Agriculture

13 pages, 1746 KiB

Open AccessArticle

Calibration of DEM Parameters and Microscopic Deformation Characteristics During Compression Process of Lateritic Soil with Different Moisture Contents

by Chao Ji, Wanru Liu, Yiguo Deng, Yeqin Wang, Peimin Chen and Bo Yan

Agriculture 2025, 15(14), 1548; https://doi.org/10.3390/agriculture15141548 - 18 Jul 2025

Abstract

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on [...] Read more.

Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments. Full article

(This article belongs to the Section Agricultural Technology)

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17 pages, 11610 KiB

Open AccessArticle

Exploring the Impact of Species Participation Levels on the Performance of Dominant Plant Identification Models in the Sericite–Artemisia Desert Grassland by Using Deep Learning

by Wenhao Liu, Guili Jin, Wanqiang Han, Mengtian Chen, Wenxiong Li, Chao Li and Wenlin Du

Agriculture 2025, 15(14), 1547; https://doi.org/10.3390/agriculture15141547 - 18 Jul 2025

Abstract

Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. [...] Read more.

Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. The identification model performances were optimal, with a high level of S. transiliense participation and with DeepLabv3p, PSPNet, and UNet achieving an overall accuracy (OA) of 97.86%, 96.51%, and 98.20%. Among the tested models, UNet exhibited the highest classification accuracy and robustness with small sample datasets, effectively differentiating between S. transiliense, C. arenarius, and bare ground. However, when C. arenarius was the primary target species, the model’s performance declined as its participation levels increased, exhibiting significant omission errors for S. transiliense, whose producer’s accuracy (PA) decreased by 45.91%. The findings of this study provide effective technical means and theoretical support for the identification of plant species and ecological monitoring in sericite–Artemisia desert grasslands. Full article

(This article belongs to the Section Digital Agriculture)

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19 pages, 4026 KiB

Open AccessArticle

The Fusion of Focused Spectral and Image Texture Features: A New Exploration of the Nondestructive Detection of Degeneration Degree in Pleurotus geesteranus

by Yifan Jiang, Jin Shang, Yueyue Cai, Shiyang Liu, Ziqin Liao, Jie Pang, Yong He and Xuan Wei

Agriculture 2025, 15(14), 1546; https://doi.org/10.3390/agriculture15141546 - 18 Jul 2025

Abstract

The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image [...] Read more.

The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus. Full article

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

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16 pages, 2433 KiB

Open AccessArticle

A Single-Cell Assessment of Intramuscular and Subcutaneous Adipose Tissue in Beef Cattle

by Mollie M. Green, Hunter R. Ford, Alexandra P. Tegeler, Oscar J. Benitez, Bradley J. Johnson and Clarissa Strieder-Barboza

Agriculture 2025, 15(14), 1545; https://doi.org/10.3390/agriculture15141545 - 18 Jul 2025

Abstract

Deposition of intramuscular fat (IM), also known as marbling, is the deciding factor of beef quality grade in the U.S. Defining molecular mechanisms underlying the differential deposition of adipose tissue in distinct anatomical areas in beef cattle is key to the development of [...] Read more.

