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Trends in Global Soil Research and a Microbiome-Based Framework for Soil Health Assessment -
Crop Yield Responses to Reduced Solar Radiation in Agrivoltaic Systems: Crop-Specific Patterns and Shading Thresholds -
Evaluating Photochemical Efficiency and Recovery Potential in Wheat Varieties with Divergent Drought Tolerance -
The Effect of the Freeze–Thaw Process on Plant Available Water and Water-Stable Aggregates as a Function of Soil Tillage and Soil Chemical Quality
Journal Description
Agronomy
Agronomy
is an international, peer-reviewed, open access journal on agronomy and agroecology published semimonthly online by MDPI. The Spanish Society of Plant Biology (SEBP) is affiliated with Agronomy and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), GEOBASE, PubAg, AGRIS, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Agronomy and Crop Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the first half of 2026).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agronomy include: Seeds, Agrochemicals, Grasses and Crops.
- Journal Cluster of Agricultural Science: Agriculture, Agronomy, Horticulturae, Soil Systems, AgriEngineering, Crops, Seeds, Grasses, Agrochemicals and AI and Precision Agriculture.
Impact Factor:
4.1 (2025);
5-Year Impact Factor:
4.4 (2025)
Latest Articles
Study on the Mechanism of Urea Arch Breaking in Vibrating Fertiliser Dischargers Based on EDEM
Agronomy 2026, 16(17), 1728; https://doi.org/10.3390/agronomy16171728 - 4 Sep 2026
Abstract
Urea discharge can become unstable as interparticle cohesion increases under moisture-affected conditions. This study combined bulk-solid mechanics, discrete element method (DEM) simulations, contact parameter calibration, and bench testing to investigate urea arching and vibration-assisted discharge. Dry-contact parameters for urea particles and a polypropylene
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Urea discharge can become unstable as interparticle cohesion increases under moisture-affected conditions. This study combined bulk-solid mechanics, discrete element method (DEM) simulations, contact parameter calibration, and bench testing to investigate urea arching and vibration-assisted discharge. Dry-contact parameters for urea particles and a polypropylene (PP) hopper were calibrated using angle-of-repose and sliding tests. The calibrated simulations differed from the physical target values by 1.71% for the angle of repose and 3.98% for the sliding friction angle. In a separate DEM sensitivity analysis, JKR surface energy was prescribed at 0, 0.05, 0.15, and 0.30 J·m−2 as an effective adhesion parameter rather than as a calibrated moisture state. The maximum EDEM-exported Total Force signal increased from 2.283 N at 0 J·m−2 to 2.704 N at 0.30 J·m−2 (18.5%), whereas the mean particle velocity during the common 5–18 s pre-discharge interval decreased from 0.0613 to 0.0397 m·s−1 (35.3%). Two combined excitation settings were evaluated: 29.17 Hz/0.2 mm and 58.33 Hz/1.2 mm. Because frequency and amplitude changed simultaneously, their individual effects could not be isolated. The bench tests yielded mean discharged masses of 566.808, 492.435, and 464.305 g for the 58.33 Hz/1.2 mm, 29.17 Hz/0.2 mm, and non-vibrating conditions, respectively. The 58.33 Hz/1.2 mm setting increased the mean mass discharged during the 30 s collection interval by approximately 22.1% relative to the non-vibrating control. The corresponding between-run coefficients of variation were 3.628%, 3.580%, and 4.303%; these values describe repeatability between replicate runs rather than temporal or spatial discharge uniformity. Overall, increasing prescribed adhesion reduced particle mobility in the DEM simulations, whereas the 58.33 Hz/1.2 mm combined excitation increased discharged mass under the tested conditions. The experiments do not directly demonstrate crystal bridge rupture or isolate an independent frequency effect.
Full article
(This article belongs to the Special Issue Smart Agricultural Equipment and Automation for Crop Production)
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Open AccessArticle
Protective Effect of Exogenous Quercetin Against Salt Stress in Triticum aestivum and Triticum durum
by
Neonila V. Kononenko, Elena M. Lazareva and Larisa I. Fedoreyeva
Agronomy 2026, 16(17), 1727; https://doi.org/10.3390/agronomy16171727 - 4 Sep 2026
Abstract
Various stress factors lead to increased reactive oxygen species (ROS) formation and increased damage to various plant tissues. Data obtained using fluorescence microscopy show that under abiotic stress, the most intense ROS staining is observed in the epidermal and cortical cells of the
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Various stress factors lead to increased reactive oxygen species (ROS) formation and increased damage to various plant tissues. Data obtained using fluorescence microscopy show that under abiotic stress, the most intense ROS staining is observed in the epidermal and cortical cells of the root cap and division zones. Increased ROS formation under abiotic stress activates the antioxidant system in wheat. The expression level of the MnSOD and Cu/ZnSOD genes in the Orenburgskaya 22 wheat variety (Triticum aestivum) is more than twice that in the Zolotaya variety (Triticum durum). An amount of 150 mM NaCl activates MnSOD and Cu/ZnSOD gene expression in the Orenburgskaya 22 wheat variety, increases glutathione (GSH) content, and activates GSH-associated enzymes. Therefore, different wheat genotypes have different mechanisms for neutralizing ROS. The antioxidant quercetin reduces ROS formation and also promotes antioxidant system activation in the Orenburgskaya 22 variety and has virtually no effect on the Zolotaya variety. However, it does reduce ROS before and after NaCl treatment in both wheat genotypes. Salt stress causes an increase in the number of small and large autophagosomes in root cells of Triticum durum wheat, while, in Triticum aestivum, large vacuoles not marked by ATG8 and small autophagosomes near the nucleus form in root cortex cells. Treatment of control plants with quercetin does not increase the number of autophagosomes. Treatment with quercetin after salt stress does not increase the number of ATG8-marked autophagosomes in cells of either genotype. Treatment with quercetin before salt exposure leads to an increase in the number of autophagosomes in durum wheat cells. PCR analysis of autophagy genes revealed features of the initiation of autophagosome formation. Thus, quercetin exerts a protective effect against salt stress. However, its use is limited to plants with high flavonoid content. Quercetin may have promising applications in agriculture.
Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
Open AccessArticle
Asymmetric Duct Design for Directional Airflow Delivery in UAV-Assisted Greenhouse Tomato Pollination
by
Yazhou Wei, Hanping Mao, Haitao Peng and Ikram Ullah
Agronomy 2026, 16(17), 1726; https://doi.org/10.3390/agronomy16171726 - 4 Sep 2026
Abstract
UAV-assisted greenhouse tomato pollination requires lateral airflow delivery toward flower clusters distributed along the crop canopy. To address this, an asymmetric duct was designed to passively redirect the rotor wake through geometric modification of three inner-wall curvature parameters (R1, R
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UAV-assisted greenhouse tomato pollination requires lateral airflow delivery toward flower clusters distributed along the crop canopy. To address this, an asymmetric duct was designed to passively redirect the rotor wake through geometric modification of three inner-wall curvature parameters (R1, R2, R3). Three-dimensional CFD simulations were conducted to evaluate the effects of these parameters on airflow redirection and aerodynamic performance. Compared with a conventional symmetric duct, the asymmetric duct shifted the high-velocity wake from a predominantly vertical direction toward the canopy side. Among the three parameters, R3 exerted the greatest influence: increasing R3 from 20 to 65 mm improved the lift-to-drag ratio from 24.7 to 184.2 but reduced the airflow velocity delivered to the pollination region from 6.48 to 2.82 m·s−1. The selected configuration (R1 = 11 mm, R2 = 20 mm, R3 = 25 mm) delivered an airflow velocity of 6.03 m·s−1 at an operating height of 1.44 m, with a lift of 7.04 N and a lift-to-drag ratio of 37.0. These results demonstrate that passive geometric asymmetry can redirect rotor-induced airflow toward the canopy side while balancing airflow delivery, operating height, and aerodynamic performance under greenhouse spatial constraints.
Full article
(This article belongs to the Section Precision and Digital Agriculture)
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Open AccessArticle
End–Edge–Cloud Collaborative Fast–Slow Semantic Planning for Agricultural Field Robots
by
Bishu Gao, Liang Gong, Yefeng Sun, Gengjie Lin, Jiayu Chen, Yifan Xu, Yanming Li and Chengliang Liu
Agronomy 2026, 16(17), 1725; https://doi.org/10.3390/agronomy16171725 - 4 Sep 2026
Abstract
Agricultural multi-robot systems in narrow and dynamic environments require global coordination, semantic event interpretation, and responsive trajectory execution. This study presents an end–edge–cloud fast–slow semantic planning framework. The cloud maintains a farm topology and generates fleet-level dispatch policies; the edge hosts an asynchronous
[...] Read more.
Agricultural multi-robot systems in narrow and dynamic environments require global coordination, semantic event interpretation, and responsive trajectory execution. This study presents an end–edge–cloud fast–slow semantic planning framework. The cloud maintains a farm topology and generates fleet-level dispatch policies; the edge hosts an asynchronous agentic vision–language planner and a fast trajectory planner; and the robot performs sensing, LiDAR odometry, low-level control, and execution. The fast planner reuses the latest valid semantic condition until an event-triggered update becomes available. The fast branch is pretrained on nuScenes and adapted using the training and validation subsets of a 3780-sample agricultural dataset comprising synchronized front- and rear-view images, robot states, motion histories, and future trajectories, with an independent 630-sample test set reserved for final evaluation. On an edge-side RTX 4080 SUPER, the complete planner achieves an average error of 0.67 m, a fast-step latency of 96.3 ms, and a throughput of 10.4 Hz. In the four-robot topology experiment, the framework achieves a 100.0% success rate under the representative single-blockage condition and maintains an 86.7% success rate under the dual-blockage condition. During an approximately 30 min operation at a nominal semantic update rate of 2 Hz, the cloud and robot communication round-trip times average 24.43 and 3.85 ms, respectively, with no robot deadline misses, while the mean trigger-to-updated-trajectory latency of the full event-driven pipeline is 2357.37 ms. These results demonstrate the feasibility of assigning global coordination to the cloud, semantic reasoning and trajectory inference to the edge, and sensing and execution to the robot.
Full article
(This article belongs to the Collection Advances of Agricultural Robotics in Sustainable Agriculture 4.0)
Open AccessArticle
Development of a Thermal-Time-Based Emergence Model for Echinochloa crus-galli
by
Hyun Hwa Park, Pyae Pyae Win and Yong In Kuk
Agronomy 2026, 16(17), 1724; https://doi.org/10.3390/agronomy16171724 - 4 Sep 2026
Abstract
Climate change-driven increases in temperature and changes in precipitation regimes are altering the timing and patterns of weed emergence in agricultural systems. Consequently, accurately predicting weed emergence and selecting the best timing for control are becoming more crucial over time. Weeds such as
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Climate change-driven increases in temperature and changes in precipitation regimes are altering the timing and patterns of weed emergence in agricultural systems. Consequently, accurately predicting weed emergence and selecting the best timing for control are becoming more crucial over time. Weeds such as Echinochloa crus-galli (barnyardgrass) are particularly problematic due to their widespread adaptability and competition across varying cultivation environments, making an accurate prediction of the timing of emergence crucial for management. The objective of this study was to characterize the emergence pattern of E. crus-galli under diverse environmental conditions and to develop and evaluate a Gompertz-based thermal-time model for predicting seedling emergence. Increased temperatures enhanced emergence rates and speeds in both growth chamber and greenhouse conditions. The effective accumulated temperature required for 50% emergence was relatively consistent (54–69 °C·d). Furthermore, high emergence percentages were maintained at soil moisture levels of 80% or greater. Across years, emergence responses differed substantially under field conditions. Independent validation using a field dataset collected in 2026 demonstrated that the model developed from the 2025 dataset successfully reproduced observed emergence patterns under field conditions (RMSE = 2.7%p, MAE = 2.3%p). Regional emergence analyses suggested a tendency toward earlier emergence under recent temperature conditions, particularly in warmer regions, although these predictions were based on only two years of field observations. Overall, the present study provides a preliminary evaluation of the applicability of a thermal-time-based approach for describing E. crus-galli emergence under Korean environmental conditions. Additional validation across multiple locations and growing seasons would further strengthen the general applicability of the model.
