Agriculture

4 pages, 277 KB

Open AccessEditorial

Multiple Soil Health Assessment Methods for Changing Agricultural Environment

by Irina Gabriela Cara, Iuliana Motrescu and Gerard Jităreanu

Agriculture 2026, 16(17), 1894; https://doi.org/10.3390/agriculture16171894 - 1 Sep 2026

Soil is the foundation of resilient agricultural systems, yet its assessment remains a complex challenge due to its inherent variability across scales and management systems [...] Full article

(This article belongs to the Special Issue Multiple Soil Health Assessment Methods for Changing Agricultural Environment)

44 pages, 3382 KB

Open AccessArticle

Hybrid CNN–LSTM–Linformer Driven Adaptive NMPC for Environmental Control in Pig Housing

by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Bin Li, Honggui Liu, Tiemin Ma, Jinming Liu and Antoine Musengimana

Agriculture 2026, 16(17), 1893; https://doi.org/10.3390/agriculture16171893 - 1 Sep 2026

Abstract

Accurate prediction and energy-efficient control of pig-house environments remain challenging because temperature, relative humidity, NH_3, and CO₂ are nonlinear, strongly coupled, and affected by ventilation and seasonal conditions. This study proposes an intelligent prediction-control framework integrating hybrid deep learning prediction with [...] Read more.

Accurate prediction and energy-efficient control of pig-house environments remain challenging because temperature, relative humidity, NH_3, and CO₂ are nonlinear, strongly coupled, and affected by ventilation and seasonal conditions. This study proposes an intelligent prediction-control framework integrating hybrid deep learning prediction with nonlinear model predictive control for multivariable pig-house environmental regulation. Several hybrid deep learning models were compared, including CNN-LSTM-standard transformer, CNN-LSTM-lightweight transformer, and the proposed CNN-LSTM-linformer-style model. The CNN-LSTM-lightweight transformer achieved the highest overall prediction accuracy, whereas the proposed CNN-LSTM-linformer-style model provided the most compact structure by using separable convolution, global average pooling, and linformer-style attention, reducing training time and memory usage by approximately 53% compared with the CNN-LSTM-standard transformer. The prediction model was integrated with FLC, NMPC, and ANMPC for closed-loop ventilation control. ANMPC adjusts control weights online according to environmental deviations to balance environmental regulation and energy use under disturbances. In the nominal closed-loop simulation, NMPC and ANMPC reduced ventilation energy consumption by approximately 44% compared with FLC, while ANMPC achieved a 4.35% lower NH₃ steady-state error and a 3.5% faster NH₃ recovery response than NMPC under disturbance conditions. In 24 -h pre-field verification, NMPC and ANMPC reduced energy consumption by 35.8% and 27.4%, respectively, while maintaining pollutant safety. Full article

(This article belongs to the Topic AI-Driven Innovations in Animal Farming and Disease Control)

39 pages, 13424 KB

Open AccessArticle

Natural and Anthropogenic Factors Condition Production–Living–Ecology Function Interactions in the Karst Region of Southwest China

by Jingxin Li, Ze Han, Zhaotong Zhang and Suju Li

Agriculture 2026, 16(17), 1892; https://doi.org/10.3390/agriculture16171892 - 1 Sep 2026

Abstract

Coordinating production–living–ecology (PLE) land use requires an accurate account of how PLE functions interact, yet most studies treat these interactions as static attributes and report their thresholds in isolation. We instead treat them as dynamic states conditioned by their driving factors. For the [...] Read more.

Coordinating production–living–ecology (PLE) land use requires an accurate account of how PLE functions interact, yet most studies treat these interactions as static attributes and report their thresholds in isolation. We instead treat them as dynamic states conditioned by their driving factors. For the karst region of southwest China, we identified the leading natural and the leading anthropogenic factors of the PLE functions with Geodetector, fixed one natural and one anthropogenic conditioning axis for each function pair by a pair-level rule, and reconstructed the 2010 and 2019 Pareto frontier of each pair along those two gradients; curve tipping points distinguished reversal-type from buffer-type thresholds, from which regulation zones follow directly. Production–ecology and living–ecology are trade-offs over the fitted range, whereas the production–living boundary rises over most of the production range. Total benefit at the equal-weight reference optimum rose in all three pairs (0.552 to 0.636, 0.701 to 0.726, and 0.823 to 0.872), with no decrease in any of 1000 block bootstrap resamples. Boundaries also differed between areas of rising and of falling constraint function, but the two groups are unbalanced in terms of elevation and population density, so this contrast is reported as a descriptive comparison only. Along the elevation gradient the living–ecology interaction shows one reversal-type threshold in each year, at 595 and 756 m, whose confidence intervals define a precautionary management envelope of 498 to 939 m; along the population density gradient no threshold survived at the sub-watershed scale. The framework gives an operable basis for differentiated regulation in fragile regions. Full article

(This article belongs to the Special Issue Agroecological Transitions and Socio-Ecological Resilience in Traditional Agricultural Landscapes)

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25 pages, 2025 KB

Open AccessReview

Review of Hygrothermal and Mechanical Mechanisms for Kernel Fissuring in Paddy Rice During Rapid Drying

by Wenlong Li, Muyu Zhang, Lijun Zhuo, Jun Xia, Chun Tang, Wenya Zhang, Guiyu Zhang and Pengpeng Yu

Agriculture 2026, 16(17), 1891; https://doi.org/10.3390/agriculture16171891 - 1 Sep 2026

Abstract

Rapid hot-air drying is indispensable, but kernel fissuring remains a major barrier to its broader adoption. This review critically collates and compares studies on empirical tests and numerical simulation, covering hygrothermal-induced stress, the role of glass transition temperature in grain strength, mechanical impact [...] Read more.