Deposition of intramuscular fat (IM), also known as marbling, is the deciding factor of beef quality grade in the U.S. Defining molecular mechanisms underlying the differential deposition of adipose tissue in distinct anatomical areas in beef cattle is key to the development of strategies for marbling enhancement while limiting the accumulation of excessive subcutaneous adipose tissue (SAT). The objective of this exploratory study was to define the IM and SAT transcriptional heterogeneity at the whole tissue and single-nuclei levels in beef steers. Longissimus dorsi muscle samples (9–11th rib) were collected from two finished beef steers at harvest to dissect matched IM and adjacent SAT (backfat). Total RNA from IM and SAT was isolated and sequenced in an Illumina NovaSeq 6000. Nuclei from the same samples were isolated by dounce homogenization, libraries generated with 10× Genomics, and sequenced in an Illumina NovaSeq 6000, followed by analysis via Cell Ranger pipeline and Seurat in RStudio (v4.3.2) By the expression of signature marker genes, single-nuclei RNA sequencing (snRNAseq) analysis identified mature adipocytes (AD; ADIPOQ, LEP), adipose stromal and progenitor cells (ASPC; PDGFRA), endothelial cells (EC; VWF, PECAM1), smooth muscle cells (SMC; NOTCH3, MYL9) and immune cells (IMC; CD163, MRC1). We detected six cell clusters in SAT and nine in IM. Across IM and SAT, AD was the most abundant cell type, followed by ASPC, SMC, and IMC. In SAT, AD made up 50% of the cellular population, followed by ASPC (31%), EC (14%), IMC (1%), and SMC (4%). In IM depot, AD made up 23% of the cellular population, followed by ASPC at 19% of the population, EC at 28%, IMC at 7% and SMC at 12%. The abundance of ASPC and AD was lower in IM vs. SAT, while IMC was increased, suggesting a potential involvement of immune cells on IM deposition. Accordingly, both bulk RNAseq and snRNAseq analyses identified activated pathways of inflammation and metabolic function in IM. These results demonstrate distinct transcriptional cellular heterogeneity between SAT and IM depots in beef steers, which may underly the mechanisms by which fat deposits in each depot. The identification of depot-specific cell populations in IM and SAT via snRNAseq analysis has the potential to reveal target genes for the modulation of fat deposition in beef cattle. Full article

(This article belongs to the Special Issue Current Research and Strategies for Improving Farm Animal Meat Quality)

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22 pages, 4732 KiB

Open AccessArticle

Improving Winter Wheat Yield Estimation Under Saline Stress by Integrating Sentinel-2 and Soil Salt Content Using Random Forest

by Chuang Lu, Maowei Yang, Shiwei Dong, Yu Liu, Yinkun Li and Yuchun Pan

Agriculture 2025, 15(14), 1544; https://doi.org/10.3390/agriculture15141544 - 18 Jul 2025

Abstract

Accurate estimation of winter wheat yield under saline stress is crucial for addressing food security challenges and optimizing agricultural management in regional soils. This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method [...] Read more.

Accurate estimation of winter wheat yield under saline stress is crucial for addressing food security challenges and optimizing agricultural management in regional soils. This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method to improve yield estimation of winter wheat in Kenli County, a typical saline area in China’s Yellow River Delta. First, feature importance analysis of a temporal vegetation index (VI) and salinity index (SI) across all growth periods were achieved to select main parameters. Second, yield models of winter wheat were developed in VI-, SI-, VI + SI-, and VI + SI + SC-based groups. Furthermore, error assessment and spatial yield mapping were analyzed in detail. The results demonstrated that feature importance varied by growth periods. SI dominated in pre-jointing periods, while VI was better in the post-jointing phase. The VI + SI + SC-based model achieved better accuracy (R² = 0.78, RMSE = 720.16 kg/ha) than VI-based (R² = 0.71), SI-based (R² = 0.69), and VI + SI-based (R² = 0.77) models. Error analysis results suggested that the residuals were reduced as the input parameters increased, and the VI + SI + SC-based model showed a good consistency with the field-measured yields. The spatial distribution of winter wheat yield using the VI + SI + SC-based model showed significant differences, and average yields in no, slight, moderate, and severe salinity areas were 7945, 7258, 5217, and 4707 kg/ha, respectively. This study can provide a reference for winter wheat yield estimation and crop production improvement in saline regions. Full article

(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)

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21 pages, 1206 KiB

Open AccessArticle

Evaluation of Olive Mill Waste Compost as a Sustainable Alternative to Conventional Fertilizers in Wheat Cultivation

by Ana García-Rández, Silvia Sánchez Méndez, Luciano Orden, Francisco Javier Andreu-Rodríguez, Miguel Ángel Mira-Urios, José A. Sáez-Tovar, Encarnación Martínez-Sabater, María Ángeles Bustamante, María Dolores Pérez-Murcia and Raúl Moral

Agriculture 2025, 15(14), 1543; https://doi.org/10.3390/agriculture15141543 - 17 Jul 2025

Abstract

This study evaluates the agronomic and environmental performance of pelletized compost derived from olive mill waste as a sustainable alternative to mineral fertilizers for cultivating wheat (Triticum turgidum L.) under conventional tillage methods. A field experiment was conducted in semi-arid Spain, employing [...] Read more.