Full article
(This article belongs to the Section Weed Science and Weed Management)
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Open AccessReview
From Smart Irrigation to Nature-Positive Water Management in Australian Sugarcane: Irrigation Technologies, Nature Frameworks, and Integration Pathways
by
Eric Wang, Ana Almeida and Yvette Everingham
Agronomy 2026, 16(17), 1723; https://doi.org/10.3390/agronomy16171723 - 4 Sep 2026
Abstract
This narrative and conceptual review examines how irrigation management in Australian sugarcane can progress from practice-based efficiency improvement towards an integrated evidence system supporting productivity, water-quality improvement and nature-positive reporting. It reviews regional irrigation conditions, standard and best management practices, crop and water
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This narrative and conceptual review examines how irrigation management in Australian sugarcane can progress from practice-based efficiency improvement towards an integrated evidence system supporting productivity, water-quality improvement and nature-positive reporting. It reviews regional irrigation conditions, standard and best management practices, crop and water modelling, grower-facing scheduling, Internet of Things (IoT) monitoring and automation, remote sensing, forecast-informed irrigation and artificial intelligence. It then considers natural-capital, nature-positive and environmental-reporting frameworks relevant to reef-connected sugarcane landscapes. The review identifies a persistent gap between information generated by irrigation technologies and the credible, transparent and auditable indicators required for broader environmental reporting. A five-part integration pathway connects established components from farm monitoring and modelling, environmental-pressure estimation, supporting evidence, documented methods and defined reporting applications. Existing studies support individual components and several adjacent linkages; this review maps how they could operate as a whole system. Farm-scale technology outputs are treated as evidence of management actions or modelled pressure pathways rather than direct measurements of ecosystem condition. Smart irrigation is thereby positioned as an enabling component linking farm profitability, reduced pressure on soil and water assets, and more credible nature-positive water management in Australian sugarcane.
Full article
(This article belongs to the Special Issue Smart Irrigation and Nature-Based Solutions for Sustainable Water Management)
Open AccessArticle
Concurrent Elevation of CO2 and Temperature Stimulates N2O Emissions from Rice Paddies in a Rice–Wheat Cropping System
by
Jiujie Liu, Qin Yi, Yuchen Song, Xiumei Min, Taoyun Chen, Yuxin Ren, Haoyu Qian, Yunlong Liu, Yanfeng Ding and Yu Jiang
Agronomy 2026, 16(17), 1722; https://doi.org/10.3390/agronomy16171722 - 4 Sep 2026
Abstract
Paddy fields are a major agricultural hotspot for nitrous oxide (N2O), contributing approximately 11% of global agricultural emissions. While elevated CO2 and warming individually regulate N2O emissions by modulating soil carbon, nitrogen (N) availability and microbial activity, the
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Paddy fields are a major agricultural hotspot for nitrous oxide (N2O), contributing approximately 11% of global agricultural emissions. While elevated CO2 and warming individually regulate N2O emissions by modulating soil carbon, nitrogen (N) availability and microbial activity, the effects of concurrent elevated CO2 and temperature (ECT) and the underlying microbial mechanisms under field conditions remain poorly understood. Here, we used a free-air CO2 enrichment and temperature increase (T-FACE) system in a rice–wheat cropping system to investigate the impacts of ECT on N2O emissions from rice paddies and identify the underlying biogeochemical and microbial mechanisms. Results showed that ECT increased area-scaled and yield-scaled N2O emissions by 15.3% and 17.6%, respectively. Mechanistically, during the peak emission period, ECT significantly increased soil NH4+–N content by 40.2% and the denitrification gene ratio [(nirK + nirS)/nosZ] by 27.5%. Furthermore, ECT increased the diversity of nitrifying communities but decreased that of denitrifying communities, while reshaping the composition of ammonia-oxidizing archaea and denitrifiers, thereby altering nitrification and denitrification. Overall, our field-based evidence suggests that ECT can stimulate N2O emissions primarily by increasing soil N substrate availability and shifting denitrifier communities in ways that may favor N2O accumulation. These findings offer mechanistic insights into climate-driven N2O emissions.
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(This article belongs to the Topic Greenhouse Gas Emission Reductions and Carbon Sequestration in Agriculture)
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Open AccessSystematic Review
Effects of Summer Catch Cropping on Nitrogen Accumulation and Loss in Farmland with Meta-Analysis
by
Jing Liu, Zongqing Wei, Yu Ma, Rui Ma, Jingyu Qi, Zhizhuang An and Lianfeng Du
Agronomy 2026, 16(17), 1721; https://doi.org/10.3390/agronomy16171721 - 4 Sep 2026
Abstract
In vegetable production, excessive nitrogen fertilizer application leads to extremely low NUE and substantial reactive nitrogen losses. Given the abundant rainfall and the high residual soil nitrogen in soil after harvest, there is a pronounced risk of nitrate leaching during the summer fallow
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In vegetable production, excessive nitrogen fertilizer application leads to extremely low NUE and substantial reactive nitrogen losses. Given the abundant rainfall and the high residual soil nitrogen in soil after harvest, there is a pronounced risk of nitrate leaching during the summer fallow period. This study conducted a meta-analysis to systematically evaluate the effects of catch crops on nitrogen loss and soil nitrogen accumulation during the summer fallow season. The results showed that catch crops significantly reduced total N leaching loss by 58.10% through decreasing leachate volume by 24.54% and reducing leachate concentrations of TDN (43.36%), NO3−–N (34.47%), and NH4+–N (56.05%), respectively. Catch crops reduced soil NO3−–N storage by 36.28% and SIN storage by 40.61%, while increasing SON storage by 69.79% and MBN by 21.47%, thereby achieving a transformation from readily available mineral N to more stable organic N. Catch crops also enhanced ammonification, mineralization, and nitrification by 126%, 142%, and 42%, respectively, and altered the microbial community structure. The effectiveness of catch crops varied significantly among taxa: Poaceae exhibited the best overall performance, the genus Zea (especially sweet corn) showed the most comprehensive performance in integrated N control, while Fabaceae increased soil NH4+-N. Environmental factors, including soil depth, pH, organic matter, total nitrogen, initial NO3−–N, rainfall, and soil texture, collectively regulated the effectiveness of catch crops. In summary, a rational selection of catch crop species tailored to site-specific soil and environmental conditions can effectively reduce nitrogen leaching during the summer fallow period and support precision nitrogen management.
