Search Results (1,502)

Grafting serves as a crucial propagation technique for superior Camellia oleifera varieties, where rootstock–scion compatibility significantly determines survival and growth performance. To systematically evaluate grafting compatibility in this economically important woody oil crop, we examined 15 rootstock–scion combinations using ‘Xianglin 210’ as the scion, assessing growth traits and conducting physiological assays (enzymatic activities of SOD and POD and levels of ROS and IAA) at multiple timepoints (0–32 days post-grafting). The results demonstrated that Comb. 4 (Xianglin 27 rootstock) exhibited superior compatibility, characterized by systemic antioxidant activation (peaking at 4–8 DPG), rapid auxin accumulation (4 DPG), and efficient sugar allocation. Transcriptome sequencing and WGCNA analysis identified 3781 differentially expressed genes, with notable enrichment in stress response pathways (Hsp70, DnaJ) and auxin biosynthesis (YUCCA), while also revealing key hub genes (FKBP19) associated with graft-healing efficiency. These findings establish that successful grafting in C. oleifera depends on coordinated rapid redox regulation, auxin-mediated cell proliferation, and metabolic reprogramming, with Comb. 4 emerging as the optimal rootstock choice. The identified molecular markers not only advance our understanding of grafting mechanisms in woody plants but also provide valuable targets for future breeding programs aimed at improving grafting success rates in this important oil crop. Full article

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction. Full article

Anaerobic germination (AG) is a pivotal trait for successful direct-seeded rice cultivation, encompassing rainfed and irrigated conditions. Elite rice cultivars are often vulnerable to flooding during germination, resulting in poor crop establishment. This drawback has led to the exploration of AG-tolerant rice landraces, which offer valuable insights into the genetic underpinnings of AG tolerance. Over the years, substantial progress has been made in identifying significant quantitative trait loci (QTLs) associated with AG tolerance, forming the basis for targeted breeding efforts. However, the intricate gene regulatory network governing AG tolerance remains enigmatic. This comprehensive review presents recent advances in understanding the physiological and genetic mechanisms underlying AG tolerance. It focuses on their practical implications in breeding elite rice cultivars tailored for direct-seeding systems. Full article

Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases. Full article

This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices. Full article

Cannabis sativa L. is a versatile plant with applications in various sectors such as agriculture, medicine, food, and cosmetics. The therapeutic properties of cannabis are often linked to its secondary compounds. The worldwide cannabis market is undergoing swift changes due to varying legal frameworks. Medicinal cannabis (as a heterozygous and dioecious species) is distinct from most annual crops grown in controlled environments, typically propagated through stem cutting rather than seeds to ensure genetic uniformity. Consequently, as with any commercially cultivated crop, biomass yield plays a crucial role in overall productivity. The key factors involved in cultivation conditions, such as successful root establishment, stress tolerance, and the production cycle duration, are critical for safeguarding, improving, and optimizing plant yield. Grafting is a long-established horticultural practice that mechanically joins the scion and rootstock of distinct genetic origins by merging their vascular systems. This approach can mitigate undesirable traits by leveraging the strengths of particular plants, proving beneficial to various applications. Grafting is not used commercially in Cannabis. Only three very recent investigations suggest that grafting holds significant promise for enhancing both the agronomic and medicinal potential of Cannabis. This review critically examines the latest advancements in cannabis grafting and explores prospects for improving biomass (stem, root, flower, etc.) yield and secondary metabolite production. Full article

Biogenic silver nanoparticles (AgNPs) were synthesized via a green chemistry strategy using wheat extract and subsequently functionalized with a carboxymethyl chitosan–cinnamaldehyde (CMC=CIN) conjugate through covalent imine bonding. The resulting nanohybrid (AgNP–CMC=CIN) was extensively characterized to confirm successful biofunctionalization: UV–Vis spectroscopy revealed characteristic cinnamaldehyde absorption peaks; ATR-FTIR spectra confirmed polymer–terpene bonding; and TEM analysis evidenced uniform nanoparticle morphology. Dynamic light scattering (DLS) measurements indicated an increase in hydrodynamic size upon coating (from 59.46 ± 12.63 nm to 110.17 ± 4.74 nm), while maintaining low polydispersity (PDI: 0.29 to 0.27) and stable surface charge (zeta potential ~ −30 mV), suggesting colloidal stability and homogeneous polymer encapsulation. Antifungal activity was evaluated against Fusarium oxysporum, Penicillium citrinum, Aspergillus niger, and Aspergillus brasiliensis. The minimum inhibitory concentration (MIC) against F. oxysporum was significantly reduced to 83 μg/mL with AgNP–CMC=CIN, compared to 708 μg/mL for uncoated AgNPs, and was comparable to the reference fungicide tebuconazole (52 μg/mL). Seed priming with AgNP–CMC=CIN led to improved germination (85%) and markedly reduced fungal colonization, while maintaining a favorable phytotoxicity profile. These findings highlight the potential of polysaccharide-terpene-functionalized biogenic AgNPs as a sustainable alternative to conventional fungicides, supporting their application in precision agriculture and integrated crop protection strategies. Full article