This study evaluates the agronomic and environmental performance of pelletized compost derived from olive mill waste as a sustainable alternative to mineral fertilizers for cultivating wheat (Triticum turgidum L.) under conventional tillage methods. A field experiment was conducted in semi-arid Spain, employing three fertilization strategies: inorganic (MAP + Urea), sewage sludge (SS), and organic compost pellets (OCP), each providing 150 kg N ha⁻¹. The parameters analyzed included wheat yield, grain quality, soil properties, and greenhouse gas (GHG) emissions. Inorganic fertilization yielded the highest productivity and nutrient uptake. However, the OCP treatment reduced grain yield by only 15%, while improving soil microbial activity and enzymatic responses. The SS and OCP treatments showed increased CO₂ and N₂O emissions compared to the control and inorganic plots. However, the OCP treatment also acted as a CH₄ sink. Nutrient use efficiency was greatest under mineral fertilization, though the OCP treatment outperformed the SS treatment. These results highlight the potential of OCP as a circular bio-based fertilizer that can enhance soil function and partially replace mineral inputs. Optimizing application timing is critical to aligning nutrient release with crop demand. Further long-term trials are necessary to evaluate their impact on the soil and improve environmental outcomes. Full article

(This article belongs to the Special Issue Fertilizer Management Strategies for Enhancing the Growth, Yield and Quality in Crops)

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5 pages, 179 KiB

Open AccessEditorial

Leaning on Smart Agricultural Systems for Crop Monitoring

by Adrian Gracia-Romero, Karen Marti-Jerez and Fabio Fania

Agriculture 2025, 15(14), 1542; https://doi.org/10.3390/agriculture15141542 - 17 Jul 2025

Abstract

Contemporary precision agriculture and breeding programs are heavily dependent on the evaluation of a high number of experimental plots, usually involving different genotypes, irrigation methods, and managing systems [...] Full article

(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)

16 pages, 992 KiB

Open AccessArticle

Relative Growth Rate and Specific Absorption Rate of Nutrients in Lactuca sativa L. Under Secondary Paper Sludge Application and Soil Contamination with Lead

by Elena Ikkonen and Marija Yurkevich

Agriculture 2025, 15(14), 1541; https://doi.org/10.3390/agriculture15141541 - 17 Jul 2025

Abstract

Cost-effective methods for improving soil fertility and mitigating the negative impact of heavy metal contamination in agricultural soils are currently under investigation. This study aimed to evaluate the impact of soil lead (Pb) contamination and the application of secondary pulp and paper mill [...] Read more.

Cost-effective methods for improving soil fertility and mitigating the negative impact of heavy metal contamination in agricultural soils are currently under investigation. This study aimed to evaluate the impact of soil lead (Pb) contamination and the application of secondary pulp and paper mill sludge on the relative growth rate (RGR) and its determinants, as well as the specific absorption rate (SAR) of nutrients of Lactuca sativa L. For the 46-day pot experiment, which was carried out in 2022 under controlled conditions at the Karelian Research Centre of RAS, sandy loam soil was used, to which Pb was added at rates of 0, 50, and 250 mg Pb(NO₃)₂ kg⁻¹. Secondary sludge was applied with each watering at concentrations of 0%, 20%, and 40%. RGR values varied significantly, primarily due to changes in net assimilation rate (NAR) rather than specific leaf area. Positive relationships were found between RGR and NAR, and RGR and SAR of nitrogen and phosphorus, but not potassium. Sludge applications can stimulate NAR at early stages of plant growth. For plants grown on soil with the highest Pb concentration studied, secondary sludge reduced root lead content by an average of 35%. Soil contamination with lead increased nutrient SAR by 79 and 39% when applied as 20 and 40% sludge, respectively, while 40% sludge increased nitrogen SAR by 51% but did not change phosphorus and potassium SAR. A sludge-mediated reduction in root Pb content and an increase in NAR suggest that secondary paper sludge may contribute to the remediation of Pb-contaminated soils and reduce the toxicity of heavy metals to plants. The results may help in finding new ways to manage soil fertility, especially for contaminated soils. Full article