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(This article belongs to the Section Farming Sustainability)
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Open AccessArticle
Effects of Saline Water Irrigation on Soil Respiration and Carbon Balance in the Winter Wheat–Summer Maize Rotation System in the North China Plain
by
Xiaozheng Ju, Caiyun Cao, Yudong Zheng, Chunlian Zheng, Hongkai Dang, Zaffar Malik, Anqi Zhang and Junpeng Zhang
Agronomy 2026, 16(17), 1720; https://doi.org/10.3390/agronomy16171720 - 4 Sep 2026
Abstract
Saline water irrigation is a potential strategy to address agricultural water scarcity, but its effects on soil respiration and carbon balance are not well understood. To clarify these effects and promote the safe utilization of saline water resources, this study investigated five irrigation
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Saline water irrigation is a potential strategy to address agricultural water scarcity, but its effects on soil respiration and carbon balance are not well understood. To clarify these effects and promote the safe utilization of saline water resources, this study investigated five irrigation water salinity levels ECiw: 1.3, 3.4, 7.1, 10.6, and 14.1 dS·m−1 (i.e., 1, 2, 4, 6, 8 PSU; 1000, 2000, 4000, 6000, 8000 mg·L−1) in a winter wheat–summer maize rotation during 2024–2025. The results indicated that saline water irrigation caused salt accumulation during the wheat season, whereas salt leaching occurred during the maize season. When ECiw ≤ 3.4 dS·m−1, no notable decreases were observed in dry matter accumulation, water productivity, and carbon emission efficiency for both crops. In contrast, when ECiw > 3.4 dS·m−1, the crop yields and net carbon input of the crop rotation system were suppressed to a considerable extent. Furthermore, under saline water irrigation, the average soil respiration rate decreased by 4.1–25.2% during the wheat growing season, while that for maize decreased by 7.4–30.7%. Soil respiration in wheat was negatively correlated with soil salinity and pH, and positively correlated with soil moisture (p < 0.01). In maize, soil respiration was negatively correlated with salinity (p < 0.01), and positively correlated with soil moisture (p < 0.05) and temperature (p < 0.01). The entropy-weighted TOPSIS model identified 3.4 dS·m−1 as the appropriate irrigation salinity threshold for maintaining yield and carbon sink function in this rotation system.
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(This article belongs to the Section Water Use and Irrigation)
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Open AccessArticle
Spray Deposition and Coverage in Potato and Brussels Sprouts Using Drift-Reducing Spray Configurations
by
Ingrid Zwertvaegher, Tewodros Andargie Zewdie, Jan Vanwijnsberghe, Sarah Bossuyt, Benny De Cauwer, Pieter Verboven and David Nuyttens
Agronomy 2026, 16(17), 1719; https://doi.org/10.3390/agronomy16171719 - 4 Sep 2026
Abstract
In dense or structurally complex canopies, spray applications often fail to adequately reach the specific plant sites where pest organisms reside, typically the lower canopy and abaxial leaf surfaces. An additional challenge is balancing drift mitigation while providing adequate spray coverage and deposition.
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In dense or structurally complex canopies, spray applications often fail to adequately reach the specific plant sites where pest organisms reside, typically the lower canopy and abaxial leaf surfaces. An additional challenge is balancing drift mitigation while providing adequate spray coverage and deposition. This study investigates spray deposition and coverage in potato (Solanum tuberosum L.) and Brussels sprouts (Brassica oleracea var. gemmifera DC.) crops using drift-reducing spray configurations. Across two growing seasons (2023 and 2024), seven spray configurations (varying per crop) were evaluated during field trials at three growth stages (early, mid and late). The configurations combined different application techniques (standard boom, air support, air-injection system, Wingssprayer, reduced boom height, droplegs) with nozzle types (flat-fan nozzles with 0%, 75%, and 90% drift reduction, and an angled nozzle). Deposition and coverage were quantified using artificial collectors (filter paper collectors and water sensitive papers) and multiple mineral chelate tracer analysis using spectrometry. This method provides a comparative assessment under standardized measurement conditions rather than absolute deposition on leaf surfaces. Significant interactions between spray configuration and collector position were observed in both crops (p < 0.001), except for relative deposition in potato at the mid and late growth stage, indicating that spray performance depended highly on canopy location. However, no consistent trends across configurations were identified. No single configuration outperformed or, more importantly, underperformed the others across all collector positions. This suggests that drift-reducing configurations could potentially be adopted without substantially compromising spray deposition and coverage, at least as indicated by measurements obtained with artificial collectors.
