Agronomy

15 pages, 1804 KB

Open AccessArticle

Risk Assessment and Source Apportionment of Heavy Metals in Agricultural Soil Across Yinchuan, China

by Yiming Liu, Tianzi Yin, Rongguang Shi, Yan Li, Jianjun Ma, Hong Li, Ke Yang, Shiyuan Ding and Xiaodong Li

Agronomy 2025, 15(12), 2726; https://doi.org/10.3390/agronomy15122726 - 26 Nov 2025

Abstract

Approximately 1.1% of global soils exceed the safety thresholds. Yinchuan is one of the key grain production bases, and the quality of its agricultural soil directly impacts the quality of agricultural products. To investigate the heavy metal contamination status of surface agricultural soil [...] Read more.

Approximately 1.1% of global soils exceed the safety thresholds. Yinchuan is one of the key grain production bases, and the quality of its agricultural soil directly impacts the quality of agricultural products. To investigate the heavy metal contamination status of surface agricultural soil in Yinchuan, this study collected 325 agricultural soil samples from the city to analyze the concentrations of five heavy metal elements—As, Hg, Pb, Cd, and Cr—and conducted a risk assessment and quantitative source apportionment of soil heavy metal contamination. The results indicate that the majority of the study area is classified as having a lightly polluted level with moderate ecological risks. The order of the over-standard rates is Hg > Cd > Pb > Cr > As. The soil in the study area is generally weakly alkaline, which has a relatively low impact on the migration and transformation of heavy metal elements. High-value areas of heavy metals are all located near the Yellow River floodplain. They are significantly affected by the agricultural and industrial wastewater discharge from the upper reaches of the Yellow River. The Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model analysis identified the sources of soil heavy metal pollution as natural sources (37.29%), agricultural sources (25.50%), coal combustion sources (20.18%), and industrial-transportation sources (17.04%). The positive matrix factorization (PMF) model explained that the sources of heavy metals in the soil were natural sources (22.42%), agricultural activities (24.46%), coal combustion sources (26.70%), and traffic sources (26.42%). Overall, the results indicate that there is a certain degree of metal pollution in the agricultural soil of Yinchuan, which is significantly influenced by human activities. Full article

(This article belongs to the Section Soil and Plant Nutrition)

18 pages, 2169 KB

Open AccessArticle

Overexpression of CmDUF239-1 Enhances Cold Tolerance in Melon Seedlings by Reinforcing Antioxidant Defense and Activating the ICE-CBF-COR Pathway

by Yang Li, Zhanming Tan, Yanjun Liu, Xiaoye Wu, Jin Zhu and Yuquan Peng

Agronomy 2025, 15(12), 2725; https://doi.org/10.3390/agronomy15122725 - 26 Nov 2025

Abstract

Low-temperature stress is a major factor that limits the productivity and geographical distribution of melon (Cucumis melo L.). This study elucidates that CmDUF239-1 is a positive regulator of cold stress, and its underlying mechanisms are investigated using root-specific overexpression lines. Seedlings overexpressing [...] Read more.

Low-temperature stress is a major factor that limits the productivity and geographical distribution of melon (Cucumis melo L.). This study elucidates that CmDUF239-1 is a positive regulator of cold stress, and its underlying mechanisms are investigated using root-specific overexpression lines. Seedlings overexpressing CmDUF239-1 exhibited increased biomass and reduced relative electrical conductivity under cold stress. CmDUF239-1 overexpression promoted the accumulation of soluble sugar and proline, which was accompanied by enhanced activity of the proline biosynthetic enzyme Δ¹-pyrroline-5-carboxylate synthase (P5CS) and suppressed activity of the proline-degrading enzyme proline dehydrogenase (PDH). Molecular analysis revealed that CmDUF239-1 overexpression upregulated antioxidant enzyme-related genes, sugar metabolism related genes, and proline-related genes. Furthermore, it activated key genes in the ICE-CBF-COR signaling pathway, including CmCBF1, CmCBF2, and the downstream effector gene CmCOR413-2. In conclusion, the CmDUF239-1 gene enhances melon cold tolerance by modulating antioxidant defense, enhancing osmolyte (sugar and proline) metabolism and activating a core signaling pathway. This study not only characterizes a novel function for a DUF family gene but also provides a promising candidate gene for the genetic improvement of cold resilience in melon and other related crops. Full article

(This article belongs to the Special Issue Environmental and Genetic Regulation of Fruit Development and Quality)

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20 pages, 4154 KB

Open AccessArticle

MSA-ResNet: A Neural Network for Fine-Grained Instar Identification of Spodoptera frugiperda Larvae in Smart Agriculture

by Quanyuan Xu, Mingyang Wang, Ying Lu, Dan Feng, Hui Ye and Yonghe Li

Agronomy 2025, 15(12), 2724; https://doi.org/10.3390/agronomy15122724 - 26 Nov 2025

Abstract

The Spodoptera frugiperda (fall armyworm), a globally significant agricultural pest, poses severe threats to crop production. Accurate identification of larval instar stages is crucial for implementing precise control measures and reducing pesticide use. However, traditional identification methods suffer from low efficiency and heavy [...] Read more.

