Search Results (912)

MicroRNAs (miRNAs) are small, non-coding RNAs that are expressed in a tissue- and temporal-specific manner during development. They have been found to be highly conserved during the evolution of different species. miRNAs regulate the expression of several genes in various organisms, with some regulating the expression of multiple genes with similar or completely unrelated functions. Frequent disease and insect pest infestations severely limit agricultural development. Thus, cultivating resistant crops via miRNA-directed gene regulation in plants, insects, and pathogens is an important aspect of modern breeding practices. To strengthen the application of miRNAs in sustainable agriculture, plant endogenous or exogenous miRNAs have been used for plant breeding. Consequently, the development of biological pesticides based on miRNAs has become an important avenue for future pest control methods. However, selecting the appropriate miRNA according to the desired target traits in the target organism is key to successfully using this technology for pest control. This review summarizes the progress in research on miRNAs in plants and other species involved in regulating plant disease and pest resistance pathways. We also discuss the molecular mechanisms of relevant target genes to provide new ideas for future research on pest and disease resistance and breeding in plants. Full article

As a significant global source of vegetable oil, the oil palm’s ability to withstand abiotic stresses, particularly drought, is crucial for sustainable agriculture. This is especially significant in tropical regions, where water scarcity is becoming more common. Nitrogen, a vital nutrient, plays an essential role in various physiological and biochemical processes in plants, directly influencing growth and stress tolerance. This study investigates the interaction between nitrogen sources (ammonium vs. nitrate) and drought stress in oil palm (Elaeis guineensis) seedlings, which is critical in enhancing productivity in this economically important crop. The experiment evaluated five commercial oil palm genotypes, which were supplied with nitrogen solutions (15 mM NH₄⁺ or NO₃⁻) for 46 days, followed by 30 days of progressive drought. The results showed that drought conditions universally reduced the biomass, with ammonium-fed plants exhibiting greater shoot biomass sensitivity than nitrate-fed plants. Drought also significantly decreased the chlorophyll a, PhiPS2, and root-reducing sugar levels—critical indicators of photosynthetic efficiency and overall plant health. The effects on the root architecture were complex, with ammonium nutrition differentially influencing the lateral root length under well-watered versus drought conditions, highlighting nitrogen forms’ nuanced role in root development. Importantly, substantial genotypic variability was observed in most traits, affecting the responses to both the nitrogen source and drought stress. This variability suggests that certain genotypes may be better suited to cultivation in specific environmental conditions, particularly drought-prone areas. In conclusion, this study underscores the intricate interplay between nitrogen nutrition, genotypic variability, and drought tolerance in oil palm seedlings. These findings highlight the need to integrate these factors into agricultural management strategies to improve resilience and productivity in oil palm plantations. Full article

Weeds are one of the primary concerns in agriculture since they compete with crops for nutrients and water, and they also attract insects and pests and are, therefore, hindering crop yield. Moreover, seasonal labour shortages necessitate the automation of such agricultural tasks using machines. For this reason, advances in agricultural robotics have led to many attempts to produce autonomous machines that aim to address the task of weeding both effectively and efficiently. Some of these machines are implementing chemical-based weeding methods using herbicides. The challenge for these machines is the targeted delivery of the herbicide so that the environmental impact of the chemical is minimised. However, environmental concerns drive weeding robots away from herbicide use and increasingly utilise mechanical weeding tools or even laser-based devices. In this case, the challenge is the development and application of effective tools. This paper reviews the progress made in the field of weeding robots during the last decade. Trends during this period are identified, and the current state-of-the-art works are highlighted. Finally, the paper examines the areas where the current technological solutions are still lacking, and recommendations on future directions are made. Full article