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(This article belongs to the Section Pest and Disease Management)
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Open AccessArticle
Design and Experimental Study of an Automatic Seedling Picking and Feeding Device for a Strawberry Bare-Root Seedling Transplanter
by
Youheng Tan, Xinxin Chen, Jianping Hu, Wei Liu, Jinhao Zhou and Haoran Wu
Agronomy 2026, 16(17), 1718; https://doi.org/10.3390/agronomy16171718 - 4 Sep 2026
Abstract
The automated transplanting of bare-root strawberry seedlings faces challenges, including a high reliance on manual feeding and a lack of adaptable equipment. Moreover, existing picking mechanisms are typically limited to plug seedlings rather than multi-stem crops. To overcome these limitations, this study proposes
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The automated transplanting of bare-root strawberry seedlings faces challenges, including a high reliance on manual feeding and a lack of adaptable equipment. Moreover, existing picking mechanisms are typically limited to plug seedlings rather than multi-stem crops. To overcome these limitations, this study proposes an automatic seedling picking and feeding system synergizing an intermittent conveying device and a manipulator. First, the physical and mechanical properties of bare-root strawberry seedlings were measured. Through radial stem compression tests, a non-destructive clamping safety threshold bounded by a bio-yield point of 28 N was determined. Secondly, an intermittent conveying device based on variable-span V-shaped supports was designed. Furthermore, based on the clamping safety threshold and spatial kinematic modeling, a seedling picking end-effector was developed. This end-effector adopts a “gather first, clamp later” operation strategy to guarantee grasping accuracy and ensure reliability by preventing seedling detachment during transportation. Finally, a system test bench was built to conduct a three-factor, three-level orthogonal experiment to investigate the effects of seedling picking frequency, gripping position, and clamping gap on operation quality. At a seedling pick-up frequency of 20 plants/min, gripping position of 25 mm, and gripping gap of 5 mm, the system reached 98% success rate in picking and feeding, validating its stability and providing core equipment and theoretical support for fully automatic strawberry transplanters.
Full article
(This article belongs to the Special Issue Intelligent Farming Equipment: Optimization Design, Dynamics Control, and Machine Learning Applications)
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Open AccessArticle
Soil Micro-Food Web Composition and Complexity Shape Multifunctionality Across Post-Cropland Restoration States
by
Yuanze Li, Xueying Huo, Yunpeng Zhang, Junying Ge, Jingwen Pang, Zhiyi Zhao, Ganggang Zhang and Fei Yu
Agronomy 2026, 16(17), 1717; https://doi.org/10.3390/agronomy16171717 - 4 Sep 2026
Abstract
Belowground multitrophic communities and their potential associations are important biological foundations for the maintenance and recovery of soil ecosystem functions. However, it remains unclear how soil micro-food web composition and network complexity are linked to soil ecosystem multifunctionality across different post-cropland restoration states.
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Belowground multitrophic communities and their potential associations are important biological foundations for the maintenance and recovery of soil ecosystem functions. However, it remains unclear how soil micro-food web composition and network complexity are linked to soil ecosystem multifunctionality across different post-cropland restoration states. In this study, we used a space-for-time substitution approach and selected cropland and three post-cropland restoration states in the Taihang Mountains, including shrub–grassland, Populus spp. plantation, and Robinia pseudoacacia plantation, each with five plots, to examine changes in bacterial, fungal, and nematode communities, soil micro-food web network complexity, and soil ecosystem multifunctionality in topsoil and subsoil. Compared with cropland, bacterial diversity was higher in the shrub–grassland, Populus spp. and R. pseudoacacia plantations, whereas fungal diversity showed a marked decline in the Populus spp. plantation. Furthermore, the topsoil of the R. pseudoacacia plantation exhibited much higher micro-food web network complexity and soil multifunctionality than cropland, whereas differences among land-use types were relatively small in the subsoil. Structural equation modeling indicated that vegetation type, soil depth, soil moisture content, microbial diversity, nematode diversity, and soil micro-food web network complexity jointly explained 94% of the variation in soil ecosystem multifunctionality (R2 = 0.94). Further random forest analysis identified soil depth as the most important predictor of soil ecosystem multifunctionality, followed by soil micro-food web network complexity; among biological variables, fungal diversity, bacterial diversity, omnivorous-predatory nematode diversity, and herbivorous nematode diversity also showed relatively high importance. Overall, these results suggest that changes in soil ecosystem multifunctionality across post-cropland restoration states were strongly soil-depth-dependent, and that soil micro-food web diversity and network complexity can serve as important biological indicators for assessing restoration outcomes.
Full article
(This article belongs to the Special Issue Linking Soil Microbial Functional Diversity with Agroecosystem Sustainability)
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Open AccessArticle
A Two-Stage Weed Stem Localization Method Based on Crop Region Exclusion in Maize Seedling Fields
by
Yuqi Zhang, Xuehai Wang, Yanan Liu, Lili Fu and Yanlei Xu
Agronomy 2026, 16(17), 1716; https://doi.org/10.3390/agronomy16171716 - 4 Sep 2026
Abstract
Accurate weed stem localization is essential for site-specific weed control, including precision spraying, laser weeding, and other targeted weed-control operations. To address species diversity, morphology, and costly multiclass annotation in maize seedling fields, this study proposes a two-stage method based on crop-region exclusion.
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Accurate weed stem localization is essential for site-specific weed control, including precision spraying, laser weeding, and other targeted weed-control operations. To address species diversity, morphology, and costly multiclass annotation in maize seedling fields, this study proposes a two-stage method based on crop-region exclusion. First, MSDNet, a lightweight YOLOv8n-based maize detector integrating ShuffleNetV2, enhanced feature fusion, coordinate attention, and Wise-IoU loss, detects maize seedlings; pixels within the detected boxes are set to zero. Second, hue–saturation–value thresholding, morphological processing, and area filtering extract vegetation and suppress soil noise. Principal component analysis determines each weed contour’s principal axis, and the image-moment centroid is projected onto this axis to estimate the stem center. MSDNet achieved a mean average precision of 93.4% at an intersection-over-union threshold of 0.5, 8.7 percentage points above the baseline, while reducing parameters by 28.66%. Vegetation segmentation achieved a mean pixel accuracy of 97.6% and a mean intersection over union of 93.8%. Within a 15-pixel tolerance (9.50 mm), stem detection rate and localization precision reached 90.1% and 92.5%, respectively, with a mean localization error of 10.65 pixels (6.74 mm). The proposed method provides visual perception and target-localization support for site-specific weed control while reducing reliance on fine-grained multiclass annotation and species-specific models.