The Spodoptera frugiperda (fall armyworm), a globally significant agricultural pest, poses severe threats to crop production. Accurate identification of larval instar stages is crucial for implementing precise control measures and reducing pesticide use. However, traditional identification methods suffer from low efficiency and heavy reliance on expert knowledge, while existing deep learning models still face challenges such as insufficient feature extraction and high computational complexity in fine-grained instar classification. To address these issues, this study proposes a novel network model, termed Multi-Scale Improved Self-Attention ResNet (MSA-ResNet), which integrates large convolutional kernels (LCK), atrous spatial pyramid pooling (ASPP), and an improved self-attention (ISA) mechanism into the ResNet50 backbone. These enhancements enable the model to more effectively capture and discriminate subtle morphological details of larvae. Experiments conducted on a self-constructed dataset comprising 24,179 images across six instar stages demonstrate that MSA-ResNet achieves an accuracy of 96.81% on the test set, significantly outperforming mainstream models such as ResNet50, VGG16, and MobileNetV3. In particular, the precision for the first instar increased by 12.94%, while the recall rates for the second and fourth instars improved by 16% and 8.97%, respectively. Ablation studies further validate the effectiveness of each module and the optimal embedding strategy. This research presents a high-precision and efficient intelligent solution for larval instar identification of S. frugiperda, offering a transferable reference for fine-grained image recognition tasks in agricultural pest management. Full article

(This article belongs to the Section Pest and Disease Management)

17 pages, 5138 KB

Open AccessArticle

Effect of Low-Temperature-Fermented Straw Compost on Alfalfa (Medicago sativa L.) Growth and Stress Tolerance

by Ziyi Zhao, Ziguang Liu, Lingyun Chang, Wenchao Sun, Haoyu Cai, Yumei Li and Juan Wu

Agronomy 2025, 15(12), 2723; https://doi.org/10.3390/agronomy15122723 - 26 Nov 2025

Abstract

The physicochemical properties of soil have a significant impact on plant growth, and fertilizers that improve soil quality play a crucial role in promoting crop development. Abandoned crop straw, once composted and converted into organic fertilizer, is an important resource for agricultural production. [...] Read more.

The physicochemical properties of soil have a significant impact on plant growth, and fertilizers that improve soil quality play a crucial role in promoting crop development. Abandoned crop straw, once composted and converted into organic fertilizer, is an important resource for agricultural production. However, the mechanisms by which straw compost regulates plant growth remain incompletely understood. In previous work, we demonstrated that low-temperature-fermented straw compost significantly promoted the growth and yield of rice. To further investigate the effects of low-temperature-fermented straw compost on alfalfa growth, this study incorporated 10% and 30% straw compost into soil for alfalfa cultivation and systematically compared plant growth and soil quality indicators. Results showed that compost application increased alfalfa leaf length by 30.55%, with significant improvements observed in multiple physiological parameters. Furthermore, straw compost amendment raised soil pH to 6.88, substantially enhanced soil organic matter content, and increased the activities of sucrase and urease by approximately 181.77% and 223.81%. The abundance of soil microorganisms, including fungi and actinomycetes, increased by approximately 129.59% and 444.44%, respectively, indicating that straw compost effectively improves soil conditions and promotes alfalfa growth. Overall, the study demonstrates that low-temperature-fermented straw compost exerts a promoting effect on crop growth and provides an important theoretical basis for promoting low-temperature straw fermentation technology and the application of straw composting in agricultural and animal husbandry production. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

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16 pages, 5069 KB

Open AccessArticle

Genome-Wide Identification, Characterization, and Expression Profiles of the CCO Gene Family in Raphanus sativus L. Response to Fusarium oxysporum Resistance

by Yinbo Ma, Wei Zuo, Piaopiao Shi, Xinyi Cao, Yuchen Xia, Run Tan, Shuai Hao, Hanyan Sun and Wenxing Hu

Agronomy 2025, 15(12), 2722; https://doi.org/10.3390/agronomy15122722 - 26 Nov 2025

Abstract

Carotenoid cleavage oxygenases (CCOs) constitute a pivotal enzyme family that catalyze the oxidative cleavage of carotenoids to produce apocarotenoids, which are important signaling molecules involved in plant development, stress adaptation, and immunity responses. Notably, several CCOs, particularly NCED members, contribute to pathogen [...] Read more.