Rapid hot-air drying is indispensable, but kernel fissuring remains a major barrier to its broader adoption. This review critically collates and compares studies on empirical tests and numerical simulation, covering hygrothermal-induced stress, the role of glass transition temperature in grain strength, mechanical impact loading, multi-field simulation methods, and in situ imaging-based validation. Achieving effective rapid drying requires a clear understanding of the grain’s coupled hygrothermal and mechanical behavior. This remains a persistent challenge due to the grain’s heterogeneous structure and oversimplified modeling assumptions. Broadly speaking, research follows two distinct paths: one rooted in empirical experiments, the other in multi-field numerical simulation. The two approaches differ substantially in their explanatory frameworks, predictive capabilities, and ability to adapt to real processing conditions. A key point emphasized in this review is that in situ micro-CT imaging is the bridge between the two approaches: it delivers real-time structural data for model calibration and validation, enabling closed-loop integration of experimental observations and numerical predictions. The integrated technical framework proposed here provides a theoretical basis for reducing grain breakage loss and optimizing drying and conveying equipment. Full article

(This article belongs to the Special Issue Innovations in Grain Storage, Handling, and Processing)

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17 pages, 6103 KB

Open AccessArticle

Nondestructive Detection of Sweet Orange Granulation Using Noncontact Acoustic Vibration and Attention-Based Deep Learning

by Dachen Wang, Tao Shi, Yang Pan, Wenlong Li, Lei Zhou, Qing Chen and Xuesong Jiang

Agriculture 2026, 16(17), 1890; https://doi.org/10.3390/agriculture16171890 - 1 Sep 2026

Abstract

Granulation is a major physiological disorder that compromises sweet orange quality. Conventional destructive detection methods lead to food waste and cannot be used for batch inspection. This study proposes a nondestructive approach for detecting granulation in sweet oranges using air-jet transient excitation and [...] Read more.

Granulation is a major physiological disorder that compromises sweet orange quality. Conventional destructive detection methods lead to food waste and cannot be used for batch inspection. This study proposes a nondestructive approach for detecting granulation in sweet oranges using air-jet transient excitation and a laser Doppler vibrometer (LDV). Acoustic vibration spectra were acquired from 640 sweet orange samples. Using both competitive adaptive reweighted sampling (CARS)-extracted feature parameters and raw acoustic vibration spectra as inputs, an ISNet-1D model integrating a multi-scale Inception module and a squeeze-and-excitation (SE) attention mechanism was developed, and its performance was compared against those of partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), and baseline deep learning models including one-dimensional convolutional neural network (1D-CNN), Visual Geometry Group network 16 (VGG16), and residual network v1 (ResNet-v1). The results demonstrated that the ISNet-1D model trained on the full raw vibration spectrum achieved the best performance, with an overall test set accuracy, recall, and specificity of 92.97%, 95.00%, and 91.18%, respectively. Ablation experiments revealed that removal of the Inception branches and the SE module reduced the overall test accuracy by 5.47% and 4.69%, respectively, indicating that their combination effectively extracts multi-scale acoustic vibration features and enhances model precision. Gradient-weighted class activation mapping further identified the critical frequency bands primarily relied upon by the model for prediction. Collectively, noncontact acoustic vibration detection combined with ISNet-1D provides a viable method for nondestructive granulation detection in sweet oranges. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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22 pages, 9229 KB

Open AccessArticle

Straw Biochar Incorporation Reduces CH₄ Emissions in Double-Cropping Rice Paddies and Partially Alleviates Warming-Induced Yield Losses

by Huifang Yang, Jinsong Liu, Bin Zhang, Haoyu Qian, Jixiang Zou and Taotao Yang

Agriculture 2026, 16(17), 1889; https://doi.org/10.3390/agriculture16171889 - 31 Aug 2026

Abstract

Straw biochar has the potential to improve rice productivity and mitigate greenhouse gas emissions. However, its synergistic regulatory effects on yield performance and methane (CH₄) emissions in double-cropping rice systems, especially under the intermediate climate warming scenario, remain poorly understood. A [...] Read more.