Full article
(This article belongs to the Special Issue Integrated Weed Management for Field Crops: Innovations, Integration, and Impact)
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Open AccessArticle
Data-Driven Framework Integrating Database Analysis and Bayesian Segmented Quantile Regression to Determine Critical Nutrient Levels in Soil and Leaves of Peach Trees
by
Jean M. Moura-Bueno, Débora L. Betemps, Lincon O. Stefanello, Gilmar A. B. Marodin, Simone P. Galarça, Corina Carranca and Gustavo Brunetto
Agronomy 2026, 16(17), 1715; https://doi.org/10.3390/agronomy16171715 - 4 Sep 2026
Abstract
Fertilization recommendations are frequently proposed by a limited number of calibration experiments conducted in a few regions, with few cultivars. Thus, recommendations concerning nutrients’ critical levels (CLs) and sufficiency ranges (SRs) are not always the most suitable ones. In addition, it is not
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Fertilization recommendations are frequently proposed by a limited number of calibration experiments conducted in a few regions, with few cultivars. Thus, recommendations concerning nutrients’ critical levels (CLs) and sufficiency ranges (SRs) are not always the most suitable ones. In addition, it is not known whether CL and SR differ when it comes to peach yield and quality variables, such as pulp firmness or total soluble solids (TSS) concentration. CL and SR can be estimated by using Bayesian segmented quantile regression (BSQR) in combination with databases. The aim is to propose nutrients’ CL and SR in soil and in leaves of peach trees grown under a subtropical climate, by combining databases and BSQR models. The dataset comprised 208 observations of Prunus persica cultivars ‘Maciel’ and ‘Chimarrita’, grown in southern Brazil. Models were developed through plateau regression based on BSQR models to measure the association between dependent variables (fruit yield and quality) and nutrient concentrations in the soil and leaves. CL and SR of N and K in leaves recorded values for variables related to fruit quality, pulp firmness, and TSS lower than values recorded for variables related to yield. We show for the first time that CL and SR in leaves for N and K related to yield are different between cultivars. A similar outcome was recorded for CL and SR of N in leaves between growing regions/sites presenting different soil types and climatic variables. CL and SR of N in leaves related to yield were higher in peach trees grown in sandy-soil sites with low organic matter content. This study presents critical advances for the Southern Brazilian peach market, but with implications for the approach presented in the present study (combination of database and BSQR modeling) relevant for application in orchards around the world.
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(This article belongs to the Special Issue Selected Papers from the 5th International Electronic Conference on Agronomy (IECAG 2025))
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Open AccessReview
Interactive Effects of Salinity and Land Use Changes on Depth-Dependent Soil Organic Carbon Fractions and Biological Activity
by
Habib Ramezanzadeh, Ahmad Bybordi, Hossein Beyrami, Ali Chenari Bouket, Sumit Kumar, Krzysztof Sztabkowski and Tomasz Oszako
Agronomy 2026, 16(17), 1714; https://doi.org/10.3390/agronomy16171714 - 4 Sep 2026
Abstract
Land-use change (LUC) and salinization interact synergistically to regulate depth-dependent fractionation and biological mediation of soil organic carbon (SOC) in vulnerable agroecosystems. Unlike previous syntheses addressing these drivers separately, the present review integrates them within a depth-resolved biological framework to reveal their combined
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Land-use change (LUC) and salinization interact synergistically to regulate depth-dependent fractionation and biological mediation of soil organic carbon (SOC) in vulnerable agroecosystems. Unlike previous syntheses addressing these drivers separately, the present review integrates them within a depth-resolved biological framework to reveal their combined effects on fraction-specific distribution under contrasting anthropogenic and ionic regimes. In the topsoil (0–30 cm), LUC and salinity synergistically collapse fungal networks, suppress carbon use efficiency, and restructure microbial communities to accelerate particulate organic matter (POM) turnover and impair mineral-associated organic matter (MAOM) formation. In the subsoil (>30 cm), salinity-driven clay dispersion and pore occlusion restrict oxygen diffusion and carbon accessibility, while LUC-induced loss of deep-rooting vegetation reduces carbon supply to mineral-associated pools. These depth-decoupled mechanisms render subsoil MAOM relatively resilient to direct ionic stress but highly vulnerable to land-use legacy, a distinction rarely represented in existing conceptual models. The evidence highlights key management implications, including restoring biological complexity in topsoil through reduced tillage, mycorrhizal re-establishment, and osmotic stress alleviation; conserving subsoil carbon by restoring deep-rooting vegetation and maintaining favorable ionic conditions for organo-mineral stabilization; and using depth-specific biomarkers, including enzymatic stoichiometry, fungal-to-bacterial ratios to detect SOC vulnerability before measurable losses occur. Future research should prioritize depth-explicit monitoring and integrated biological–physicochemical approaches to improve predictions of SOC dynamics. The resulting framework provides a mechanistic basis for depth-differentiated carbon management in salinizing landscapes.