Ecuadorian cocoa possesses important organoleptic characteristics such as aroma and flavor, called fine and aromatic cocoa. The objective of this study was to evaluate the initial growth responses of young cocoa seedlings to glyphosate in a dose progression in 45 cocoa plants (5 months old), which were transplanted into pots with substrate adjusted to pH 6.0–6.5. Glyphosate doses (0 to 904 g e.e. ha⁻¹) were applied every two weeks, evaluating the impact at 30 and 60 days post-application. Data on shikimate accumulation parameters, chlorophyll content and PSII quantum efficiency were subjected to multivariate analysis using a three-dimensional scatter plot. The results indicated that high concentrations of glyphosate contributed to higher shikimate concentration and lower PSII quantum efficiency. The findings for the variables crop damage, stem height and stem diameter were evaluated by ANOVA. Similarities were reported between the results of the variables height and diameter, and significant differences (p < 0.05) in the variable crop damage for all treatments were also reported. In terms of phytotoxic reaction and growth parameters, the most efficient treatment was DO4, since the seedlings with this dosage showed a low percentage of damage (10%) and the best indices in terms of height and diameter. The least efficient treatment was D15. The control plants (DO1) showed a crop damage of >50% because the absence of control favored weed proliferation. These indications highlight the need to adjust glyphosate doses according to the specific needs of each crop and the development stage of the plant in order to reduce negative effects and maximize potential benefits. Full article

Crop monitoring with unmanned aerial vehicles (UAVs) has the potential to reduce field monitoring costs while increasing monitoring frequency and improving efficiency. However, the utilization of RGB-based UAV imagery for crop-specific monitoring, especially for sugarcane, remains limited. This work proposes a UAV platform with an RGB camera as a low-cost solution to monitor sugarcane fields, complementing the commonly used multi-spectral methods. This new approach optimizes the RGB vegetation indices for accurate prediction of sugarcane growth, providing many improvements in scalable crop-management methods. The images were captured by a DJI Mavic Pro drone. Four RGB vegetation indices (VIs) (GLI, VARI, GRVI, and MGRVI) and the crop surface model plant height (CSM_PH) were derived from the images. The fractional vegetation cover (FVC) values were compared by image classification. Sugarcane plant height predictions were generated using two machine learning (ML) algorithms—multiple linear regression (MLR) and random forest (RF)—which were compared across five predictor combinations (CSM_PH and four VIs). At the early stage, all VIs showed significantly lower values than later stages (p < 0.05), indicating an initial slow progression of crop growth. MGRVI achieved a classification accuracy of over 94% across all growth phases, outperforming traditional indices. Based on the feature rankings, VARI was the least sensitive parameter, showing the lowest correlation (r < 0.5) and mutual information (MI < 0.4). The results showed that the RF and MLR models provided better predictions for plant height. The best estimation results were observed withthe combination of CSM_PH and GLI utilizing RF model (R² = 0.90, RMSE = 0.37 m, MAE = 0.27 m, and AIC = 21.93). This study revealed that VIs and the CSM_PH derived from RGB images captured by UAVs could be useful in monitoring sugarcane growth to boost crop productivity. Full article

Slow/controlled-release fertilizers (SRFs/CRFs) occupy a critical position in agricultural advancement, enhancing productivity and sustainability by regulating nutrient release, improving fertilizer efficiency, reducing pollution, and promoting lasting agricultural progress. To attain an in-depth understanding of the current landscape, hotspots, and development trends in SRF/CRF research, this study employed the Bibliometrix toolkit in R, VOSviewer, and CiteSpace for the statistical and graphical analysis of pertinent literature in the Web of Science Core Collection (WOSCC) database from 1990 to 2023. In this study, several dimensions were evaluated to assess the research scope and impact, including the quantity of published articles, authorship, citation frequency, keywords, institutional affiliations, publication journals, and source countries. The results indicate a significant increase in scholarly publications related to SRFs/CRFs from 1990 to 2023, totaling 1676 published papers across 77 subject categories. Research activities spanned 69 countries/regions, with China and the USA leading contributions. A total of 1691 research institutions published on SRFs/CRFs, with the University of Florida, the Chinese Academy of Sciences, and China’s Shandong Agricultural University being preeminent. HortScience, Science of the Total Environment, and Communications in Soil Science and Plant Analysis were the top three journals. Keyword co-occurrence and burst analysis disclosed that current research primarily focuses on several key areas: nitrogen (N) use efficiency, the processes of nitrification and denitrification, degradation, the use of phosphate (P) fertilizers, urea, and factors affecting crop growth and quality. The findings revealed several critical areas and trends within the sphere of SRFs/CRFs, with future research specifically directed towards developing cost-effective, efficacious, and environmentally friendly alternatives. Furthermore, future progress will concentrate on addressing the enduring environmental ramifications of SRF/CRF utilization. Full article