Triticale (Triticosecale Wittmack) is a versatile forage crop valued for its high yield, balanced nutrition, and environmental adaptability. However, the dough-stage triricale has higher dry matter and starch content but lower water-soluble carbohydrate levels than earlier stages, posing fermentation challenges that may impair silage quality. This study aimed to investigate the effects of lactic acid bacteria inoculation on the fermentation quality, bacterial community, and metabolome of whole-plant triticale silage at the dough stage. Fresh triticale was ensiled for 30 days without or with an inoculant containing Lactiplantibacillus plantarum and Streptococcus bovis. Fermentation quality, bacterial succession, and metabolic profiles were analyzed at multiple time points. Inoculation significantly improved fermentation quality, characterized by a rapid pH drop, increased lactic acid production, and better preservation of fiber components. Microbial analysis revealed that inoculation successfully established Lactobacillus as the dominant genus while suppressing spoilage bacteria like Enterobacter and Clostridium. Metabolomic analysis on day 30 identified numerous differential metabolites, indicating that inoculation primarily altered pathways related to amino acid and purine metabolism. In conclusion, inoculating dough-stage triticale with this LAB combination effectively directs the fermentation trajectory. It enhances silage quality not only by optimizing organic acid profiles and microbial succession but also by modulating key metabolic pathways, ultimately leading to improved nutrient preservation. Full article

Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m³, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m³, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m³, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r² values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

The pollen coat is the outermost layer of pollen and plays a key role in successful pollination and environmental adaptation. It consists of lipids, proteins, and phenolic compounds that protect pollen from environmental stress, promote hydration, and enable a proper interaction with the stigma. However, many questions remain unanswered, such as what the components of the pollen coat are and how they are formed, as well as how defects in the pollen coat affect the normal function of pollen. This review highlights the molecular mechanisms behind the biosynthesis and transport of pollen coat components and their contributions to pollen hydration, pollination compatibility, and fertility. Moreover, we discuss the role of selected gene families in pollen coat formation and their potential impact on agricultural breeding, paving the way for the breeding of more efficient crops. Full article

Insect pests remain a major threat to agricultural productivity, particularly in open-field cropping systems where conventional monitoring methods are labor-intensive and lack scalability. This study presents the design, implementation, and field evaluation of a neural network-guided smart trap specifically developed to monitor and selectively capture nocturnal insect pests under real agricultural conditions. The proposed trap integrates light and rain sensors, servo-controlled mechanical gates, and a single-layer perceptron neural network deployed on an ATmega-2560 microcontroller by Microchip Technology Inc. (Chandler, AZ, USA). The perceptron processes normalized sensor inputs to autonomously decide, in real time, whether to open or close the gate, thereby enhancing the selectivity of insect capture. The system features a removable tray containing a food-based attractant and yellow and green LEDs designed to lure target species such as moths and flies from the orders Lepidoptera and Diptera. Field trials were conducted between June and August 2023 in La Barca, Jalisco, Mexico, under diverse environmental conditions. Captured insects were analyzed and classified using the iNaturalist platform, with the successful identification of key pest species including Tetanolita floridiana, Synchlora spp., Estigmene acrea, Sphingomorpha chlorea, Gymnoscelis rufifasciata, and Musca domestica, while minimizing the capture of non-target organisms such as Carpophilus spp., Hexagenia limbata, and Chrysoperla spp. Statistical analysis using the Kruskal–Wallis test confirmed significant differences in capture rates across environmental conditions. The results highlight the potential of this low-cost device to improve pest monitoring accuracy, and lay the groundwork for the future integration of more advanced AI-based classification and species recognition systems targeting nocturnal Lepidoptera and other pest insects. Full article

The floral transition in Chinese cabbage (Brassica rapa ssp. pekinensis) is governed by a complex interplay of gene expression and hormonal regulation. Temporal transcriptome profiling was conducted across three developmental stages: pre-bolting (PBS), bolting (BS), and flowering stages (FS), to investigate the underlying molecular mechanisms. A total of 7092 differentially expressed genes (DEGs) were identified, exhibiting distinct expression trajectories during the transition. Moreover, functional enrichment analyses revealed strong associations with plant hormone signaling, MAPK pathways, and developmental regulation processes. Key flowering-related genes, such as BrFLM, BrAP2, BrFD, BrFT, and BrSOC1s displayed antagonistic expression patterns. Hormonal pathways involving auxin, ABA, ET, BR, GA, JA, CK, and SA showed stage-dependent modulation. Further, orphan genes (OGs), especially EARLY FLOWERING 1 (EF1), showed significant upregulation during the transition, which exhibited 1.84-fold and 1.93-fold increases at BS and FS compared to PBS, respectively (p < 0.05). Functional validation through EF1 overexpression (EF1OE) in Arabidopsis consistently promoted early flowering. The expression levels of AtFT and AtSOC1 were significantly upregulated in EF1OE lines compared to wild-type (WT) plants. The findings contribute to understanding the coordinated genetic and hormonal events driving floral development in Chinese cabbage, suggesting EF1 as a candidate for bolting resistance breeding. This work also expands the existing regulatory framework through the successful integration of OGs into the complex floral induction system of Brassica crops. Full article