(This article belongs to the Section Agricultural Soils)

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18 pages, 4659 KiB

Open AccessArticle

Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil

by Yadong Li, Caiyun Lu, Hongwen Li, Jin He, Zhinan Wang and Chengkun Zhai

Agriculture 2025, 15(14), 1540; https://doi.org/10.3390/agriculture15141540 - 17 Jul 2025

Abstract

When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations [...] Read more.

When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations and analyze propagation characteristics. The effects of the compression ratio (0/6/12%), excitation frequency (20/40/60 kHz), and amplitude (5/10/15 μm) on signal variation and attenuation were analyzed. The results show consistent trends: time/frequency domain signal intensity increased with a higher compression ratio and amplitude but decreased with frequency. Comparing ultrasonic signals at soil particles before and after the seed along the propagation path shows that the seed significantly absorbs and attenuates ultrasonic waves. Time domain intensity drops 93.99%, and first and residual wave frequency peaks decrease by 88.06% and 96.39%, respectively. Additionally, comparing ultrasonic propagation velocities in the double media of seed–soil and the single soil medium reveals that the velocity in the seed is significantly higher than that in the soil. At compression ratios of 0%, 6%, and 12%, the sound velocity in the seed is 990.47%, 562.72%, and 431.34% of that in the soil, respectively. These findings help distinguish seed presence and provide a basis for ultrasonic seed position monitoring after sowing. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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16 pages, 1650 KiB

Open AccessArticle

Physico-Chemical and Resistance Characteristics of Rosehip Seeds

by Alina-Daiana Ionescu, Gheorghe Voicu, Elena-Madalina Stefan, Gabriel-Alexandru Constantin, Paula Tudor and Gheorghe Militaru

Agriculture 2025, 15(14), 1539; https://doi.org/10.3390/agriculture15141539 - 17 Jul 2025

Abstract

Both the pulp and the seeds of rosehip are important for human health. Rosehip seeds are rich in polyunsaturated fats, which support a healthy skin membrane and protect it from inflammatory factors. In order to be used, the seeds require initial processing, mainly [...] Read more.

Both the pulp and the seeds of rosehip are important for human health. Rosehip seeds are rich in polyunsaturated fats, which support a healthy skin membrane and protect it from inflammatory factors. In order to be used, the seeds require initial processing, mainly by grinding. This paper first presents a brief review of the physicochemical properties and the content of bioactive compounds in rosehip (Rosa canina) and its seeds. Original research results on the compression behavior of rosehip seeds are presented below, together with the key values of the most important parameters derived from the analysis. For seeds with a thickness ranging from 1.80 to 3.55 mm, the compressive force at the onset of fracture was recorded to be between 94.4 and 156.0 N, while the force required for complete fracture ranged from 114.0 to 495.0 N (with about 12.5% of values considered outside a normal distribution). Additionally, for these forces, the deformation of the seeds ranged between 0.142 and 0.916 mm at the onset of fracture and between 0.248 and 1.878 mm at complete fracture. For these characteristics, the energy consumed ranged between 0.012 and 0.041 J at the onset of fracture and between 0.017 and 0.322 J at complete breaking. The elasticity of the seeds also ranged between 159.9 and 789.1 N/mm, considering the forces and deformations at the onset of fracture. The results of our study contribute to expanding the database on the mechanical characteristics of rosehip seeds, knowledge of which is essential for the initial processing operations used in the pharmaceutical industry aimed at oil extraction. Full article