Carotenoid cleavage oxygenases (CCOs) constitute a pivotal enzyme family that catalyze the oxidative cleavage of carotenoids to produce apocarotenoids, which are important signaling molecules involved in plant development, stress adaptation, and immunity responses. Notably, several CCOs, particularly NCED members, contribute to pathogen resistance by mediating abscisic acid (ABA) biosynthesis and apocarotenoid-derived defense signaling. While this gene family has been characterized in diverse plant species, its composition and functions in radish (Raphanus sativus L.) remain poorly understood. This study presents the comprehensive, genome-wide identification of the CCO gene family in radish, revealing 19 putative RsCCO members. A combination of phylogenetic, gene structure, chromosomal localization, and synteny analyses revealed conserved evolutionary patterns and species-specific diversification of the RsCCO family within the Brassicaceae. Promoter analysis identified an abundance of cis-acting regulatory elements related to phytohormone signaling, light perception, and stress responses, suggesting complex transcriptional regulation. Crucially, transcriptomic profiling following infection with Fusarium oxysporum revealed that two genes, RsNCED6 and RsNCED9b, were significantly upregulated in a resistant radish inbred line (‘T5’) compared to a susceptible line (‘T24’), identifying them as key candidate genes involved in ABA-mediated defense responses. This work provides a foundational characterization of the radish CCO gene family, offering novel insights into its role in carotenoid metabolism and pathogen defense. Full article

(This article belongs to the Special Issue Phytopathogens and Crop Diseases)

20 pages, 8098 KB

Open AccessArticle

Quantitative Source Apportionment and Source-Oriented Health Risk of Heavy Metals in Soils: A Case Study of Yutian County in the Southern Margin of Tarim Basin, China

by Wei Fan, Jinlong Zhou, Jianghua Zheng, Songtao Wang, Jiangyan Du, Lina Hu, Ruiqi Shan and Lizhong Zhang

Agronomy 2025, 15(12), 2721; https://doi.org/10.3390/agronomy15122721 - 26 Nov 2025

Abstract

To explore the pollution sources and health risks of heavy metals in the soil of the southern margin of the Tarim Basin, 1231 soil samples were collected and analyzed for pH and eight heavy metals (Cr, Cd, Pb, Zn, Cu, Ni, As, and [...] Read more.

To explore the pollution sources and health risks of heavy metals in the soil of the southern margin of the Tarim Basin, 1231 soil samples were collected and analyzed for pH and eight heavy metals (Cr, Cd, Pb, Zn, Cu, Ni, As, and Hg). The self-organizing map (SOM) and positive matrix factorization (PMF) models were used to analyze the sources of heavy metals in the soil of the southern Tarim Basin, and a Monte Carlo method-based health risk assessment model was used to quantify the human health risks of different sources of pollution. The results showed that the average contents of all elements did not exceed the local soil background values, except Cd and Hg. The content of As in 0.24% samples was higher than the national risk screening value of China, and the content of the other heavy metals was lower than the Chinese national risk screening value. The main sources of heavy metal pollution were natural–traffic–agricultural mixed sources (60.9%), atmospheric dust fall sources (18.4%), and agricultural sources (20.7%). Soil As, Cr, Pb, Zn, Cu, and Ni were mainly influenced by natural–traffic–agricultural mixed sources. Hg was influenced by atmospheric dust fall (55%) and agricultural sources (45%). Cd was mainly influenced by natural–traffic–agricultural mixed sources (61.6%) and agricultural sources (37.8%). The levels of heavy metals in the soil in Yutian County did not pose a non-carcinogenic risk to humans, but they pose an alert carcinogenic risk to children and adults. Cr is identified as the priority pollutant for human health risk control, while the mixed sources from natural, traffic, and agricultural activities are recognized as the primary targets for pollution control. This study provides a reference for the precise prevention and control of soil heavy metal pollution in the southern margin of the Tarim Basin. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

15 pages, 4103 KB

Open AccessArticle

Study on Preparation and Performance of Biomass–Polyurethane Light Planting Substrate

by Zhiyu Ma, Jinqiu Song, Xuan Chu, Hongli Liu, Yinghui Mu, Song Gu, Hongyu Wei and Xingping Chen

Agronomy 2025, 15(12), 2720; https://doi.org/10.3390/agronomy15122720 - 26 Nov 2025

Abstract

A biodegradable, lightweight substrate for facility-based stereoscopic planting was developed via a one-step polyurethane foaming process. The substrate was synthesized by incorporating a biomass mixture of bamboo charcoal and cassava flour into a polyurethane foam matrix. This study investigated the effects of varying [...] Read more.