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(This article belongs to the Section Farming Sustainability)
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Open AccessArticle
Identifying Controlling Climate Factors Conducive to Water and Nitrogen Export from an Agricultural Watershed During the Snowmelt Runoff Period Using the SWAT Model
by
Qiang Zhao, Dan Chang, Zhenyang Peng, Chong Li, Xinghua Wu, Jingwei Wu, Chenyao Guo, Chengeng Li, Qian Yao and Guoqing Lei
Agronomy 2026, 16(17), 1713; https://doi.org/10.3390/agronomy16171713 - 4 Sep 2026
Abstract
Temperature and precipitation variations during the freeze–thaw period affect snowmelt and accompanying nitrogen export in a complex manner. These influences can be long-lasting, superimposed, and strengthened. Daily discharge and nitrate-nitrogen NO3−-N concentrations were monitored during the snowmelt periods of 2015
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Temperature and precipitation variations during the freeze–thaw period affect snowmelt and accompanying nitrogen export in a complex manner. These influences can be long-lasting, superimposed, and strengthened. Daily discharge and nitrate-nitrogen NO3−-N concentrations were monitored during the snowmelt periods of 2015 and 2016 in an agricultural watershed in northeastern China. The SWAT model was used to simulate the water and NO3−-N export during the snowmelt period of 1951–2014 to identify the controlling climate factors and the combinations associated with enhanced snowmelt water and NO3−-N export. Our results show that the SWAT model performs well for Re values in simulating the daily snowmelt runoff and NO3−-N export, but poorly for NSE and R2 values in simulating NO3−-N export. This is attributed to the absence of snowmelt water refreezing and hysteresis modules. The number of days and precipitation of the stable freezing period and the starting day of the snowmelt period were the factors most strongly associated with daily snowmelt runoff, while daily NO3−-N export is mostly affected by precipitation during the snowmelt period. The combinations of climatic factors favored by snowmelt runoff and NO3−-N export were different. Years with longer stable freezing periods, later snowmelt period starting days, and higher rainfall during snowmelt more readily generated high snowmelt runoff. Correspondingly, under the present model configuration, later-appearing, higher-magnitude and more concentrated rainfall events, and higher temperatures between these rainfall and snowmelt events were linked to elevated NO3−-N export. Finally, this study is of great importance for the prevention of spring floods and water pollution during snowmelt periods.
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(This article belongs to the Section Farming Sustainability)
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Open AccessArticle
A Two-Step Hybrid Statistical and Machine-Learning Framework with Full Genetic Effects for Multi-Environment Genomic Prediction in Maize
by
Qi Wang, Xiaohe Liang, Jiayu Zhuang, Jiajia Liu and Ailian Zhou
Agronomy 2026, 16(17), 1712; https://doi.org/10.3390/agronomy16171712 - 3 Sep 2026
Abstract
Accurate genomic prediction across environments remains challenging because phenotypic variation is jointly influenced by environmental conditions, genetic effects, and genotype-by-environment interactions. We developed a two-step hybrid statistical and machine-learning framework for multi-environment genomic prediction in maize (Zea mays L.). A trait-specific
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Accurate genomic prediction across environments remains challenging because phenotypic variation is jointly influenced by environmental conditions, genetic effects, and genotype-by-environment interactions. We developed a two-step hybrid statistical and machine-learning framework for multi-environment genomic prediction in maize (Zea mays L.). A trait-specific mixed model was first used to statistically decompose phenotypic variation into adjusted environmental means and residuals, after which the environmental component was predicted from environmental metadata and covariates, while the residual component was modeled using genomic main effects (G), genotype-by-environment effects ( ), and pairwise epistatic effects ( ). The framework was evaluated for grain yield, pollen DAP, silk DAP, and anthesis–silking interval (ASI) using environment-grouped five-fold cross-validation and an independent 2022 temporal test. On the 2022 test set, the best two-step models increased global Pearson correlation coefficients from 0.578, 0.559, 0.573, and 0.277 to 0.652, 0.635, 0.644, and 0.362, respectively. An ablation using arithmetic environmental means showed that the two-step formulation itself improved ranking performance, while mixed-model adjustment provided additional gains. Five-fold cross-validation showed the strongest and most stable improvements for pollen DAP and silk DAP, with global PCC increasing by 62.3% and 71.9% and global RMSE decreasing by 33.5% and 37.8%, respectively. Adding produced only modest, trait-dependent gains, whereas provided no consistent benefit. Overall, the proposed statistical decomposition and component-wise modeling improved the use of environmental and genomic information, although the magnitude and source of predictive gains were strongly trait-dependent.
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(This article belongs to the Section Crop Breeding and Genetics)
Open AccessArticle
Construction of Synthetic Microbial Community for Straw Degradation Based on Multi-Omics Integration Technology and Elucidation of the Degradation Mechanism
by
Zhongnan Xu, Hui Yao, Yiqiang Li and Xiangwei You
Agronomy 2026, 16(17), 1711; https://doi.org/10.3390/agronomy16171711 - 3 Sep 2026
Abstract
The comprehensive utilization of lignocellulose is still constrained by the inefficiency of its degradation. The application of a synthetic microbial community represents a promising strategy to promote straw decomposition. By employing multi-omics coupling techniques to explore the microbial community and functional composition of
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The comprehensive utilization of lignocellulose is still constrained by the inefficiency of its degradation. The application of a synthetic microbial community represents a promising strategy to promote straw decomposition. By employing multi-omics coupling techniques to explore the microbial community and functional composition of various straw-associated environments, we identified core microbial taxa driving straw decomposition and subsequently constructed three microbial communities: a bacterial community, a fungal community, and a cross-kingdom community. In particular, the bacterial community exhibited intrinsic synergistic effects, increasing its straw weight loss by 10.8–57.3% compared to single-constituent strains. Liquid fermentation experiments further verified that the bacterial community possessed excellent straw degradation performance, achieving net degradation rates of 26.4% within 3 days and 33.7% within 30 days under 1% (w/v) tobacco straw conditions. The bacterial community exhibited cellulolytic and ligninolytic enzyme activities. Metagenomic analysis revealed that the bacterial community was dominated by Paenibacillus (42.3–70.9%), followed by Paenarthrobacter (7.1–31.7%) and Microbacterium (6.5–26.2%). CAZy annotation revealed that the bacterial community harbored numerous lignocellulose-degrading genes, including those encoding glycoside hydrolases (GHs, 51.2%), carbohydrate esterases (CEs, 17.7%), and auxiliary activities (AAs, 5.7%). Non-targeted metabolomics analysis identified 1024 differentially expressed metabolites that were involved in alanine, aspartate, and glutamate metabolism; butanoate metabolism; and cofactor biosynthesis. These results indicate that a community constructed with multi-omics coupling techniques can effectively degrade lignocellulose, which provides meaningful guidance for constructing synthetic microbial consortia aimed at lignocellulose decomposition.