(This article belongs to the Section Seed Science and Technology)

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19 pages, 3309 KiB

Open AccessArticle

Harnessing Microbial Agents to Improve Soil Health and Rice Yield Under Straw Return in Rice–Wheat Agroecosystems

by Yangming Ma, Yanfang Wen, Ruhongji Liu, Zhenglan Peng, Guanzhou Luo, Cheng Wang, Zhonglin Wang, Zhiyuan Yang, Zongkui Chen, Jun Ma and Yongjian Sun

Agriculture 2025, 15(14), 1538; https://doi.org/10.3390/agriculture15141538 - 17 Jul 2025

Abstract

We clarified the effect of wheat straw return combined with microbial agents on rice yield and soil properties. A field experiment was conducted using hybrid indica rice ‘Chuankangyou 2115’ and five treatments: no wheat straw return (T₁), wheat straw [...] Read more.

We clarified the effect of wheat straw return combined with microbial agents on rice yield and soil properties. A field experiment was conducted using hybrid indica rice ‘Chuankangyou 2115’ and five treatments: no wheat straw return (T₁), wheat straw return alone (T₂), T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:1) (T₃); T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 3:1) (T₄); T₂+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:3) (T₅). T₂–T₅ significantly increased dry matter accumulation, soil total N, ammonium N, nitrate N, and organic matter, improving yield by 3.81–26.63%. T₃ exhibited the highest yield increases in two consecutive years. At the jointing and heading stages, Penicillium and Saitozyma dominated under T₃ and positively correlated with dry matter, yield, and nitrogen levels. Straw return combined with Bacillus subtilis and Trichoderma harzianum (20 g m⁻² each) enhanced soil nitrogen availability and dry matter accumulation and translocation. Our findings guide efficient straw utilization, soil microbial regulation, and sustainable high-yield rice production. Full article

(This article belongs to the Section Agricultural Soils)

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27 pages, 68526 KiB

Open AccessArticle

Design and Evaluation of a Novel Actuated End Effector for Selective Broccoli Harvesting in Dense Planting Conditions

by Zhiyu Zuo, Yue Xue, Sheng Gao, Shenghe Zhang, Qingqing Dai, Guoxin Ma and Hanping Mao

Agriculture 2025, 15(14), 1537; https://doi.org/10.3390/agriculture15141537 - 16 Jul 2025

Abstract

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an [...] Read more.

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

(This article belongs to the Topic New Research on Automated and Efficient Agricultural Machineries)

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26 pages, 6624 KiB

Open AccessArticle

Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge

by Shuntaro Aotake, Takuya Otani, Masatoshi Funabashi and Atsuo Takanishi

Agriculture 2025, 15(14), 1536; https://doi.org/10.3390/agriculture15141536 - 16 Jul 2025

Abstract

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. [...] Read more.

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

(This article belongs to the Special Issue Research Progress on Agricultural Equipments for Precision Planting and Harvesting)

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18 pages, 2521 KiB

Open AccessArticle

Transcriptomics and Metabolomics Reveal the Dwarfing Mechanism of Pepper Plants Under Ultraviolet Radiation

by Zejin Zhang, Zhengnan Yan, Xiangyu Ding, Haoxu Shen, Qi Liu, Jinxiu Song, Ying Liang, Na Lu and Li Tang

Agriculture 2025, 15(14), 1535; https://doi.org/10.3390/agriculture15141535 - 16 Jul 2025

Abstract

As a globally significant economic crop, pepper (Capsicum annuum L.) plants display excessive plant height (etiolation) in greenhouse production under an undesirable environment, leading to lodging-prone plants with reduced stress resistance. In the present study, we provided supplementary ultraviolet-B (UV-B, 280–315 nm) [...] Read more.

