrigation treatments are associated with changes in [ABA]leaf and Kl, and have a major role in affecting plant hydraulic integrity and water use efficiency of cotton exposed to salinity stress.
Kevan W. Lamm, Lauren Pike, Lauren Griffeth
et al.
Throughout the United States, the agricultural, forestry, and natural resource industries are facing a multitude of challenges. While each industry is facing unique challenges on a national level, these challenges vary in scope and topic, and they are not necessarily generalizable to smaller geographic regions. Based on the socio-economic importance of agriculture in the state, along with five distinct geographic regions ranging from coastal to mountainous, this study compiled a comprehensive list of critical issues facing the agricultural, forestry, and natural resource industries in the state of Georgia. The study used the Delphi methodology with an expert panel composed of agricultural, forestry, and natural resource opinion leaders. Using a three-round consensus-building process, a total of 40 critical issues were identified with eight items receiving 100% agreement amongst the panelists. The final list of items were then analyzed using the constant comparative method to identify themes within the retained items. Six themes emerged based on the analysis, including (alphabetically) economic considerations, operations and infrastructure, policy, public perceptions, regulations, and workforce. The proposed themes, and subsumed critical issues, represent a heuristic framework within which to facilitate dialogue amongst agricultural, forestry, and natural-resource-related industries, as well as inform future research and praxis oriented efforts.
Une charte de 1183 mentionne des plantae pinaudi, dont la traduction est délicate. Il est proposé de voir dans cette locution non pas des variétés de vignes, pinot bourguignon ou pineau ligérien, mais les vignes d’un dénommé Pinaud.
Agricultural industries, Economic history and conditions
Abstract Processed meat products are a staple part of the typical European diet. Product packaging can include a considerable amount of information and, with other intrinsic and extrinsic attributes, substantially influence consumers' preferences and purchasing decisions. This study investigates 14 product attributes of processed meat products using a cross-country analysis. Based on an online survey conducted in Hungary (n = 410), Italy (n = 268), and Serbia (n = 402), an object-case best–worst scaling approach was applied. Results reveal both international and country-specific characteristics of preferences. Best-Worst scores reveal that taste and best-before date are among the most significant considerations in all three countries, while brand is among the attributes considered least important. Comparisons indicate significant differences according to country and socioeconomic characteristics. The study provides managerial implications.
Nutrition. Foods and food supply, Agricultural industries
Estela M. Pasuquin, Philip L. Eberbach, Toshihiro Hasegawa
et al.
Rising air temperatures have the capacity to impact rice yields in future climates. Studies in large temperature-controlled field chambers were established to examine the responses of four contrasting rice genotypes to elevated daytime temperatures (ET) during reproductive development under paddy conditions. Field chambers were effective in raising mean above-canopy maximum daytime temperatures from 29.9 to 41.1°C during 12 d of ET treatment (68–80 d after emergence, DAE), while increased transpiration under ET resulted in lowering of mean lower-canopy maximum temperature to 33.2°C. Nevertheless, the earliest genotype Vandana encountered a hot spell of 37.0°C at 68–74 DAE in the lower canopy at its late reproductive stage, which exceeded the spikelet sterility threshold of 33.7°C, so its spikelet fertility, grain number and grain yield were reduced under ET. Genotypes differed in the extent of canopy cooling, with less reduction in Vandana and IR64 than in N22 and Takanari. For canopy cooling to be effective, stratification of air layers must occur within the canopy, which was more effective under the shorter and denser canopy of N22 and Takanari (plant height of 70–80 cm) than under IR64 (90–110 cm) and Vandana (115–130 cm). Genotypes with appropriate canopy structures should be chosen for high vapour pressure deficit (VPD) conditions. Both maximum canopy temperature and VPD need to be specified to define the critical threshold for heat tolerance. Takanari was notable for greater leaf area retention and greater leaf photosynthetic capacity due to the maintenance of a higher internal leaf CO2 concentration, which led to higher spikelet and grain numbers and higher yield potential under ET conditions.
Puwich Chaikhumwang, Adthakorn Madapong, Kepalee Saeng-chuto
et al.
AbstractThis study was conducted to evaluate the induction of systemic and mucosal immune responses and protective efficacy following the intranasal administration of inactivated porcine reproductive and respiratory syndrome virus (PRRSV) loaded in polylactic acid (PLA) nanoparticles coupled with heat-labile enterotoxin subunit B (LTB) and dimethyldioctadecylammonium bromide (DDA). Here, 42- to 3-week-old PRRSV-free pigs were randomly allocated into 7 groups of 6 pigs each. Two groups represented the negative (nonvaccinated pigs/nonchallenged pigs, NoVacNoChal) and challenge (nonvaccinated/challenged, NoVacChal) controls. The pigs in the other 5 groups, namely, PLA nanoparticles/challenged (blank NPs), LTB-DDA coupled with PLA nanoparticles/challenged (adjuvant-blank NPs), PLA nanoparticles-encapsulating inactivated PRRSV/challenged (KNPs), LTB-DDA coupled with PLA nanoparticles loaded with inactivated PRRSV/challenged pigs (adjuvant-KNPs) and inactivated PRRSV/challenged pigs (inactivated PRRSV), were intranasally vaccinated with previously described vaccines at 0, 7 and 14 days post-vaccination (DPV). Serum and nasal swab samples were collected weekly and assayed by ELISA to detect the presence of IgG and IgA, respectively. Viral neutralizing titer (VNT) in sera, IFN-γ-producing cells and IL-10 secretion in stimulated peripheral blood mononuclear cells (PBMCs) were also measured. The pigs were intranasally challenged with PRRSV-2 at 28 DPV and necropsied at 35 DPV, and then macro- and microscopic lung lesions were evaluated. The results demonstrated that following vaccination, adjuvant-KNP-vaccinated pigs had significantly higher levels of IFN-γ-producing cells, VNT and IgG in sera, and IgA in nasal swab samples and significantly lower IL-10 levels than the other vaccinated groups. Following challenge, the adjuvant-KNP-vaccinated pigs had significantly lower PRRSV RNA and macro- and microscopic lung lesions than the other vaccinated groups. In conclusion, the results of the study demonstrated that adjuvant-KNPs are effective in eliciting immune responses against PRRSV and protecting against PRRSV infections over KNPs and inactivated PRRSV and can be used as an adjuvant for intranasal PRRSV vaccines.
Nur Adibah Mohidem, Nik Norasma Che’Ya, Abdul Shukor Juraimi
et al.
Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.