Straw biochar has the potential to improve rice productivity and mitigate greenhouse gas emissions. However, its synergistic regulatory effects on yield performance and methane (CH₄) emissions in double-cropping rice systems, especially under the intermediate climate warming scenario, remain poorly understood. A two-year field experiment was carried out from 2022 to 2023 with four treatments: ambient temperature with direct straw incorporation (CKS), ambient temperature with straw biochar incorporation (CKB), warming with direct straw incorporation (WS), and warming with straw biochar incorporation (WB). Rice yield and yield components, CH₄ emission characteristics, soil physicochemical properties, and functional microbial abundance were systematically determined. The results showed that for early rice, compared with CKS, CKB maintained a stable yield, while WS had a significantly reduced yield of 7.01% (p < 0.05); compared with WS, WB had an increased yield of 4.65% (p > 0.05), which was mainly attributed to the increases in grain weight, dry matter accumulation from heading to maturity, and leaf SPAD value after heading. For late rice, compared with CKS, CKB had no significant difference in yield, while WS had a decreased yield of 8.47% (p < 0.05); compared with WS, WB had a significantly increased yield of 6.47% (p < 0.05), mainly due to the increases in spikelets per panicle, dry matter accumulation at tillering, as well as the number of secondary branches and spikelets on secondary branches. For CH₄ emissions, compared with CKS, CKB significantly reduced CH₄ emissions and yield-scaled CH₄ emissions by 48.67% (p < 0.05) and 47.75% (p < 0.05) in early rice, and by 35.22% (p < 0.05) and 37.22% (p < 0.05) in late rice, respectively. Compared with WS, WB significantly reduced CH₄ emissions and yield-scaled CH₄ emissions by 57.81% (p < 0.05) and 59.61% (p < 0.05) in early rice, and by 42.55% (p < 0.05) and 46.09% (p < 0.05) in late rice, respectively. Correlation analysis revealed that CKB and WB reduced CH₄ emissions by decreasing soil

N H_{4}^{+} - N

, DOC, and methanogen abundance in early rice, and by lowering DOC and methanogen abundance in late rice. In summary, straw biochar incorporation can significantly reduce CH₄ emissions from double-cropping rice paddies under ambient temperature. Under the intermediate climate warming scenario, this practice can partially mitigate yield losses (significant in the late-rice season) and simultaneously lower CH₄ emissions. It is an efficient agricultural management strategy adapted to climate warming, providing a key scientific basis and feasible technical pathways for sustainable production in double-cropping rice regions of South China. Full article

(This article belongs to the Section Crop Production)

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28 pages, 8287 KB

Open AccessArticle

A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments

by Jiahuan Lu, Qihan Deng, Weiping Zheng, Binglong Cai, Shan Zeng and Jiehao Li

Agriculture 2026, 16(17), 1888; https://doi.org/10.3390/agriculture16171888 - 31 Aug 2026

Abstract

Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango [...] Read more.

Reliable joint detection of mango fruits and stems is an essential upstream perception task for robotic harvesting, but remains challenging because stems are small, slender, frequently occluded, and visually degraded by illumination variation. This study proposes MangoNET, a YOLOv11n-based framework for joint mango fruit and stem detection in complex orchard environments. A P2 high-resolution detection head preserves fine spatial information for small targets, while SPPF-ELAN aggregates local and contextual features for partially visible objects. SENet recalibrates channel responses under illumination variation, and WIoU v3 regulates bounding-box samples with different localization qualities. A dataset containing 1782 original images of Tainong and Jinhuang mangoes was collected from two orchards and data augmentation was applied only to the training set, increasing its size from 1172 to 2886 images through rotation, contrast adjustment, and Gaussian noise addition. MangoNET achieved fruit and stem F1-scores of 0.920 and 0.916, respectively, with mAP50 and mAP50–95 values of 0.941 and 0.690. Compared with YOLOv11n, mAP50 and mAP50–95 increased by 1.6 and 2.9 percentage points, respectively, while stem recall increased from 0.877 to 0.906. Source-image-independent five-fold cross-validation yielded mean mAP50 and mAP50–95 values of 0.944 and 0.711, respectively. Pilot evaluations using images acquired by a UAV and an RGB-D camera in a geographically distinct orchard suggested that MangoNET could maintain detection performance in a different orchard environment. MangoNET supplies fruit and stem candidate regions for subsequent association, harvesting-point localization, and robotic manipulation. Full article

(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)

17 pages, 6125 KB

Open AccessArticle

Tracking Insects in Controlled Experiments

by Guy Zaidman, Shaked Ben Aharon, Omer Cohen, Shon Hacmon, Guy Shani, Yoshiahu Goldstein and Vered Tzin

Agriculture 2026, 16(17), 1887; https://doi.org/10.3390/agriculture16171887 - 31 Aug 2026

Abstract

Continuous trajectories of small insects can provide measurements of movement distance, speed, resting periods, spatial preference, and contact with plant material, but manual observation is labor-intensive. We present a low-cost smartphone-to-web workflow for aphid tracking in controlled laboratory experiments. The system integrates image [...] Read more.

Continuous trajectories of small insects can provide measurements of movement distance, speed, resting periods, spatial preference, and contact with plant material, but manual observation is labor-intensive. We present a low-cost smartphone-to-web workflow for aphid tracking in controlled laboratory experiments. The system integrates image acquisition, online upload and experiment management, training-free detection using multi-threshold binarization and blob filtering, Kalman-filter prediction, global nearest-neighbor association, trajectory visualization, and data export. The contribution is the accessible end-to-end integration of established methods rather than a new detection or tracking algorithm. Across nine controlled sequences, the mean precision, recall, and Multiple Object Tracking Accuracy (MOTA) were 0.852, 0.845, and 0.671, respectively, with MOTA ranging from 0.122 to 0.915. Performance was highest under clean, well-focused conditions and degraded in the presence of blur, dirt, and aphid-like stationary objects. The current implementation assumes a standardized overhead view and a light, visually uniform background. field deployment and tracking accuracy over multi-hour or multi-day sequences were not evaluated. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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25 pages, 5425 KB