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(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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Open AccessArticle
Linking Soil Health to Soybean (Glycine max L.) Productivity Under Biochar and Organic Fertilizer Application: Evidence from PCA and Correlation Analyses
by
Marianus Evarist Ngui, Yong-Hong Lin, Chia-Chung Wang, Ya-Zhen Xu, Chuan-Chi Chien, Rung-Jiun Gau, Yan-Jia Liou and Chun-Shen Cheng
Agronomy 2026, 16(17), 1710; https://doi.org/10.3390/agronomy16171710 - 3 Sep 2026
Abstract
Increasing fertilizer costs, climate-related stresses, and soil degradation caused by the prolonged use of chemical fertilizers threaten the sustainability of agricultural production. Organic soil amendments offer a promising approach to restoring soil health while reducing dependence on synthetic inputs. This study evaluated the
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Increasing fertilizer costs, climate-related stresses, and soil degradation caused by the prolonged use of chemical fertilizers threaten the sustainability of agricultural production. Organic soil amendments offer a promising approach to restoring soil health while reducing dependence on synthetic inputs. This study evaluated the combined effects of biochar and organic fertilizer on soil health and soybean (Glycine max L.) productivity under acidic soil conditions. During the 2024 growing season, a greenhouse pot experiment was conducted using a completely randomized design (CRD) comprising seven treatments. Each treatment was replicated three times, resulting in a total of 21 pots. The treatments consisted of different combinations of biochar (B) and organic fertilizer (F), applied at rates of grams per 10.5 kg of soil: control (B0F0), B35F70, B35F105, B35F140, B70F70, B70F105, and B70F140. Treatment means were compared using the Least Significant Difference (LSD) test at p < 0.05. The results showed that the highest soil pH value (5.41) was recorded under the B35F70 treatment at 45 days after amendment of the acidic soil. Application of the B35F140 treatment resulted in a significant increase (p < 0.05) in electrical conductivity (0.23 mS cm−1) compared with the control. Soil organic matter and available phosphorus reached their highest values under B70F140, at 5.25% and 13.87 mg kg−1, respectively, and were significantly greater than those in the control treatment. Soil available iron (Fe) and manganese (Mn) concentrations also increased significantly (p < 0.05) compared with the control, with the B35F70 and B70F70 treatments resulting in the highest Fe (371.29 mg kg−1) and Mn (37.77 mg kg−1) concentrations, respectively. At 100 days after amendment of the reddish-brown acidic soil, exploratory Pearson correlation analyses were conducted to examine relationships between soil health indicators and soybean performance. Soil available phosphorus and potassium exhibited positive associations with soil pH (r = 0.69 and r = 0.65, respectively; p < 0.01). Soybean growth traits, including plant height and number of leaves, were positively associated with seed yield (r = 0.56 and r = 0.77, respectively; p < 0.01). Furthermore, seed yield was positively correlated with SPAD values (r = 0.80, p < 0.01), soil pH (r = 0.56, p < 0.01), available K (r = 0.68, p < 0.01), and Mg (r = 0.47, p < 0.05). Principal component analysis (PCA) further demonstrated clear treatment clustering and consistent positive relationships among soil properties, plant growth traits, and soybean yield variables. The control treatment was clearly separated from all biochar-organic fertilizer treatments along PC1. Among all treatments, B35F140 (3.33 g biochar kg−1 soil + 13.33 g organic fertilizer kg−1 soil) showed the strongest positive association with soil health and plant growth and produced the highest soybean seed yield (10.77 g plant−1). Overall, the combined use of biochar and organic fertilizer improved soil health, soybean growth, and yield, demonstrating its potential as a sustainable strategy for enhancing soybean productivity under acidic soil conditions.
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(This article belongs to the Section Soil and Plant Nutrition)
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Open AccessArticle
Research on Thrips Object Detection and Counting Algorithms in Leaf Backgrounds
by
Dongxue Huang, Ming Lei, Zhiliang Zhang, Zhangzhang He, Wei Zhan and Yu Zhang
Agronomy 2026, 16(17), 1709; https://doi.org/10.3390/agronomy16171709 - 3 Sep 2026
Abstract
Thrips are major minute pests that threaten crop growth, and their early and accurate detection is of great significance for pest monitoring and precision control. Existing image-based thrips detection studies have mainly focused on insects captured on sticky traps, whereas direct detection of
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Thrips are major minute pests that threaten crop growth, and their early and accurate detection is of great significance for pest monitoring and precision control. Existing image-based thrips detection studies have mainly focused on insects captured on sticky traps, whereas direct detection of thrips on plant surfaces under complex leaf-background conditions remains relatively underexplored. Moreover, in leaf-background images, thrips are extremely small, have indistinct contours, and are susceptible to interference from noise such as leaf textures and spots, resulting in insufficient detection and counting accuracy. To address these issues, this study proposes SCIC-DEIM, a small-object thrips detection model based on the end-to-end detector DEIM. First, leaf-background thrips images were collected at Jingchu University of Technology to construct a dataset containing 5618 images and 47,726 thrips instances. Second, focusing on three key aspects—background redundancy suppression, target response enhancement, and detail information recovery—the SCIC-DEIM model was developed. Specifically, the SCConv and IIA modules were introduced into the backbone network to construct SCIStage, and CARAFE was adopted to replace conventional upsampling operations. Experimental results show that, compared with DEIM, SCIC-DEIM improves Precision, mAP@50, and mAP@50:95 by 2.4, 2.0, and 2.9 percentage points, respectively, while reducing GFLOPs by approximately 20.6%. It also achieves improved MAE, RMSE, and R2 values compared with DEIM. The proposed algorithm demonstrates strong capability in small-object thrips detection under leaf-background conditions and can provide a methodological reference for intelligent monitoring of tiny pests on leaf surfaces in protected agriculture.
Full article
(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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