A biodegradable, lightweight substrate for facility-based stereoscopic planting was developed via a one-step polyurethane foaming process. The substrate was synthesized by incorporating a biomass mixture of bamboo charcoal and cassava flour into a polyurethane foam matrix. This study investigated the effects of varying the content ratios of polyether polyol, isocyanate, bamboo charcoal powder, and cassava flour on the structural and functional properties of the composite foam. Results indicated that the biomass blend significantly influenced the foam’s physicochemical properties, water retention capacity, hardness, and elasticity. Specifically, bamboo charcoal powder enhanced the porosity and degradation rate of the foam, whereas the swelling of cassava flour upon water absorption improved the matrix’s resilience and cohesion. A polyether polyol/isocyanate ratio of 4:1 yielded a substrate with superior physicochemical properties, water retention capacity, germination rate, seedling index, and plant dry weight. Subsequently, the optimal overall performance was achieved at a biomass/polyol–isocyanate ratio of 1:3. This optimal formulation exhibited a degradation rate of 6.24 ± 0.94%, porosity of 66.07 ± 1.10%, and water retention capacity of 86.03 ± 1.59%. Consequently, it also produced the highest seed germination rate (84 ± 5.16%), seedling index (12.49 ± 1.94), and mature plant dry weight (4.00 ± 0.51 g). Microscopic analysis confirmed that the biomass addition refined the substrate’s pore structure, leading to greater uniformity and stability of the internal pores. This enhancement reduced the foam’s susceptibility to collapse and improved its elasticity and cohesion, thereby making it more amenable to mechanized handling and planting operations. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

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28 pages, 3279 KB

Open AccessArticle

Early Stress Resilience in Turfgrass: Comparative Germination and Seedling Responses of Lolium perenne L. and Poa pratensis L. Under Osmotic and Salt Stress

by Ligia Craciun, Rodolfo J. Bacharach Sánchez, Diana M. Mircea, Adrián Sapiña-Solano, Radu E. Sestras, Monica Boscaiu, Adriana F. Sestras and Oscar Vicente

Agronomy 2025, 15(12), 2719; https://doi.org/10.3390/agronomy15122719 - 26 Nov 2025

Abstract

Seed germination and early seedling development represent critical stages for turfgrass establishment under increasingly frequent drought and salinity constraints. This study evaluated the germination performance of three cultivars of Lolium perenne L. and three cultivars of Poa pratensis L. exposed to iso-osmotic drought [...] Read more.

Seed germination and early seedling development represent critical stages for turfgrass establishment under increasingly frequent drought and salinity constraints. This study evaluated the germination performance of three cultivars of Lolium perenne L. and three cultivars of Poa pratensis L. exposed to iso-osmotic drought stress simulated with polyethylene glycol (PEG) and salt stress induced by NaCl. Germination percentage, mean germination time, germination index, seedling vigor index, and radicle and plumule elongation were quantified, and post-stress recovery tests assessed the reversibility of stress effects. Osmotic restriction imposed by PEG caused stronger inhibition of germination and seedling growth than NaCl at equivalent water potentials. L. perenne showed higher overall tolerance, maintaining faster emergence and greater seedling vigor across treatments, while P. pratensis was more sensitive but exhibited substantial germination recovery after stress removal. Cultivar-dependent variation was evident in both species, and multivariate analyses consistently differentiated tolerant and sensitive genotypes. The contrasting germination strategies, with rapid activation in L. perenne and delayed, recovery-oriented germination in P. pratensis, highlight species-specific adaptive responses to water and salt stress. These findings provide a physiological basis for selecting resilient turfgrass cultivars suited to drought- and salinity-prone environments, contributing to sustainable turfgrass establishment and management. Full article

(This article belongs to the Special Issue Innovations in Turfgrass Management for Enhanced Sustainability and Conservation)

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22 pages, 9456 KB

Open AccessArticle

A Multi-Feature Estimation Model for Olive Canopy Chlorophyll Combining XGBoost with UAV Imagery

by Weiyu Zhuang, Dong Li, Weili Kou, Ning Lu, Fan Wu, Shixian Sun and Zhefeng Liu

Agronomy 2025, 15(12), 2718; https://doi.org/10.3390/agronomy15122718 - 26 Nov 2025

Abstract

Olive (Olea europaea L.) is an important woody oil crop worldwide, and accurate estimation of leaf chlorophyll content is critical for assessing nutritional status, photosynthetic capacity, and precision crop management. Unmanned aerial vehicle (UAV) remote sensing, with high spatiotemporal resolution, has increasingly [...] Read more.

Olive (Olea europaea L.) is an important woody oil crop worldwide, and accurate estimation of leaf chlorophyll content is critical for assessing nutritional status, photosynthetic capacity, and precision crop management. Unmanned aerial vehicle (UAV) remote sensing, with high spatiotemporal resolution, has increasingly been applied in crop growth monitoring. However, the small, thick, waxy leaves of olive, together with its complex canopy structure and dense arrangement, may reduce estimation accuracy. To identify sensitive features related to olive leaf chlorophyll and to evaluate the feasibility of UAV-based estimation methods for olive trees with complex canopy structures, UAV multispectral orthophotos were acquired, and leaf chlorophyll was measured using a SPAD (Soil Plant Analysis Development) meter to provide ground-truth data. A dataset including single-band reflectance, vegetation indices, and texture features was built, and sensitive variables were identified by Pearson correlation. Modeling was performed with linear regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Partial Least Squares Regression (PLSR), and Support Vector Machine (SVM). Results showed that two spectral bands (green and red), one vegetation index (TCARI/OSAVI), and twelve texture features correlated strongly with SPAD values. Among the machine learning models, XGBoost achieved the highest accuracy, demonstrating the effectiveness of integrating multi-feature UAV data for complex olive canopies. This study demonstrates that combining reflectance, vegetation indices, and texture features within the XGBoost model enables reliable chlorophyll estimation for olive canopies, highlighting the potential of UAV-based multispectral approaches for precision monitoring and providing a foundation for applications in other woody crops with complex canopy structures. Full article