Open AccessArticle

Reducing Nitrogen Application and Using Biogas Residues Improve Yield and Nitrogen Use Efficiency in Maize Grown Under Drought Conditions

by Shijie Huang, Weiqiang Wang, Feng Wang, Haofeng Meng, Chunqing Miao, Jian Du and Shuping He

Agriculture 2026, 16(17), 1886; https://doi.org/10.3390/agriculture16171886 - 31 Aug 2026

Abstract

Excessive chemical fertilizer and low N use efficiency constrain mulched drip irrigated maize production in China’s Hexi Oasis, while livestock manure is underutilized. Recycling biogas residue and slurry offers a solution, but optimal substitution ratios under reduced N remain unknown. We conducted a [...] Read more.

Excessive chemical fertilizer and low N use efficiency constrain mulched drip irrigated maize production in China’s Hexi Oasis, while livestock manure is underutilized. Recycling biogas residue and slurry offers a solution, but optimal substitution ratios under reduced N remain unknown. We conducted a two year field experiment with two reduced N rates (15% and 30% relative to the conventional local rate of 360 kg N ha⁻¹) combined with biogas residue replacing basal N at rates of 25%, 50%, or 75% and biogas slurry replacing 50% of topdressed N. The best performing treatment (N₁25: 15% N reduction + 25% residue substitution) significantly increased LAI (11.77%), SPAD value (10.98%), dry matter (2.51%), leaf and stem N translocation (13.81% and 10.00%, respectively), leading to a 2.14% yield gain. NUE, ANUE, and PFPN improved by 27.4%, 19.5%, and 18.5%. Path analysis revealed that N₁25 enhanced canopy photosynthesis and dry matter translocation, increasing grain weight. These findings demonstrate that integrated N reduction with biogas substitutes enhances productivity and N efficiency while recycling organic waste. Therefore, under the present experimental conditions, N₁25 represents a promising strategy for simultaneously improving grain yield and nitrogen use efficiency; however, multisite and long term validation is warranted to substantiate its broader applicability. Full article

(This article belongs to the Special Issue Organic Fertilizer Substitution: Effects on Soil Fertility, Crop Productivity, and Environmental Sustainability)

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52 pages, 1148 KB

Open AccessReview

Allelopathy and Allelochemicals: Sources, Mechanisms of Action, and Their Role in Integrated and Sustainable Crop Protection

by Emanuela Talarico, Eleonora Greco, Francesco Guarasci, Marina Camoli, Leonardo Bruno and Fabrizio Araniti

Agriculture 2026, 16(17), 1885; https://doi.org/10.3390/agriculture16171885 - 30 Aug 2026

Abstract

Allelopathy is an ecological process in which released chemicals modify the performance of neighbouring organisms, whereas phytotoxicity describes an inhibitory response to a substance under a defined assay and, by itself, does not demonstrate field-level allelopathy. This critical narrative review combines a structured [...] Read more.

Allelopathy is an ecological process in which released chemicals modify the performance of neighbouring organisms, whereas phytotoxicity describes an inhibitory response to a substance under a defined assay and, by itself, does not demonstrate field-level allelopathy. This critical narrative review combines a structured re-screening of the literature with targeted updating through 21 August 2026. Searches used combinations of allelopath*, allelochemical*, phytotox*, bioherbicid*, hormesis, biostimulant*, rhizosphere, microbiome, formulation, resistance, and regulation; peer-reviewed studies were complemented by authoritative regulatory and resistance databases. Evidence was appraised according to experimental context and mechanistic strength, explicitly distinguishing field or whole-plant validation from controlled bioassays, experimentally supported molecular targets from physiological or omics associations, and computational predictions. The synthesis covers chemical diversity, botanical and microbial sources, agro-industrial by-products, mechanisms of phytotoxicity and low-dose responses, rhizosphere interactions, and formulation strategies. Important corrections emerge from this evidence hierarchy: strigolactones are carotenoid-derived apocarotenoid hormones/signals rather than sesquiterpenes; multi-target activity does not preclude resistance evolution; cyanobacterial responses cannot be directly extrapolated to weeds or crops; and hormetic stimulation under controlled conditions is not equivalent to reliable field biostimulant performance. The principal translational gaps are insufficient dose–response standardisation, limited crop-selectivity and field validation, variable biomass chemistry, incomplete carrier and non-target safety data, and regulatory uncertainty for multifunctional products. Allelochemicals therefore represent promising components of integrated crop protection, but their agronomic value depends on rigorous evidence, formulation-specific validation, and context-dependent deployment rather than on laboratory phytotoxicity alone. Full article

(This article belongs to the Special Issue Regulatory Mechanisms of Exogenous Natural Compounds in the Growth and Stress Resistance of Horticultural Crops)

33 pages, 74908 KB

Open AccessArticle

CIAFNet: An RGB-D Cross-Modal Interaction and Adaptive Fusion Network for Camellia oleifera Fruit Detection

by Yan Chen, Chengxin Yang, Chao Yuan, Yiming Lu, Dandan Fu, Yinghui Fang, Shuman Liu and Hui Ai

Agriculture 2026, 16(17), 1884; https://doi.org/10.3390/agriculture16171884 - 30 Aug 2026

Abstract

To better address the accuracy bottleneck of RGB-only Camellia oleifera C.Abel fruit detection in complex orchard environments, this paper proposes CIAFNet—a dual-stream RGB-D fusion detection network—and evaluates its potential as a visual front end for relative 3D localization using sensor-measured depth and camera [...] Read more.

