Search Results (6,839)

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications. Full article

Optimizing nutrient management in organic polytunnel production remains challenging due to the limited availability of field-based knowledge on the mineralization dynamics of organic fertilizers. At the same time, microalgae-based products such as Chlorella vulgaris have gained increasing attention in recent research, yet their interactions with nutrient supply intensity are not well understood. This study aimed to evaluate the effects of increasing nutrient supply intensities (34, 116, and 189 kg ha⁻¹ N from different organic sources), in combination with C. vulgaris foliar application, on the crop performance of kapia pepper and a subsequent leafy green crop under on-farm organic polytunnel conditions on soil with moderate organic matter content. Increasing production intensity did not result in significant improvements in pepper yield or vegetative biomass (p > 0.05), and no significant residual effects of nutrient supply were detected in the yield of the subsequent leafy green crop (p: 0.08–0.94). C. vulgaris treatment showed predominantly non-significant but positive trends in several parameters, but only in combination with high-intensity technology, while reducing the total pest damage of the thrips and stinkbug index up to 15.7% in most technology variations. These results indicate that the effects of C. vulgaris may be strongly context-dependent and confirm that increasing the intensity of nutrient supply may carry the risks of conventionalization of organic farming practices. Full article

Water scarcity is one of the main constraints on maize production in semiarid regions, making it essential to adopt management strategies that reconcile water savings, crop resilience, and economic viability. This study evaluated the effects of deficit irrigation strategies integrated with the use of bioinputs on physiological, productive, and economic parameters of maize grown under field conditions in the Brazilian semiarid region over two growing seasons (2023 and 2024). The experiment was conducted using a randomized complete block design with a split-plot arrangement. Irrigation strategies comprised full irrigation (FI; 100% of crop water requirements), continuous deficit irrigation (RD50%; 50% throughout the crop cycle), and stage-specific controlled deficit irrigation (50%) imposed during the vegetative (CDV50%), flowering/grain formation (CDF50%), and grain-filling (CDG50%) stages, while seed treatments involved inoculation with Bacillus aryabhattai, coinoculation with B. aryabhattai + Azospirillum brasilense, and control treatments. Physiological variables, yield components, water use efficiency, the crop sensitivity coefficient to water deficit (Ky), and economic indicators were assessed. Controlled deficits irrigation, particularly under CDV50%, maintained grain yield comparable to FI (6465.80 kg ha⁻¹, in second growing season), whereas RD50% reduced yield in 26%. Inoculation treatments enhanced gas exchange, carboxylation efficiency, and water use efficiency, resulting in higher agricultural income under specific production systems. The CDV50% strategy combined with coinoculation showed the greatest potential as a sustainable approach for maize production in semiarid environments and reduced the water use by up to 18.9%. Full article

Accurate estimation of crop development, water use and yield is essential for improving irrigation management in Mediterranean agricultural systems under increasing climate variability. However, many crop models require extensive input data and technical expertise, limiting their operational use by farmers and technicians. This study proposes an integrated agro-modelling framework that combines thermal time modelling, satellite-derived vegetation indices and simplified yield estimation approaches to assess maize phenology, crop water use and productivity under real farming conditions. A key component of the framework is the use of the Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series to dynamically identify crop growth stages and derive actual basal crop coefficients (K_{cb act}), enabling the estimation of actual crop transpiration (T_{c act}). These NDVI-based estimates of actual K_cb and T_c were evaluated against simulations from the previously calibrated soil water balance model SIMDualKc. The results showed that the temporal profiles of the NDVI successfully captured the progression of the maize growth stages, although some discrepancies were observed during early stages of development due to the effects of the soil background and the satellite revisit intervals. An empirical relationship between the NDVI and K_cb was developed using multi-year observations and model simulations, improving crop transpiration estimation under field conditions. The NDVI-based approach adequately reproduced daily transpiration dynamics with good agreement with SIMDualKc simulations, yielding RMSE values of 0.11–0.69 mm d⁻¹ and errors generally below 21% of the mean transpiration rate. Seasonal transpiration estimates showed stronger agreement once canopy cover reached its maximum. The integrated AEZ–Stewart modelling framework incorporating NDVI-based transpiration estimations provided accurate yield predictions, with RMSE values of 1.7–2.3 t ha⁻¹ (representing less than 14% of the observed yields). Overall, the proposed framework demonstrates strong potential as a practical and scalable decision-support tool for irrigation management and yield assessment in Mediterranean maize systems. Its novelty lies in the operational integration of NDVI-derived crop development and transpiration estimates within a simplified yield modelling structure, offering a transferable approach applicable to other regions and cropping systems where satellite data are available. Full article