(This article belongs to the Special Issue Precision Monitoring of Crops and Pastures Using UAV, Satellite, and Sensor Technologies)

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20 pages, 3615 KB

Open AccessArticle

Heavy Metal Pollution and Health Risk Assessment in Black Soil Region of Inner Mongolia Province, China

by Lin Xu, Zijie Gao, Jie Jiang and Guoxin Sun

Agronomy 2025, 15(12), 2717; https://doi.org/10.3390/agronomy15122717 - 25 Nov 2025

Abstract

In order to investigate the current status of soil heavy metal pollution, ecological risk, and risk sources in the black soil area of the Eastern Inner Mongolia Province, topsoil (0–20 cm) samples from farmland in the black soil area (N = 163) were [...] Read more.

In order to investigate the current status of soil heavy metal pollution, ecological risk, and risk sources in the black soil area of the Eastern Inner Mongolia Province, topsoil (0–20 cm) samples from farmland in the black soil area (N = 163) were collected to determine the contents of seven heavy metals. The levels of soil heavy metal pollution and ecological risk in the study area were evaluated by combining the geo-accumulation index, potential ecological risk index, and static environmental carrying capacity; the positive matrix factorization (PMF) model was used to identify the pollution sources and contributions of heavy metals in the soil and analyze the risk levels to adults and children. The soil was predominantly weakly acidic, with mean values of Cr, Ni, Cu, As, Cd, Pb, and Zn of 61.77, 26.77, 17.07, 12.11, 0.08, 12.61, and 85.71 mg·kg⁻¹. The mean concentrations of heavy metals exceeded the background values, except for Pb, the mean concentration of which was lower than the soil background. Ni concentrations of 6.21% at the sampling sites exceeded the risk screening value for agricultural soils. The geo-accumulation index showed that Cr (55.15%) and As (54.00%) were mainly mild pollutants; the static environmental carrying capacity indicated that the soils were slightly polluted by Ni, As, and Zn; and the potential ecological risk indices of Cd, Ni, and As were at moderate levels. The PMF model analyzed three pollution sources: mixed agricultural practice–transportation sources (39.46%), mineral-related activity sources (27.01%), and pesticide–fertilizer agricultural practices (33.53%). The human health risk assessment indicated that 46.58% of sampling sites posed a carcinogenic risk to children, with Ni as the main carcinogenic element. In conclusion, the potential contamination of As, Cd, Ni, Cr, and Zn in the Eastern Inner Mongolia farmland black soil area should be further studied. Full article

(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)

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13 pages, 868 KB

Open AccessArticle

Partial Replacement of Chemical Fertilizer by Biochar-Based Fertilizer Increases Rice Yield and Soil Quality

by Chao Ding, Xikun Luo, Yuhui Wang, Weihua Long, Yongxiang Guan, Qiong Hou, Cansheng Yuan and Lin Wang

Agronomy 2025, 15(12), 2716; https://doi.org/10.3390/agronomy15122716 - 25 Nov 2025

Abstract

Substituting chemical fertilizers with organic fertilizers is a significant agricultural practice that can enhance crop yield while influencing soil activity. To investigate the effects of biochar-based organic fertilizer on rice yield, quality, and soil physicochemical properties and activity, this study conducted a field [...] Read more.

Substituting chemical fertilizers with organic fertilizers is a significant agricultural practice that can enhance crop yield while influencing soil activity. To investigate the effects of biochar-based organic fertilizer on rice yield, quality, and soil physicochemical properties and activity, this study conducted a field experiment with three treatments: chemical fertilizer only (CK), 30% of chemical nitrogen substituted with conventional organic fertilizer (CF), and 30% of chemical nitrogen substituted with biochar-based organic fertilizer (BF). Compared with chemical fertilizer alone (CK), both CF and BF treatments significantly increased rice yield by 8.9% and 14.2%, respectively, with BF showing a further increase over CF, primarily attributed to an 18.7% increase in panicle number. Both organic fertilizer treatments significantly improved grain quality, reducing amylose content by 4.6% and 13.1%, and increasing taste value by 3.3% and 3.6%, respectively. Dry matter accumulation throughout the growth period was significantly enhanced, with BF increasing total dry weight by 11.2% at maturity compared to CK. Root morphology was markedly improved, with BF increasing root volume by 146.1% at the grain-filling stage. Soil nutrient content was significantly elevated, showing maximum increases under BF of 118.9% for alkali-hydrolyzable nitrogen, 51.7% for ammonium nitrogen, 30.6% for available phosphorus, and 177.6% for available potassium. Soil enzyme activity analysis revealed significant enhancements in urease, acid phosphatase, and sucrase activities, with maximum increases of 91.5%, 105.6%, and 104.2%, respectively, under BF. These findings demonstrate that organic fertilizers, particularly biochar-based organic fertilizer, can synergistically enhance rice yield and quality by promoting root growth, strengthening soil microbial activity and enzymatic reactions, and optimizing nutrient supply. Biochar-based organic fertilizer exhibits significant advantages in improving soil biological fertility and maintaining stable nutrient supply during the late growth stages of rice. Full article