To better address the accuracy bottleneck of RGB-only Camellia oleifera C.Abel fruit detection in complex orchard environments, this paper proposes CIAFNet—a dual-stream RGB-D fusion detection network—and evaluates its potential as a visual front end for relative 3D localization using sensor-measured depth and camera back projection under controlled conditions. With RGB images and depth maps as parallel dual-branch inputs, the network integrates the C3k2_PartialNetBlock for efficient intra-modal feature extraction with reduced computational redundancy, devises the cross-modal interaction and difference-aware adaptive fusion (CIDAF) module for adaptive cross-modal feature fusion, and adopts an SC-EUCB-augmented BiFPN in the neck to optimize multiscale feature aggregation and detail restoration during upsampling. Pseudo-depth maps generated from natural orchard RGB images via Depth Anything V2 were paired with RGB counterparts to build an RGB–pseudo-depth dataset. Synchronized RGB-D data collected by an Intel RealSense D435i under controlled conditions were used to quantify pseudo-to-sensor depth discrepancies and evaluate input adaptability. On the natural orchard test set, CIAFNet achieved 93.33% mAP@0.5 with only 10.49 GFLOPs and 3.80 M parameters. Second-stage fine-tuning improved CIAFNet’s adaptation to D435i-measured depth under controlled conditions. In the subsequent relative displacement consistency experiment, the mean absolute consistency errors along the X, Y, and Z axes were 3.10, 3.15, and 3.27 mm, respectively, and the mean 3D Euclidean consistency error was 5.60 mm. These results demonstrate that CIAFNet improves Camellia oleifera fruit detection using natural orchard RGB–pseudo-depth data and has potential as a visual front end for relative 3D localization under controlled conditions. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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27 pages, 7915 KB

Open AccessArticle

DualSlim-YOLO: A Lightweight Detection Model Based on Unmanned Aerial Vehicle Imagery for Cauliflower Seedling Identification and Growth Assessment

by Yike Wang, Jun Zhang, Dongfang Zhang, Yanxu Hou, Xinzhuo Gao, Jing Cui, Xiaofei Fan, Xingwei Yao and Deling Sun

Agriculture 2026, 16(17), 1883; https://doi.org/10.3390/agriculture16171883 - 30 Aug 2026

Abstract

Cauliflower emergence rate and seedling growth are key indicators of field conditions and varietal potential. Traditional manual surveys are unsuitable for continuous monitoring across multiple varieties. This study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor [...] Read more.

Cauliflower emergence rate and seedling growth are key indicators of field conditions and varietal potential. Traditional manual surveys are unsuitable for continuous monitoring across multiple varieties. This study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth. Built on YOLOv11, the model incorporates a lightweight feature extraction structure and an optimized detection-scale configuration. It reduces computational complexity while maintaining detection accuracy, thereby improving the efficiency of cauliflower seedling detection. DualSlim-YOLO achieved P, R, F1-score, mAP@0.5, and mAP@0.5:0.95 of 95.35%, 96.75%, 96.05%, 98.55%, and 86.65%, respectively. The number of parameters was reduced by 38.61%, while the inference speed increased by 22.16%, demonstrating good lightweight performance. Based on this model, UAV images of 171 cauliflower varieties acquired at 7, 21, and 28 d after transplanting were used for seedling detection and emergence rate estimation. In addition, 18 time-series seedling phenotypic traits were extracted, enabling a comprehensive quantitative evaluation of emergence dynamics and early-growth performance across multiple cauliflower varieties. This method effectively screens cauliflower varieties for high emergence rates, rapid emergence, and excellent seedling growth performance. It provides technical support for high-throughput, nondestructive seedling phenotyping and early germplasm screening under field conditions. Full article

(This article belongs to the Special Issue Unmanned Aerial System for Crop Monitoring in Precision Agriculture)

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43 pages, 7769 KB

Open AccessReview

Dielectric Sensing and Multiphysics Modelling for Electrostatic Separation in Rice Cleaning Systems

by Xinyang Gu, Zhong Tang and Jiahao Shen

Agriculture 2026, 16(17), 1882; https://doi.org/10.3390/agriculture16171882 - 30 Aug 2026

Abstract

Agricultural threshing mixtures are moist, heterogeneous, and dynamically disturbed, conditions that reduce the selectivity of cleaning systems based solely on size, density, and aerodynamic response. Electrostatic separation introduces an additional property dimension, but its application to raw paddy rice remains constrained by moisture-sensitive [...] Read more.