As a globally significant economic crop, pepper (Capsicum annuum L.) plants display excessive plant height (etiolation) in greenhouse production under an undesirable environment, leading to lodging-prone plants with reduced stress resistance. In the present study, we provided supplementary ultraviolet-B (UV-B, 280–315 nm) light to pepper plants grown in a greenhouse to assess the influences of UV-B on pepper growth, with an emphasis on the molecular mechanisms mediated through the gibberellin (GA) signaling pathway. The results indicated that UV-B significantly decreased the plant height and the fresh weight of pepper plants. However, no significant differences were observed in the chlorophyll content of pepper plants grown under natural light and supplementary UV-B radiation. The results of the transcriptomic and metabolomic analyses indicated that differentially expressed genes (DEGs) were significantly enriched in plant hormone signal transduction and that UV radiation altered the gibberellin synthesis pathway of pepper plants. Specifically, the GA3 content of the pepper plants grown with UV-B radiation decreased by 39.1% compared with those grown without supplementary UV-B radiation; however, the opposite trend was observed in GA34, GA7, and GA51 contents. In conclusion, UV-B exposure significantly reduced plant height, a phenotypic response mechanistically linked to an alteration in GA homeostasis, which may be caused by a decrease in GA3 content. Our study elucidated the interplay between UV-B and gibberellin biosynthesis in pepper morphogenesis, offering a theoretical rationale for developing UV-B photoregulation technologies as alternatives to chemical growth inhibitors. Full article

(This article belongs to the Special Issue The Effects of LED Lighting on Crop Growth, Quality, and Yield)

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32 pages, 6589 KiB

Open AccessArticle

Machine Learning (AutoML)-Driven Wheat Yield Prediction for European Varieties: Enhanced Accuracy Using Multispectral UAV Data

by Krstan Kešelj, Zoran Stamenković, Marko Kostić, Vladimir Aćin, Dragana Tekić, Tihomir Novaković, Mladen Ivanišević, Aleksandar Ivezić and Nenad Magazin

Agriculture 2025, 15(14), 1534; https://doi.org/10.3390/agriculture15141534 - 16 Jul 2025

Abstract

Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) [...] Read more.

Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) and Red-Green-Blue (RGB) imagery. Research analyzes five European wheat cultivars across 400 experimental plots created by combining 20 nitrogen, phosphorus, and potassium (NPK) fertilizer treatments. Yield variations from 1.41 to 6.42 t/ha strengthen model robustness with diverse data. The ML approach is automated using PyCaret, which optimized and evaluated 25 regression models based on 65 vegetation indices and yield data, resulting in 66 feature variables across 400 observations. The dataset, split into training (70%) and testing sets (30%), was used to predict yields at three growth stages: 9 May, 20 May, and 6 June 2022. Key models achieved high accuracy, with the Support Vector Regression (SVR) model reaching R² = 0.95 on 9 May and R² = 0.91 on 6 June, and the Multi-Layer Perceptron (MLP) Regressor attaining R² = 0.94 on 20 May. The findings underscore the effectiveness of precisely measured MS indices and a rigorous experimental approach in achieving high-accuracy yield predictions. This study demonstrates how a precise experimental setup, large-scale field data, and AutoML can harness UAV and machine learning’s potential to enhance wheat yield predictions. The main limitations of this study lie in its focus on experimental fields under specific conditions; future research could explore adaptability to diverse environments and wheat varieties for broader applicability. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)

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23 pages, 9811 KiB

Open AccessArticle

Is the Cultivation of Dictyophora indusiata with Grass-Based Substrates an Efficacious and Sustainable Approach for Enhancing the Understory Soil Environment?

by Jing Li, Fengju Jiang, Xiaoyue Di, Qi Lai, Dongwei Feng, Yi Zeng, Yufang Lei, Yijia Yin, Biaosheng Lin, Xiuling He, Penghu Liu, Zhanxi Lin, Xiongjie Lin and Dongmei Lin

Agriculture 2025, 15(14), 1533; https://doi.org/10.3390/agriculture15141533 - 16 Jul 2025

Abstract

The integration of forestry and agriculture has promoted edible fungi cultivation in forest understory spaces. However, the impact of spent mushroom substrates on forest soils remains unclear. This study explored the use of seafood mushroom spent substrates (SMS) and grass substrates to cultivate [...] Read more.