(This article belongs to the Section Soil and Plant Nutrition)

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29 pages, 5027 KB

Open AccessArticle

Flower Thinning Strategy of Flat Peach Inflorescence Based on RBCN-YOLO

by Yongchuang Xiong, Benxue Ma, Yanxing Chen, Ying Xu and Jincheng Chen

Agronomy 2025, 15(12), 2715; https://doi.org/10.3390/agronomy15122715 - 25 Nov 2025

Abstract

Accurately identifying the morphology and spatial distribution of flat peach inflorescence is crucial for guiding precise flower thinning operations. In this study, based on the YOLOv8 framework, a flat peach inflorescence detection model (RBCN-YOLO) was developed for detecting all growth stages, from bud [...] Read more.

Accurately identifying the morphology and spatial distribution of flat peach inflorescence is crucial for guiding precise flower thinning operations. In this study, based on the YOLOv8 framework, a flat peach inflorescence detection model (RBCN-YOLO) was developed for detecting all growth stages, from bud to initial flowering and full flowering. The model optimized the neck network architecture by incorporating RepBlock and BiFusion modules, integrating the CAFM module into the backbone network, and combining the NWD loss function with the CIoU loss function. The improved model showed better detection performance in remote viewing angles, backlight conditions, and complex scenarios. Moreover, it demonstrated good real-time performance on edge devices. Based on this model, a flower thinning strategy was designed by combining the density classification algorithm, inflorescence membership categorization, and interval flower-thinning requirements. The results showed that the RBCN-YOLO model achieved a mAP@0.5 of 82.9% and an F1 score of 78.9%. These scores represented improvements of 3.0% and 2.4%, respectively, compared to YOLOv8. Notably, the model performance in the initial flowering stage showed the most significant improvement, with the mAP@0.5 increasing from 65.1% to 70.7%. Additionally, the flower thinning strategy based on RBCN-YOLO achieved a flower thinning ratio of 54.55%, with a thinning accuracy of 78.84%. To further enhance the application of the research, a visualization system with integrated object detection and flower thinning functions was designed. This study provides a valuable reference for flower-thinning operations in flat peach orchards. Full article

(This article belongs to the Special Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables)

19 pages, 4034 KB

Open AccessArticle

Research on Spatiotemporal Combination Optimization of Remote Sensing Mapping of Farmland Soil Organic Matter Considering Annual Variability

by Wenzhu Dou, Wenqi Zhang, Shiyu He, Xue Li and Chong Luo

Agronomy 2025, 15(12), 2714; https://doi.org/10.3390/agronomy15122714 - 25 Nov 2025

Abstract

Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and [...] Read more.

Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and machine learning. Youyi Farm in the Sanjiang Plain, Heilongjiang Province, was selected as the study area, covering three representative years: 2019 (flood), 2020 (normal), and 2021 (drought). Based on multi-temporal Sentinel-2 imagery and environmental covariates, Random Forest models were used to evaluate single- and dual-period combinations. Results showed that combining bare-soil and crop-season images consistently improved accuracy, with optimal combinations varying by year (R² = 0.544–0.609). Incorporating temperature, precipitation, and elevation enhanced model performance, particularly temperature, which contributed most to prediction accuracy. Feature selection further improved model stability and generalization. Spatially, SOM showed a pattern of higher values in the northeast and lower in the central region, shaped by topography and cultivation. This study innovatively integrates interannual climatic variability with remote sensing temporal combination and feature selection, constructing a climate-adaptive SOM mapping framework and providing new insights for accurate inversion of cropland SOM under extreme climates, highlights the importance of multi-temporal imagery, environmental factors, and feature selection for robust SOM mapping under different climatic conditions, providing technical support for long-term cropland quality monitoring. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Agronomic Application for Mapping and Modeling Soil Properties)

13 pages, 2064 KB

Open AccessArticle

Comparative Study on Photosynthetic Characteristics and Leaf Structure of Paphiopedilum parishii in Different Growth Periods

by Li Lu, Haiying Jiang, Xinru Cai, Xi Li, Guohua He, Shuo Feng, Xiao Wei and Jianmin Tang

Agronomy 2025, 15(12), 2713; https://doi.org/10.3390/agronomy15122713 - 25 Nov 2025

Abstract

This study investigates the differences in photosynthetic characteristics of Paphiopedilum parishii (Rchb.f.) Stein during its reproductive and nutrient growth periods. Using plants from the same individual, we compared light response curves, chlorophyll content, leaf epidermal structure, and leaf anatomical structure between these two [...] Read more.