Agricultural threshing mixtures are moist, heterogeneous, and dynamically disturbed, conditions that reduce the selectivity of cleaning systems based solely on size, density, and aerodynamic response. Electrostatic separation introduces an additional property dimension, but its application to raw paddy rice remains constrained by moisture-sensitive charging, irregular particle motion, wall deposition, and variable feed conditions. This critical review synthesizes rice-specific studies on dielectric and material-state sensing with evidence concerning particle charging, electro-aerodynamic transport, multiphysics modelling, and process control. The available literature shows that dielectric measurements can characterize moisture- and density-dependent material states, but they do not directly quantify charge retention or electrostatic separability. Rice-specific evidence remains concentrated in dielectric characterization and machinery operating conditions, whereas electrostatic processing studies mainly concern processed rice-derived materials or analogous particle systems. Direct evidence from raw paddy threshing mixtures under continuous and field-relevant operating conditions remains limited. Based on the evidence, a four-zone framework is proposed comprising feed dispersion, charge acquisition, electro-aerodynamic deflection, and outlet collection. The framework identifies the measurements, model parameters, and validation steps required to relate dielectric state, charge decay, particle trajectories, and control actions, thereby providing a research basis for the development and rice-specific validation of electrostatic cleaning systems. Full article

(This article belongs to the Section Agricultural Technology)

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28 pages, 3828 KB

Open AccessArticle

The Impact of Rural Population Aging on Food Prices: Empirical Evidence from China

by Zhen Nie, Zhenzhen Liu, Wen Li, Qiongyao Liu and Jiaxing Pang

Agriculture 2026, 16(17), 1881; https://doi.org/10.3390/agriculture16171881 - 30 Aug 2026

Abstract

Stabilizing food prices is essential for ensuring food security in an aging society. Using panel data from 30 Chinese provinces spanning 2005 to 2022, this study employs a nonlinear panel model and a Spatial Durbin Model (SDM) to analyze the impact of rural [...] Read more.

Stabilizing food prices is essential for ensuring food security in an aging society. Using panel data from 30 Chinese provinces spanning 2005 to 2022, this study employs a nonlinear panel model and a Spatial Durbin Model (SDM) to analyze the impact of rural population aging on food prices and its spatial spillover effects. This study derives the following findings based on empirical research: (1) Rural population aging exhibits a significant inverted U-shaped relationship with food prices, with an inflection point at approximately 19.03%. Before reaching this point, rural population aging helps facilitate food prices. When the inflection point is passed, rural population aging adversely impacts food prices. This effect is significant in western regions but not in eastern and central regions. (2) Farmland transfer and agricultural technological progress significantly influence this relationship, causing the curve to reverse into a U-shaped pattern, which implies a gradual future increase in food prices. (3) Local rural population aging has a significant U-shaped spillover effect on food prices in neighboring provinces. These findings indicate that China’s rural population aging presents a complex dynamic for food price fluctuations. To address the current changes in the population and capital structure and ensure food security, the government will need to formulate forward-looking policies, further improve socialized agricultural services, and systematically optimize food production models. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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30 pages, 4767 KB

Open AccessArticle

Two-Layer Conditional Prediction of Soil Electrical Conductivity Assisted by Post-Irrigation Soil Moisture Trajectories in Farmland of the Bachu Irrigation District

by Pengfei Xu, Zhiguo Wang, Qingyong Bian and Liang Ma

Agriculture 2026, 16(17), 1880; https://doi.org/10.3390/agriculture16171880 - 30 Aug 2026

Abstract

Using multi-depth continuous observations from nine farmland sites in the Bachu irrigation district, Xinjiang (November 2024–April 2026), this study developed a two-layer conditional prediction model to test whether predicted soil-moisture trajectories improve apparent electrical conductivity (EC) prediction beyond a persistence benchmark. Four prediction [...] Read more.

Using multi-depth continuous observations from nine farmland sites in the Bachu irrigation district, Xinjiang (November 2024–April 2026), this study developed a two-layer conditional prediction model to test whether predicted soil-moisture trajectories improve apparent electrical conductivity (EC) prediction beyond a persistence benchmark. Four prediction windows (0–1, 1–3, 3–7, and 7–15 d) were evaluated using time-forward validation under observed ERA5 forcing, paired ablation, SHAP, and post-hoc meteorological-deficit linkage analysis. For EC₂₀, the R² values were 0.978, 0.923, 0.727, and −1.376, with corresponding Skill values of −0.001, 0.277, 0.540, and 0.635; absolute performance at 7–15 d remained unreliable. EC₄₀ achieved R² of 0.943–0.987, with Skill of 0.029–0.325, and EC₆₀ achieved R² of 0.737–0.974, with Skill of 0.065–0.177. Ablation showed the clearest gain for EC₂₀ at 3–7 d (ΔR² = 0.193; ΔSkill = 0.325), whereas most other combinations showed no consistent improvement. At 0–1 d, SHAP contributions of predicted moisture trajectories to EC₂₀, EC₄₀, and EC₆₀ were 39.2%, 37.6%, and 26.8%, respectively. Meteorological-deficit linkage responses varied by depth and horizon but were generally limited. Overall, trajectory benefits were depth- and horizon-dependent and reflected model dependence rather than causal water–salt mechanisms. The framework is currently applicable mainly to monitored, unfrozen post-irrigation periods without additional wetting recharge. Full article

(This article belongs to the Section Agricultural Water Management)

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28 pages, 20167 KB

Open AccessArticle

Effects of Strip Configurations on Canopy Photosynthetic Performance and Resource Use Efficiency of Winter-Seeded Spring Wheat Relay Intercropped with Sunflower in the Hetao Irrigation District

by Xuede Luan, Fan Xia, Rui Chen, Mengyuan Li, Min Xie, Qi Gao and Yongping Zhang

Agriculture 2026, 16(17), 1879; https://doi.org/10.3390/agriculture16171879 - 30 Aug 2026

Abstract

The traditional spring wheat–sunflower relay intercropping system in the Hetao Irrigation District of Inner Mongolia is constrained by restrictions on sowing time, relatively low resource use efficiency, and suboptimal strip configurations. Based on the winter-seeding technique for spring wheat, this study established an [...] Read more.