Analyzing the impacts of climate change on phytosanitary problems in Brazil is crucial due to the country’s special role in global food security as one of the largest producers of essential commodities. This review focuses on the effects of climate change on plant diseases and discusses its main challenges in light of Brazil’s diverse agricultural landscape. To assess the risk of diseases caused by fungi, bacteria, viruses, oomycetes, nematodes, and spiroplasms, we surveyed 304 pathosystems across 32 crops of economic importance from 2005 to 2022. Results show that diseases caused by fungi account for 79% of the pathosystems evaluated. Predicting the occurrence of diseases in a changing climate is a complex challenge, and the continuity of this work is strategic for Brazil’s agricultural defense. The future risk scenarios analyzed here aim to help guide disease mitigation for cropping systems. Despite substantial progress and ongoing efforts, further research will be needed to effectively prevent economic and environmental damage. Full article

The Ficus genus, having radiated from the tropics and subtropics to the temperate zone worldwide, is the largest genus among woody plants, comprising over 800 species. Evolution of the Ficus species results in genetic diversity, global radiation and geographical differentiations, suggesting adaption to diverse environments and coping with stresses. Apart from familiar physiological changes, such as stomatal closure and alteration in plant hormone levels, the Ficus species exhibit a unique mechanism in response to abiotic stress, such as regulation of leaf temperature and retention of drought memory. The stress-resistance genes harbored by Ficus result in effective responses to abiotic stress. Understanding the stress-resistance mechanisms in Ficus provides insights into the genetic breeding toward stress-tolerant crop cultivars. Following upon these issues, we comprehensively reviewed recent progress concerning the Ficus genes and relevant mechanisms that play important roles in the abiotic stress responses. These highlight prospectively important application potentials of the stress-resistance genes in Ficus. Full article

With global warming, drought stress is becoming increasingly severe, causing serious impacts on crop yield and quality. In order to survive under adverse conditions such as drought stress, plants have evolved a certain mechanism to cope. The tolerance to drought stress is mainly improved through the synergistic effect of regulatory pathways, such as transcription factors, phytohormone, stomatal movement, osmotic substances, sRNA, and antioxidant systems. This study summarizes the research progress on plant drought resistance, in order to provide a reference for improving plant drought resistance and cultivating drought-resistant varieties through genetic engineering technology. Full article

The fall armyworm (Spodoptera frugiperda) poses a substantial threat to many important crops worldwide, emphasizing the need to develop and implement advanced technologies for effective pest control. CRISPR/Cas9, derived from the bacterial adaptive immune system, is a prominent tool used for genome editing in living organisms. Due to its high specificity and adaptability, the CRISPR/Cas9 system has been used in various functional gene studies through gene knockout and applied in research to engineer phenotypes that may cause economical losses. The practical application of CRISPR/Cas9 in diverse insect orders has also provided opportunities for developing strategies for genetic pest control, such as gene drive and the precision-guided sterile insect technique (pgSIT). In this review, a comprehensive overview of the recent progress in the application of the CRISPR/Cas9 system for functional gene studies in S. frugiperda is presented. We outline the fundamental principles of applying CRISPR/Cas9 in S. frugiperda through embryonic microinjection and highlight the application of CRISPR/Cas9 in the study of genes associated with diverse biological aspects, including body color, insecticide resistance, olfactory behavior, sex determination, development, and RNAi. The ability of CRISPR/Cas9 technology to induce sterility, disrupt developmental stages, and influence mating behaviors illustrates its comprehensive roles in pest management strategies. Furthermore, this review addresses the limitations of the CRISPR/Cas9 system in studying gene function in S. frugiperda and explores its future potential as a promising tool for controlling this insect pest. Full article