This study investigates the differences in photosynthetic characteristics of Paphiopedilum parishii (Rchb.f.) Stein during its reproductive and nutrient growth periods. Using plants from the same individual, we compared light response curves, chlorophyll content, leaf epidermal structure, and leaf anatomical structure between these two growth stages. The results show the following: (1) The overall shape of the light response curves was similar across both periods, but plants in the nutrient growth period exhibited higher net photosynthetic rates (P_n) at all light intensities compared to those in the reproductive growth period. (2) During the nutrient growth period, apparent quantum efficiency (AQY), maximum net photosynthetic rate (P_max), and light saturation point (LSP) were all significantly higher than in the reproductive growth period, while the light compensation point (LCP) and dark respiration rate (Rd) showed no significant differences. (3) Structurally, during the nutrient growth period, stomatal density significantly increased, while stomatal area decreased. Additionally, leaf thickness and mesophyll tissue thickness both markedly increased, indicating enhanced carbon assimilation efficiency through improved CO₂ uptake capacity and expanded photosynthetic area. (4) Significant differences in leaf anatomical structure between the two periods were primarily observed in leaf thickness and mesophyll tissue thickness, providing more space for energy accumulation during the post-flowering recovery phase. This study systematically reveals the dynamic changes in photosynthetic physiology and structural characteristics of P. parishii across different phenological stages, offering a theoretical foundation for its reintroduction and cultivation management. Full article

(This article belongs to the Section Plant-Crop Biology and Biochemistry)

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21 pages, 13199 KB

Open AccessArticle

Development of an Optimization Method for Dry-Type Rice Straw Modeling Considering Mechanical Properties Using the Discrete Element Method

by Min-Seung Kim, Dae-Wi Jeong, Se-O Choi, Shin-Young Noh, Yeon-Soo Kim and Yong-Joo Kim

Agronomy 2025, 15(12), 2712; https://doi.org/10.3390/agronomy15122712 - 25 Nov 2025

Abstract

Accurate load prediction is essential for optimizing the performance and design of agricultural machinery. However, obtaining field-based load data is challenging due to the limited harvesting period of crops. To address this, the Discrete Element Method (DEM) has been widely applied to simulate [...] Read more.

Accurate load prediction is essential for optimizing the performance and design of agricultural machinery. However, obtaining field-based load data is challenging due to the limited harvesting period of crops. To address this, the Discrete Element Method (DEM) has been widely applied to simulate crop–machine interactions under controlled virtual conditions. Previous DEM studies on rice straw often assumed uniform mechanical properties throughout the stem, neglecting sectional heterogeneity and limiting the accuracy of tensile and shear response prediction. This study developed an optimized DEM-based modeling approach by dividing rice straw into four sections—Top, Mid, Node, and Bottom—and experimentally determining their mechanical properties for model calibration. The Mid section exhibited the highest average tensile strength (178.71 N), while the Node showed the greatest shear resistance (114.08 N). One-way ANOVA confirmed significant sectional differences in both tensile (F = 18.12, p < 0.001) and shear (F = 23.61, p < 0.001) strengths. Two DEM models were validated: a multi-particle (Model A) and a simplified single-particle (Model B) configuration. Both achieved over 95% prediction accuracy, with Model B reducing computation time by 77.5% (80→18 min). Although the modeling was based on fully dried straw, future studies should incorporate moisture-dependent properties to enhance predictive fidelity. The proposed approach improves both accuracy and efficiency, providing a foundation for raking and baling load simulations. Full article

(This article belongs to the Special Issue Research Progress in Agricultural Robots in Arable Farming)

25 pages, 1080 KB

Open AccessArticle

Multifunctional Endophytic Fungi from Ginger (Zingiber officinale) with Antimicrobial, Enzymatic, and Antioxidant Potential

by Rogelio Borrego, Alejandro Bódalo, Inmaculada Izquierdo-Bueno, Javier Moraga, María Carbú, Hernando José Bolivar-Anillo, María Dolores Vela-Delgado, Jesús M. Cantoral, Carlos Garrido and Victoria E. González-Rodríguez

Agronomy 2025, 15(12), 2711; https://doi.org/10.3390/agronomy15122711 - 25 Nov 2025

Abstract

Endophytic fungi were isolated from ginger (Zingiber officinale) tubers and identified through molecular characterization of ITS and 28S rRNA regions. Nine species were obtained, belonging to the genera Aspergillus, Penicillium, Plectosphaerella, and Pseudogymnoascus. Several isolates, particularly Penicillium [...] Read more.