The traditional spring wheat–sunflower relay intercropping system in the Hetao Irrigation District of Inner Mongolia is constrained by restrictions on sowing time, relatively low resource use efficiency, and suboptimal strip configurations. Based on the winter-seeding technique for spring wheat, this study established an annual double-cropping system of winter-seeded spring wheat relay intercropped with sunflower. The objectives were to evaluate crop photosynthetic performance, grain yield, resource use efficiency, and economic benefits under different strip configurations and to identify a suitable strip arrangement. A two-year fixed-site field experiment was conducted from 2023 to 2025. Four winter-seeded spring wheat/sunflower relay-intercropping treatments (W9S2, W9S4, W18S2, and W18S4) were compared with sole-cropped winter-seeded spring wheat and sole-cropped sunflower. Relay intercropping increased leaf area index, SPAD values, and the net photosynthetic rate of both crops at key growth stages and also improved grain yield and resource use efficiency. Among the strip configurations, W18S2 showed the best overall performance. Its two-year average grain yields of wheat and sunflower were 13.2% and 32.2% higher, respectively, than those of the corresponding sole-cropping treatments. The land equivalent ratio and nitrogen uptake equivalent ratio of all relay-intercropping treatments were greater than 1, with the highest values observed under W18S2. In addition, W18S2 had higher light use efficiency, water use efficiency, and nitrogen partial factor productivity than the sole-cropping treatments and the other relay-intercropping configurations. The two-year average net profit of W18S2 was 46.8% and 8.0% higher than that of sole-cropped wheat and sole-cropped sunflower, respectively. Overall, the superior performance of W18S2 was associated with a more favorable canopy structure and photosynthetic performance, greater dry matter accumulation, and coordinated improvements in grain yield, resource use efficiency, and economic benefits. These findings provide a reference for the high-yield and resource-efficient cultivation of winter-seeded spring wheat relay intercropped with sunflower in the Hetao Irrigation District. Full article

(This article belongs to the Topic Advances in Cultivation Techniques for Increasing Crop Yield)

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23 pages, 21743 KB

Open AccessArticle

Integrated Analysis of Metabolome and Transcriptome Provides New Insights into the Genetic Basis Underlying the Regulation of α-Linolenic Acid Biosynthesis in Perilla frutescens Seeds

by Yukun Wang, Yuan Yuan, Yunna Zhu, Jianguo Liu and Hong Ye

Agriculture 2026, 16(17), 1878; https://doi.org/10.3390/agriculture16171878 - 30 Aug 2026

Abstract

Perilla (Perilla frutescens) is an important oil-bearing crop rich in α-linolenic acid (ALA), and seed oil quality varies greatly among different germplasms. However, the molecular and metabolic mechanisms underlying genotypic differences in ALA accumulation remain unclear. In this study, four Perilla [...] Read more.

Perilla (Perilla frutescens) is an important oil-bearing crop rich in α-linolenic acid (ALA), and seed oil quality varies greatly among different germplasms. However, the molecular and metabolic mechanisms underlying genotypic differences in ALA accumulation remain unclear. In this study, four Perilla varieties with distinct seed phenotypic traits were used to investigate the variations in seed quality, metabolome, and transcriptome. Significant genotypic differences were observed in seed color, thousand-grain weight, and oil content. QO8 showed the highest seed oil content, while QS5 and QO10 exhibited relatively lower oil accumulation levels. Metabolome analysis revealed that lipid metabolism was the dominant metabolic category in Perilla seeds. Multiple differentially accumulated metabolites (DAMs), including ALA, stearic acid, traumatic acid, and 10-OPDA, displayed genotype-specific accumulation patterns. KEGG enrichment demonstrated that α-linolenic acid metabolism and unsaturated fatty acid biosynthesis were the most significantly divergent pathways among different Perilla germplasms. Transcriptome analysis identified numerous differentially expressed genes (DEGs) involved in fatty acid and ALA biosynthesis, such as FAD2, LOX, AOS, AOC, OPR, KAT, ECH, and ACOX. Integrated transcriptome and metabolome analysis further confirmed that the differential expression of structural genes altered the metabolic flux of the ALA and downstream jasmonic acid pathway, resulting in varied accumulation of core lipid intermediates. In addition, WRKY and MYB transcription factors were identified as key upstream regulators that positively or negatively modulated ALA metabolic homeostasis. This study systematically clarified the phenotypic, metabolic, and transcriptional differences in seeds of different Perilla varieties and revealed the core regulatory network of ALA biosynthesis. These findings provide valuable candidate genes and a theoretical foundation for elucidating the molecular mechanism of high ALA accumulation and quality improvement in Perilla seeds. Full article

(This article belongs to the Special Issue Genetic Diversity Assessment and Breeding of Ornamental Crops)

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29 pages, 2570 KB

Open AccessReview

Hyperprolificacy in Modern Sow Genetics: A Review of the Neonatal, Immunological, and Welfare Costs of Increased Litter Size

by Vasileios G. Papatsiros, Georgios I. Papakonstantinou and Nikolaos Tsekouras

Agriculture 2026, 16(17), 1877; https://doi.org/10.3390/agriculture16171877 - 29 Aug 2026

Abstract

Over the past two decades, genetic selection has increased average total litter size in commercial sow herds from approximately 9–10 piglets to more than 14 piglets per farrowing, an achievement that has delivered clear economic benefit but has been accompanied by a parallel [...] Read more.