The frequent occurrence of global climate change and natural disasters highlights the importance of precision agricultural monitoring, yield forecasting, and early warning systems. The data assimilation method provides a new possibility to solve the problems of low accuracy of yield prediction, strong dependence on the field, and poor adaptability of the model in traditional agricultural applications. Therefore, this study makes a systematic literature retrieval based on Web of Science, Scopus, Google Scholar, and PubMed databases, introduces in detail the assimilation strategies based on many new remote sensing data sources, such as satellite constellation, UAV, ground observation stations, and mobile platforms, and compares and analyzes the progress of assimilation models such as compulsion method, model parameter method, state update method, and Bayesian paradigm method. The results show that: (1) the new remote sensing platform data assimilation shows significant advantages in precision agriculture, especially in emerging satellite constellation remote sensing and UAV data assimilation. (2) SWAP model is the most widely used in simulating crop growth, while Aquacrop, WOFOST, and APSIM models have great potential for application. (3) Sequential assimilation strategy is the most widely used algorithm in the field of agricultural data assimilation, especially the ensemble Kalman filter algorithm, and hierarchical Bayesian assimilation strategy is considered to be a promising method. (4) Leaf area index (LAI) is considered to be the most preferred assimilation variable, and the study of soil moisture (SM) and vegetation index (VIs) has also been strengthened. In addition, the quality, resolution, and applicability of assimilation data sources are the key bottlenecks that affect the application of data assimilation in the development of precision agriculture. In the future, the development of data assimilation models tends to be more refined, diversified, and integrated. To sum up, this study can provide a comprehensive reference for agricultural monitoring, yield prediction, and crop early warning by using the data assimilation model. Full article

Accurately quantifying flora and their respective anatomical structures within natural ecosystems is paramount for both botanical breeders and agricultural cultivators. For breeders, precise plant enumeration during the flowering phase is instrumental in discriminating genotypes exhibiting heightened flowering frequencies, while for growers, such data inform potential crop rotation strategies. Moreover, the quantification of specific plant components, such as flowers, can offer prognostic insights into the potential yield variances among different genotypes, thereby facilitating informed decisions pertaining to production levels. The overarching aim of the present investigation is to explore the capabilities of a neural network termed GhP2-YOLO, predicated on advanced deep learning techniques and multi-target tracking algorithms, specifically tailored for the enumeration of rapeseed flower buds and blossoms from recorded video frames. Building upon the foundation of the renowned object detection model YOLO v8, this network integrates a specialized P2 detection head and the Ghost module to augment the model’s capacity for detecting diminutive targets with lower resolutions. This modification not only renders the model more adept at target identification but also renders it more lightweight and less computationally intensive. The optimal iteration of GhP2-YOLOm demonstrated exceptional accuracy in quantifying rapeseed flower samples, showcasing an impressive mean average precision at 50% intersection over union metric surpassing 95%. Leveraging the virtues of StrongSORT, the subsequent tracking of rapeseed flower buds and blossom patterns within the video dataset was adeptly realized. By selecting 20 video segments for comparative analysis between manual and automated counts of rapeseed flowers, buds, and the overall target count, a robust correlation was evidenced, with R-squared coefficients measuring 0.9719, 0.986, and 0.9753, respectively. Conclusively, a user-friendly “Rapeseed flower detection” system was developed utilizing a GUI and PyQt5 interface, facilitating the visualization of rapeseed flowers and buds. This system holds promising utility in field surveillance apparatus, enabling agriculturalists to monitor the developmental progress of rapeseed flowers in real time. This innovative study introduces automated tracking and tallying methodologies within video footage, positioning deep convolutional neural networks and multi-target tracking protocols as invaluable assets in the realms of botanical research and agricultural administration. Full article