Cucumber (Cucumis sativus L.) is a globally significant vegetable crop, and its fruit quality remains a major focus of research. The hollow-heart trait, characterized by internal cracks or cavities, severely compromises both the commercial value and edible quality of cucumber fruit. In this study, a six-generation segregating population (P₁, P₂, F₁, F₂, BC₁P₁, BC₁P₂) was developed from the parental lines “JZ6-1-2” and “D0432-3-4”. BSA-seq was employed to map candidate genomic regions associated with the hollow-heart trait to chromosomes 2, 3, and 7. Subsequently, a major QTL for the trait was delineated on chromosome 7, spanning a region containing 98 genes. Comparative RNA-seq between the parental lines identified 2141 differentially expressed genes. The integration of QTL mapping and RNA-seq data revealed 11 candidate genes residing within the key QTL interval. Through further validation via qRT-PCR, gene sequence comparison, and gene annotation, Csa7G039280 was identified as a promising candidate gene regulating hollow-heart formation, potentially via the lignin biosynthesis pathway. The identification of these candidate regions and genes provides critical information for molecular breeding aimed at developing non-hollow-heart cucumber varieties, thereby enhancing the understanding of the genetic regulatory mechanisms underlying this economically important trait. Full article

Fine-scale crop type information is essential for agricultural monitoring, irrigation management, and food security assessment. This study mapped three major crops—wheat, corn, and sunflower—in the Hetao Irrigation District, China, using multi-temporal Sentinel-2 optical imagery and Sentinel-1 SAR observations at the parcel scale. A multi-source feature set, including spectral bands, vegetation and red-edge indices, moisture-related variables, radar backscatter coefficients, and derived radar features, was constructed from the full growing season. An LSTM network was used to learn temporal representations of crop phenological dynamics, and the resulting embeddings were then combined with traditional machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for final classification. The results show that the hybrid framework substantially improves classification performance compared with the corresponding non-LSTM classifiers. Among all tested models, XGBoost + LSTM achieved the best performance, with an overall accuracy of 93.61%, a Kappa coefficient of 91.66%, and a mean IoU of 87.41%. The class-wise F1-scores were 85.61% for wheat, 97.22% for corn, and 87.27% for sunflower. Additional experiments further confirmed the advantages of parcel-based aggregation in improving spatial consistency and reducing mixed-field noise. The proposed framework provides a promising parcel-scale workflow for crop type mapping in fragmented irrigation districts, while its transferability across years and regions still requires further validation. Full article

Leaf water content (LWC) is a key physiological indicator for assessing crop water status. However, its spectral response may vary under different irrigation practices, which limits the general applicability of existing models. This study aims to develop irrigation-specific LWC estimation models for spring wheat based on UAV multispectral imagery. Field experiments were conducted during two growing seasons (2023–2024) under three irrigation methods, with five water treatments and three replicates, resulting in a total of 45 experimental plots. Multispectral data and in situ measurements were collected at key growth stages. Irrigation-dependent sensitive vegetation indices were identified through correlation analysis, and machine learning models, including Random Forest (RF), Multiple Linear Regression (MLR), and Backpropagation Neural Network (BPNN), were constructed and evaluated using a five-fold cross-validation framework. The results showed that spectral sensitivity to LWC varied significantly across irrigation methods, with different dominant indicators under FD, ND, and MD. Model performance also exhibited irrigation-dependent differences. Among the three models, RF showed the most stable performance, achieving mean R² values of 0.70, 0.74, and 0.62 and corresponding RMSE values of 0.04, 0.06, and 0.08 under FD, ND, and MD, respectively. In contrast, MLR showed lower predictive accuracy, while BPNN exhibited limited robustness under the current dataset, particularly under ND. These findings highlight the importance of irrigation-specific modeling strategies for improving LWC estimation reliability. Full article

Diaporthe spp. are among the most serious pathogens of soybean. Many different Diaporthe species can infect soybean plants. The species differ in their aggressiveness or virulence and in the severity of the damage they cause. Resistance breeding in soybean has been performed for only two Diaporthe species, so far. It would be very advantageous to identify soybean cultivars with resistance against other Diaporthe species as well, both as sources of resistance for breeding and to inform farmers which cultivars should be planted when a given Diaporthe species shows high incidence. We performed greenhouse experiments to differentiate levels of resistance using the Stem Cut and Stem Wound methods for inoculation of the plants with Diaporthe. Symptom severity was rated visually, and at 5 dpi the level of lipid peroxidation (LP), activity of superoxide dismutase (SOD), total phenolics and total flavonoids were measured. Among the four Diaporthe species tested, D. caulivora was most aggressive, followed by D. longicolla. Of the cultivars evaluated, Magnolia exhibited the highest level of resistance with no significant differences observed among the other cultivars. Although biochemical responses could be observed, it was impossible to determine the specific response responsible for elevated resistance in Magnolia. Full article