Endophytic fungi were isolated from ginger (Zingiber officinale) tubers and identified through molecular characterization of ITS and 28S rRNA regions. Nine species were obtained, belonging to the genera Aspergillus, Penicillium, Plectosphaerella, and Pseudogymnoascus. Several isolates, particularly Penicillium melinii, Aspergillus ustus, and Plectosphaerella cucumerina, exhibited strong antagonistic activity against Botrytis cinerea (up to 98.6% growth inhibition), while moderate effects were observed against Colletotrichum acutatum, Staphylococcus aureus and Klebsiella pneumoniae. All isolates produced at least one extracellular enzyme, with lipolytic and cellulolytic enzymes being the most frequently observed, and showed measurable antioxidant activity (EC₅₀ values ranging from 21.7 to 673.6 µg/mL). P. melinii and P. cucumerina demonstrated the highest radical scavenging capacities. These findings reveal the multifunctional potential of ginger-associated endophytic fungi as sustainable sources of bioactive compounds, with promising applications in biocontrol, food preservation, and industrial biotechnology. Full article

(This article belongs to the Special Issue Exploring the Diversity of Endophytic Microorganisms: From Microbial Ecology to Agronomic Applications—2nd Edition)

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5 pages, 163 KB

Open AccessEditorial

Qualitative and Quantitative Plant Screening Measurements for Yield and Quality Enhancement

by Antonela Markulj Kulundžić and Domagoj Šimić

Agronomy 2025, 15(12), 2710; https://doi.org/10.3390/agronomy15122710 - 25 Nov 2025

Abstract

Climate variability limits resources, and the growing demand for high-yielding, high-quality crops is increasingly challenging global agricultural production [...] Full article

(This article belongs to the Special Issue Qualitative and Quantitative Plant Screening Measurements for Yield and Quality Enhancement)

20 pages, 3620 KB

Open AccessArticle

Orchard Variable-Rate Sprayer Using LiDAR-Based Canopy Volume Measurement

by Chao Zhang, Qiujie Li, Pengcheng Yuan and Hongping Zhou

Agronomy 2025, 15(12), 2709; https://doi.org/10.3390/agronomy15122709 - 25 Nov 2025

Abstract

This study developed and evaluated a LiDAR-based variable-rate orchard sprayer to address the inefficiency of traditional constant-rate application. The system dynamically adjusts pesticide output in real-time using a canopy volume calculation model and an adaptive delayed-spray mechanism, synchronized with LiDAR scans and travel [...] Read more.

This study developed and evaluated a LiDAR-based variable-rate orchard sprayer to address the inefficiency of traditional constant-rate application. The system dynamically adjusts pesticide output in real-time using a canopy volume calculation model and an adaptive delayed-spray mechanism, synchronized with LiDAR scans and travel speed. Experimental results demonstrated effective performance: the canopy volume estimation achieved a low overall error of 2.84%, enabling precise spray decision-making. The dosage control system showed an average error of 8.78%, and the adaptive system responded within 160 ms, distinguishing target gaps as small as 75 mm. Deposition tests confirmed uniform coverage within the canopy and minimal drift. The system proves to be a practical solution for significantly reducing pesticide use, operational costs, and environmental impact, marking a substantial advancement in precision orchard management. Full article

(This article belongs to the Special Issue Advances in Precision Pesticide Spraying Technology and Equipment)

21 pages, 6311 KB

Open AccessArticle

A New Approach to Sensible Heat Flux via CFD-Surface Renewal Integration

by Yang Li, Yongguang Hu, Yongzong Lu, Yihui Fu and Jizhang Wang

Agronomy 2025, 15(12), 2708; https://doi.org/10.3390/agronomy15122708 - 25 Nov 2025

Abstract

This study integrates surface renewal theory (SR) with computational fluid dynamics (CFD) models to explore the estimation of sensible heat flux in tea plantations. SR describes the turbulent transport processes at the air–water interface and has been widely applied in sensible heat flux [...] Read more.

This study integrates surface renewal theory (SR) with computational fluid dynamics (CFD) models to explore the estimation of sensible heat flux in tea plantations. SR describes the turbulent transport processes at the air–water interface and has been widely applied in sensible heat flux estimation. However, its practical application faces challenges, such as determining the calibration coefficient (α) and the reliability of high-frequency temperature sensors (10 hz). This research addresses these issues by combining large eddy simulation (LES) models with CFD simulations to simulate high-frequency temperature variations in flat tea plantation fields. The results indicate that the LES model accurately simulates temperature fluctuations across different timescales (1 min, 30 min), with R² values ranging from 0.72 to 0.99, suggesting its suitability for precise sensible heat flux calculations. Furthermore, a new method for determining the calibration coefficient α using CFD simulations is proposed, which accounts for variations in atmospheric stability and terrain, thus improving the accuracy and applicability of SR in heterogeneous environments. The findings demonstrate that the CFD-based approach offers a cost-effective alternative to traditional eddy covariance systems, simplifying field measurements and enhancing the precision of sensible heat flux calculations under various atmospheric conditions (sunny, cloudy, overcast, nighttime), thereby broadening the potential applications of surface renewal theory in crop water requirement research. Full article

(This article belongs to the Special Issue Smart Strategies and Technologies for Sustainability and Biodiversity in Herbaceous and Horticultural Crops—3rd Edition)

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