Quinoa is a healthy food that possesses high levels of protein that is enriched for dietary essential amino acids. The crop is highly diverse and well-adapted to changing climatic conditions. In spite of being vulnerable to pests and diseases, the development of new resistant varieties is possible. Taking advantage of this genetic variability is crucial for breeding programs, especially to adapt quinoa to the shifting needs of producers. In this study, 25 Peruvian accessions and two commercial varieties were characterized and agronomically evaluated in the Peruvian Pacific desert. Specific methodologies and descriptors of existing crops were used, analyzing a total of 24 quantitative and 23 qualitative variables with 15 repetitions per accession. The data were processed using descriptive statistics and a multivariate analysis. The results showed a high variability in morphological characteristics, with an area under the disease progress curve (AUDPC) of the presence of mildew between 529 and 1725, highlighting ACC06 with a lower severity of mildew. The percentage of saponins varied between 0.04 and 0.21 percent, with ACC06 being the one with the lowest percentage. Regarding the crop yield, it ranged between 0.35 and 8.80 t ha⁻¹, highlighting the high-yielding accessions ACC55 and ACC14. These results were promising for the improvement of quinoa yield in the production conditions of the Peruvian Pacific desert. Full article

In the contemporary context, pest detection is progressively moving toward automation and intelligence. However, current pest detection algorithms still face challenges, such as lower accuracy and slower operation speed in detecting small objects. To address this issue, this study presents a crop pest target detection algorithm, GLU-YOLOv8, designed for complex scenes based on an enhanced version of You Only Look Once version 8 (YOLOv8). The algorithm introduces the SCYLLA-IOU (SIOU) loss function, which enhances the model generalization to various pest sizes and shapes by ensuring smoothness and reducing oscillations during training. Additionally, the algorithm incorporates the Convolutional Block Attention Module (CBAM) and Locality Sensitive Kernel (LSK) attention mechanisms to boost the pest target features. A novel Gated Linear Unit CONV (GLU-CONV) is also introduced to enhance the model’s perceptual and generalization capabilities while maintaining performance. Furthermore, GLU-YOLOv8 includes a small-object detection layer with a feature map size of 160 × 160 to extract more features of small-target pests, thereby improving detection accuracy and enabling more precise localization and identification of small-target pests. The study conducted a comparative analysis between the GLU-YOLOv8 model and other models, such as YOLOv8, Faster RCNN, and RetinaNet, to evaluate detection accuracy and precision. In the Scolytidae forestry pest dataset, GLU-YOLOv8 demonstrated an improvement of 8.2% in [email protected] for small-target detection compared to the YOLOv8 model, with a resulting [email protected] score of 97.4%. Specifically, on the IP102 dataset, GLU-YOLOv8 outperforms the YOLOv8 model with a 7.1% increase in [email protected] and a 5% increase in [email protected]:0.95, reaching 58.7% for [email protected]. These findings highlight the significant enhancement in the accuracy and recognition rate of small-target detection achieved by GLU-YOLOv8, along with its efficient operational performance. This research provides valuable insights for optimizing small-target detection models for various pests and diseases. Full article

When plant-available phosphorus (P) is lost from a soil solution, it often accumulates in the soil as a pool of unavailable legacy P. To acquire legacy P, plants employ recovery strategies, such as forming associations with soil microbes. However, the degree to which plants rely on microbial associations for this purpose varies with crop domestication and subsequent breeding. Here, by generating microbial co-occurrence networks, we sought to explore rhizosphere bacterial interactions in low-P conditions and how they change with tomato domestication and breeding. We grew wild tomato, traditional tomato (developed circa 1900), and modern tomato (developed circa 2020) in high-P and low-P soil throughout their vegetative developmental stage. Co-occurrence network analysis revealed that as the tomatoes progressed along the stages of domestication, the rhizosphere microbiome increased in complexity in a P deficit. However, with the addition of P fertilizer, the wild tomato group became more complex, surpassing the complexity of traditional and modern tomato, suggesting a high degree of responsiveness in the rhizosphere microbiome to P fertilizer by wild tomato relatives. By illustrating these changing patterns of network complexity in the tomato rhizosphere microbiome, we can further understand how plant domestication and breeding have shaped plant–microbe interactions. Full article