Chinese cabbage is one of the major vegetable crops in northern Asia. Its leaves are the major organ for photosynthesis and production, and leaf color directly influences its yield and quality. Here, we obtained a pale green mutant pgm3. This mutant line was derived from EMS mutagenesis of Chinese cabbage DH line FT. pgm3 exhibited chlorosis and etiolation, delayed growth, reduced photosynthetic pigment content and net photosynthetic rates, and impaired development of the chloroplast inner membrane system. Genetic analysis revealed that the pale green phenotype was controlled by a single recessive nuclear gene, Brpgm3. Mutmap analysis indicated that Brpgm3 is located on a 13.9 Mb region in A03. Within this region, a single SNP (A03: 7194530) with an SNP-index of 1, located in BraA03g015750.3C (BrClpC1), was identified from 40 differential SNPs. KASP genotyping demonstrated that the SNP co-segregated with the pale green phenotype in the F₂ population. Sanger sequencing confirmed a G-to-A SNP in exon 4 of BrClpC1, which resulted in an amino acid substitution from S to G. Furthermore, multiple sequence alignment of homologs from 28 species demonstrated that this mutated residue is highly conserved. BrClpC1 was predominantly expressed in leaves and exhibited the highest transcript abundance among the nine members of the Class I Clp gene family in Brassica rapa. This is the first report identifying ClpC1 in Brassica crops. Our results not only confirmed BrClpC1 as a strong candidate gene for the pale green mutant of Chinese cabbage, but also highlighted BrClpC1 as a target for chloroplast biology research in Brassica crops. Full article

Sea fennel (Crithmum maritimum L.) is an emerging crop valued for its nutritional and sensory properties and has been reported to exert health-promoting effects, including antioxidant, anti-inflammatory, antimicrobial, and cardioprotective activities, as well as potential benefits for gut health and metabolic regulation. Building on these features, the present study aimed to unlock the potential of sea fennel to produce novel pickles. Two independent batches were prepared using young leaves and stems of sea fennel fermented in brine. After fermentation, salt concentration was standardized in all prototypes, and two types of vinegar (apple and wine) were added at four acetic acid levels (0.05%, 0.2%, 0.5%, and 0.7%). All prototypes were subsequently subjected to mild pasteurization. During fermentation, physicochemical and microbiological parameters were monitored, while after pasteurization additional physicochemical, microbiological, volatile organic compound (VOCs), and sensory analyses were performed during storage. In both batches and across all prototypes, fermentation resulted in a significant pH decrease, dominance of lactic acid bacteria, inhibition of Enterobacteriaceae, and a gradual increase in yeasts. Following vinegar addition and pasteurization, pH, titratable acidity, and salt content remained stable over six months of storage in most prototypes, particularly those with 0.2% acetic acid. Pasteurization effectively inactivated lactic acid bacteria and Enterobacteriaceae in all prototypes, whereas yeasts and mesophilic bacteria persisted in low-acidity samples (0.05%). Therefore, the 0.05% acidity samples were later excluded due to mid-stage microbial spoilage. Batch-dependent differences were observed in color and sensory attributes, with batch 2 showing higher overall stability mainly in acidic flavor and aroma, particularly in prototypes with 0.2% acidity. VOCs analysis revealed profiles primarily driven by batch variation, with secondary modulation by vinegar type: sesquiterpenes remained stable, while γ-terpinene, limonene, and p-cymene were the dominant compounds, with greater stability observed in batch 2. Overall, the combined use of lactic acid fermentation, vinegar pickling, and mild pasteurization represents a promising strategy for preserving sea fennel and supports its potential as a vegetable crop. Full article

Agri-food processing in Europe generates large quantities of organic residues that remain insufficiently valorized despite their significant biochemical potential. Among these, wastes derived from root vegetables and anthocyanin-rich crops represent a distinct category of non-lignocellulosic biomass characterized by high moisture content, low lignin levels, and substantial concentrations of fermentable carbohydrates and bioactive compounds. This review provides a systematic overview of the origin, composition, and valorization potential of these residues, as well as extraction methods, with particular emphasis on root vegetable processing wastes and pigment-rich agri-food by-products. Valorization options are discussed within an integrated biorefinery perspective, particularly for specific compositional characteristics of the investigated waste streams related to suitable recovery strategies, followed by the conversion of post-extraction residues into secondary products and bioenergy. These options are evaluated in relation to the origin, biochemical profile, and valorization potential of each waste stream, as detailed in the dedicated sections of the review. Cascading utilization strategies are highlighted as a means to improve resource efficiency and reduce environmental burdens compared to single-route treatment options. By integrating information on feedstock characteristics and processing pathways, this review contributes to a better understanding of non-lignocellulosic agri-food wastes and supports the development of sustainable valorization strategies in the European circular bioeconomy. Full article