The integration of forestry and agriculture has promoted edible fungi cultivation in forest understory spaces. However, the impact of spent mushroom substrates on forest soils remains unclear. This study explored the use of seafood mushroom spent substrates (SMS) and grass substrates to cultivate Dictyophora indusiata. After cultivation, soil pH stabilized, organic carbon increased by 34.02–62.24%, total nitrogen rose 1.1–1.9-fold, while soil catalase activity increased by 43.78–100.41% and laccase activity surged 3.3–11.2-fold. The 49% Cenchrus fungigraminus and 49% SMS treatment yielded the highest 4-coumaric acid levels in the soil, while all treatments reduced maslinic and pantothenic acid content. SMS as padding material with C. fungigraminus enhanced soil bacterial diversity in the first and following years. Environmental factors and organic acids influenced the recruitment of genus of Latescibacterota, Acidothermus, Rokubacteriales, Candidatus solibacter, and Bacillus, altering organic acid composition. In conclusion, cultivating D. indusiata understory enhanced environmental characteristics, microbial dynamics, and organic acid profiles in forests’ soil in short time. Full article

(This article belongs to the Special Issue Effects of Different Managements on Soil Quality and Crop Production)

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15 pages, 466 KiB

Open AccessArticle

Metagenomic Profiling of the Grapevine Virome in Canadian Vineyards

by Bhadra Murthy Vemulapati, Kankana Ghoshal, Sylvain Lerat, Wendy Mcfadden-Smith, Mamadou L. Fall, José Ramón Úrbez-Torres, Peter Moffet, Ian Boyes, James Phelan, Lucas Bennouna, Debra L. Moreau, Mike Rott and Sudarsana Poojari

Agriculture 2025, 15(14), 1532; https://doi.org/10.3390/agriculture15141532 - 16 Jul 2025

Abstract

A high-throughput sequencing-based grapevine metagenomic survey was conducted across all grape-growing Canadian provinces (British Columbia, Ontario, Nova Scotia, and Québec) with the objective of better understanding the grapevine virome composition. In total, 310 composite grapevine samples representing nine Vitis vinifera red; five V. [...] Read more.

A high-throughput sequencing-based grapevine metagenomic survey was conducted across all grape-growing Canadian provinces (British Columbia, Ontario, Nova Scotia, and Québec) with the objective of better understanding the grapevine virome composition. In total, 310 composite grapevine samples representing nine Vitis vinifera red; five V. vinifera white; seven American–French red; and five white hybrid cultivars were analyzed. dsRNA, enriched using two different methods, was used as the starting material and source of viral nucleic acids in HTS. The virome status on the distribution and incidence in different regions and grapevine cultivars is addressed. Results from this study revealed the presence of 20 viruses and 3 viroids in the samples tested. Twelve viruses, which are in the regulated viruses list under grapevine certification, were identified in this survey. The major viruses detected in this survey and their incidence rates are GRSPaV (26% to 100%), GLRaV-2 (1% to 18%), GLRaV-3 (15% to 63%), GRVFV (0% to 52%), GRGV (0% to 52%), GPGV (3.3% to 77%), GFkV (1.5% to 31.6%), and GRBV (0% to 19.4%). This survey is the first comprehensive virome study using viral dsRNA and a metagenomics approach on grapevine samples from the British Columbia, Ontario, Nova Scotia, and Quebec provinces in Canada. Results from this survey highlight the grapevine virome distribution across four major grapevine-growing regions and their cultivars. The outcome of this survey underlines the need for strengthening current management options to mitigate the impact of virus spread, and the implementation of a domestic grapevine clean plant program to improve the sanitary status of the grapevine ecosystem. Full article

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

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22 pages, 8891 KiB

Open AccessArticle

Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing

by Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang, Huanjun Liu, Lu Chen and Xiaomeng Zhu

Agriculture 2025, 15(14), 1531; https://doi.org/10.3390/agriculture15141531 - 15 Jul 2025

Abstract

Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang [...] Read more.

Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang County, Heihe City, Heilongjiang Province, in 2023. The soil available nitrogen content ranged from 65.81 to 387.10 mg kg⁻¹, with a mean value of 213.85 ± 61.16 mg kg⁻¹. Sentinel-2 images and normalized vegetation index (NDVI) and enhanced vegetation index (EVI) time series data were acquired on the Google Earth Engine (GEE) platform in the study area during the bare soil period (April, May, and October) and the growth period (June–September). These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. The research results indicate that (1) the introduction of growth information at different growth periods of soybean and maize has different effects on the accuracy of soil AN mapping. In soybean plantations, the introduction of EVI data during the pod setting period increased the mapping accuracy R² by 0.024–0.088 compared to other growth periods. In maize plantations, the introduction of EVI data during the grouting period increased R² by 0.004–0.033 compared to other growth periods, which is closely related to the nitrogen absorption intensity and spectral response characteristics during the reproductive growth period of crops. (2) Combining the crop types and their optimal period growth information could improve the mapping accuracy, compared with only using the bare soil period image (R² = 0.597)—the R² increased by 0.035, the root mean square error (RMSE) decreased by 0.504%, and the mapping accuracy of R² could be up to 0.632. (3) The mapping accuracy of the bare soil period image differed significantly among different months, with a higher mapping accuracy for the spring data than the fall, the R² value improved by 0.106 and 0.100 compared with that of the fall, and the month of April was the optimal window period of the bare soil period in the present study area. The study shows that when mapping the soil AN content in arable land, different crop types, data collection time, and crop growth differences should be considered comprehensively, and the combination of specific crop types and their optimal period growth information has a greater potential to improve the accuracy of mapping soil AN content. This method not only opens up a new technological path to improve the accuracy of remote sensing mapping of soil attributes but also lays a solid foundation for the research and development of precision agriculture and sustainability. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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26 pages, 6371 KiB

Open AccessArticle

Growth Stages Discrimination of Multi-Cultivar Navel Oranges Using the Fusion of Near-Infrared Hyperspectral Imaging and Machine Vision with Deep Learning

by Chunyan Zhao, Zhong Ren, Yue Li, Jia Zhang and Weinan Shi

Agriculture 2025, 15(14), 1530; https://doi.org/10.3390/agriculture15141530 - 15 Jul 2025

Abstract

To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep learning is employed. NIR reflectance spectra and hyperspectral and [...] Read more.

To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep learning is employed. NIR reflectance spectra and hyperspectral and RGB images for 740 Gannan navel oranges of five cultivars are collected. Based on preprocessed spectra, optimally selected hyperspectral images, and registered RGB images, a dual-branch multi-modal feature fusion convolutional neural network (CNN) model is established. In this model, a spectral branch is designed to extract spectral features reflecting internal compositional variations, while the image branch is utilized to extract external color and texture features from the integration of hyperspectral and RGB images. Finally, growth stages are determined via the fusion of features. To validate the availability of the proposed method, various machine-learning and deep-learning models are compared for single-modal and multi-modal data. The results demonstrate that multi-modal feature fusion of HSI and MV combined with the constructed dual-branch CNN deep-learning model yields excellent growth stage discrimination in navel oranges, achieving an accuracy, recall rate, precision, F1 score, and kappa coefficient on the testing set are 95.95%, 96.66%, 96.76%, 96.69%, and 0.9481, respectively, providing a prominent way to precisely monitor the growth stages of fruits. Full article

(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)

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