Over the past two decades, genetic selection has increased average total litter size in commercial sow herds from approximately 9–10 piglets to more than 14 piglets per farrowing, an achievement that has delivered clear economic benefit but has been accompanied by a parallel rise in pre-weaning mortality (PWM), which is now commonly reported in the range of 12–20% and, by some indicative estimates, is trending upward even in well-managed herds. This review synthesizes the veterinary and reproductive-physiology literature on hyperprolificacy in sows, examining its downstream consequences for individual piglet birth weight, farrowing duration, colostrum access, and passive immunoglobulin G (IgG) transfer. Attention is given to the biological mechanism by which litter size dilutes colostral IgG, including vaccine-induced maternally derived antibody (MDA) against porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2), and to the strength of the supporting evidence with regard to each pathogen, which differs considerably. This evidence is considerably stronger for PCV2, where maternal vaccination has a directly demonstrated effect on offspring antibody titres, than for PRRSV, where litter-size-specific dilution of maternally derived antibody has not yet been directly measured and remains an inferred, biologically plausible mechanism rather than a demonstrated one. The review further appraises management interventions including cross-fostering, split suckling, nurse sows, immunoglobulin supplementation, and structured neonatal triage, together with their documented trade-offs and considers the animal-welfare dimension of continued genetic selection for litter size. A dedicated section evaluates the current state of precision livestock farming (automated farrowing and crushing surveillance, computer-vision piglet weighing, and genomic selection for litter uniformity and robustness), concluding that these tools are promising but largely still at the research or early-adoption stage. We conclude that inadequate transfer of passive immunity behaves as an independent risk factor for mortality, separate from birth weight, and that management responses should be viewed as necessary complements to, rather than substitutes for, a re-balancing of genetic selection indices toward piglet survivability. Full article

(This article belongs to the Special Issue Enhancing Piglet Health, Welfare, and Pre‑Weaning Survival in Hyperprolific Sow Systems)

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22 pages, 4820 KB

Open AccessArticle

Non-Destructive Detection of Nutritional Elements in Fresh Tea Leaves Using Hyperspectral Technology Combined with a Multi-Stage Feature Selection Strategy

by Yang Guo, Bo Zhou, Jianlong Li, Jiaming Chen, Zhirui Yan, Jinchi Tang and Yiyong Chen

Agriculture 2026, 16(17), 1876; https://doi.org/10.3390/agriculture16171876 - 29 Aug 2026

Abstract

This study addresses two key challenges in tea nutrient analysis: the limited range of detectable nutrient elements in tea gardens and the interference caused by moisture in fresh tea leaves during spectral data acquisition. To overcome these issues, hyperspectral technology combined with effective [...] Read more.

This study addresses two key challenges in tea nutrient analysis: the limited range of detectable nutrient elements in tea gardens and the interference caused by moisture in fresh tea leaves during spectral data acquisition. To overcome these issues, hyperspectral technology combined with effective spectral intelligent processing algorithms was used to develop quantitative, non-destructive prediction models for four essential nutrients—nitrogen, phosphorus, potassium and carbon—in fresh tea leaves. This study utilises EPO to address the issue of moisture interference in the spectra of fresh tea leaves, and combines it with SG for spectral data processing, namely SG-EPO. Through comparative analysis with traditional pre-processing algorithms, this method was found to effectively reduce moisture interference in fresh tea leaves and enhance prediction accuracy (R²). Finally, based on a multi-stage feature selection strategy involving SG-EPO-VCPA-IRIV-SVM_RFE and SG-EPO-BOSS-SVM_RFE, and in combination with three machine learning models—XGBoost, BP and SVR—quantitative prediction models were developed. The results indicated that the R² for nitrogen is 0.896; phosphorus, 0.954; potassium, 0.913; and carbon, 0.928. The RMSEP values for N, P, K, and C were 0.062, 0.075, 0.438, and 0.157, respectively. Using this model, nitrogen, phosphorus, potassium and carbon contents were rapidly predicted in tea leaves after the exogenous application of GABA at different concentrations, enabling an assessment of the effects of exogenous GABA application on these nutrient levels. Furthermore, the Shapley Additive Explanation method was employed to identify the feature wavelengths that had the greatest contribution to the XGBoost model, effectively explaining the information underlying the improved model predictions regarding the correlation between spectral and chemical values. Finally, the accuracy of the predictions was confirmed using 20 samples from independent data, demonstrating that the proposed model can achieve rapid, non-destructive detection of multiple nutrient elements in fresh tea leaves under in situ conditions in tea plantations. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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