Salt stress represents a significant abiotic stress factor that adversely affects plant growth and development. It directly inhibits both vegetative and reproductive growth, resulting in substantial reductions in crop yield and quality. Consequently, the identification of salt tolerance genes and the elucidation of their underlying molecular mechanisms are crucial for improving crop salt tolerance and ensuring agricultural productivity. To investigate the molecular basis underlying differential salt tolerance between Zheng58 and PH4CV, we employed pooled sequencing (BSA-seq) using extreme phenotypic individuals from their F₂ population and conducted a comparative transcriptome analysis at the seedling stage of the two genotypes. Phenotypic, physiological, biochemical, and ion content analyses revealed that Zheng58 exhibited significantly superior performance compared to PH4CV under salt stress conditions. BSA-seq analysis identified six genomic regions associated with salt tolerance, encompassing a total of 391 genes. Functional annotation enabled the screening of 151 candidate genes potentially involved in salt stress responses. Transcriptome profiling indicated that differentially expressed genes were significantly enriched in biological processes, particularly plant hormone signal transduction and MAPK signaling pathways. Integrating BSA-seq and transcriptome data, key candidate gene ZmACC2 (Zm00001eb419400) was identified as potentially involved in the regulation of salt tolerance in maize. This gene may modulate Na⁺/K⁺/Ca²⁺ homeostasis and reactive oxygen species metabolism through defense responses mediated by ethylene (ETH) and hydrogen peroxide, as well as through ion homeostasis regulatory pathways. This study provides valuable candidate genes and a theoretical foundation for further dissection of the molecular mechanisms governing salt tolerance in maize. Full article

The perennial grass timothy (Phleum pratense) is an important forage crop in cold temperate regions. It forms three types of tillers: vegetative (VEG), generative (GEN), and non-flowering elongated (ELONG). To understand the influence of plant development and tiller formation on biomass production and the diversity in these traits, a total of 246 wild and domesticated accessions of timothy and the related species, P. nodosum and P. alpinum, were investigated. The length of different plant developmental stages and the formation of different tiller types were studied to test the hypotheses: (1) the proportion (%) of different tiller types affects biomass and is influenced by the lengths of the different plant developmental stages, (2) domestication and breeding have affected the length of developmental stages and proportions of tiller types. While timothy cultivars did not differ significantly from wild accessions in biomass, wild accessions had higher VEG%, which increased with latitude of accession origin. P. nodosum cultivars produced the highest number of ELONG of all accessions and species, and the ELONG% showed a strong positive correlation with biomass. Timothy cultivars showed later emergence and tillering, and reached stem elongation and heading earlier than wild accessions, suggesting that delayed tillering, but an overall faster development, has been favoured during breeding. The time between tillering and stem elongation showed a positive correlation with VEG%. This study reveals large diversity in developmental and tiller traits among accessions, reflecting differences in their domestication and breeding history, and highlighting the importance of considering early developmental traits and ELONG formation for yield and quality in further pre-breeding research. Full article

Fiji, with its many islands and mountainous terrain, has only about 11% of its total land area (2000 km²) suitable for cultivation. Therefore, it aims to meet both energy and food production simultaneously through agricultural photovoltaic (APV) systems. This study proposed an optimal agricultural management of APV system to increase farm income and solve the problem of low vegetable production. The practice is planned based on the data from farmer surveys, field study, simulation analysis, and agricultural market analysis. Firstly, a farmer survey was conducted to gather data on the agricultural activities and income of local farmers. Based on the survey results, field studies with various vegetables were conducted in an APV system. In simulation, yields of lettuce, taro, long bean, and cucumber were estimated in the APV system with different cropping management techniques (planting schedule and plant density). With the average yields of lettuce, taro, long bean, and cucumber at highest plant densities being (72.4, 71.1, 3.9, and 10.8) Mg/ha, respectively, according to economic analysis, the highest gross margin was achieved in taro in the APV system. This study shows that the APV system can increase farmers’ annual household income by 1.19 to 1.38%, which represents a meaningful absolute gain given the low average income levels identified in the farm survey. Full article