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Role of Intercropping, Herbicides and Fungicides in Compensating for the Lack of Crop Rotation in Long-Term Continuous Cropping of Two Potato Cultivars
Polyploidy Promotes Larger Mango Fruits with Cultivar-Specific Quality Changes
An Overview of Bacterial Canker in Stone Fruits Caused by Different Pseudomonads: Pseudomonas syringae Species Complex and Related Species

Journal Description

Agriculture

Agriculture is an international, peer-reviewed, open access journal published semimonthly online.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), GEOBASE, PubAg, AGRIS, RePEc, and other databases.
Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Plant Science)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.4 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the first half of 2026).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Agriculture include: Poultry, Grasses, Crops, AIPA and Grain Science.
Journal Cluster of Agricultural Science: Agriculture, Agronomy, Horticulturae, Soil Systems, AgriEngineering, Crops, Seeds, Grasses, Agrochemicals and AI and Precision Agriculture.

Impact Factor: 4.5 (2025); 5-Year Impact Factor: 4.6 (2025)

Imprint Information Journal Flyer Open Access ISSN: 2077-0472

Latest Articles

21 pages, 971 KB

Open AccessArticle

Digital Infrastructure and Agricultural Total Factor Productivity: A New Structural Economics Perspective on the “Broadband China” Policy

by Siyi Guo and Jiafeng Gu

Agriculture 2026, 16(17), 1813; https://doi.org/10.3390/agriculture16171813 (registering DOI) - 24 Aug 2026

This study investigates the impact of the “Broadband China” pilot policy on agricultural total factor productivity (TFP) from a new structural economics (NSE) perspective. Using panel data from 297 Chinese cities (2000–2024), we employ a spatial difference-in-differences (SDiD) approach. We find that the [...] Read more.

This study investigates the impact of the “Broadband China” pilot policy on agricultural total factor productivity (TFP) from a new structural economics (NSE) perspective. Using panel data from 297 Chinese cities (2000–2024), we employ a spatial difference-in-differences (SDiD) approach. We find that the policy significantly enhances agricultural TFP by approximately 25.8 percentage points in treated cities. We identify international internet users as a novel partial mediator, explaining why domestic broadband gains depend on international connectivity. We also provide evidence of positive spatial spillovers: a 1% increase in neighboring cities’ digital economy is associated with a 0.292% increase in local agricultural TFP. Heterogeneity analysis reveals stronger policy effects in less-developed regions (Northwest China, low-GDP areas) and areas with higher scientific expenditure. By integrating institutional and spatial structures into the NSE framework, this study contributes a nuanced, endowment-contingent understanding of how state-led digital infrastructure investment reshapes agricultural efficiency. Full article

(This article belongs to the Special Issue Competitiveness, Productivity, and Efficiency in the Agricultural Market)

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20 pages, 3866 KB

Open AccessArticle

Optimization Design and Experiment of a Pulling–Cutting–Clamping End-Effector for Hang Pepper Harvesting

by Xingxiao Ma, Mingjie Li, Hongxuan Liang, Jianneng Chen and Xiong Zhao

Agriculture 2026, 16(17), 1812; https://doi.org/10.3390/agriculture16171812 (registering DOI) - 24 Aug 2026

Existing studies on selective pepper harvesting commonly use the pedicel as the picking target. However, for Hang pepper, dense branches and leaves cause severe fruit stem occlusion, resulting in a low harvesting success rate. To address this limitation, an end-effector that uses the [...] Read more.

Existing studies on selective pepper harvesting commonly use the pedicel as the picking target. However, for Hang pepper, dense branches and leaves cause severe fruit stem occlusion, resulting in a low harvesting success rate. To address this limitation, an end-effector that uses the fruit body as the picking target was designed in this study. Physical property tests of Hang pepper were first conducted. In radial compression tests, a post-release deformation of less than 5% was used as the criterion for low-damage clamping. The maximum allowable clamping force was determined to be 3.5 N, under which the maximum fruit compression ratio was 10%. The maximum cutting force required to shear the pedicel was 15.03 N. Based on these results, a harvesting strategy consisting of fruit clamping, fruit pull-down, and pedicel cutting was proposed. A five-bar linkage was adopted as the main mechanism of the end-effector, and its kinematic and mechanical models were established. Taking the minimum driving torque as the optimization objective, a genetic algorithm was used to optimize the linkage dimensions. The optimized driving torque was 0.801 N m, and a calculation method relating the target fruit diameter to the servo rotation angle was established. Harvesting experiments under prescribed target diameters showed a harvesting success rate of 95%, and the single-fruit harvesting time was 5.1 s, demonstrating the excellent harvesting capability of the proposed end-effector. Full article

(This article belongs to the Section Agricultural Technology)

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16 pages, 3271 KB

Open AccessArticle

Stand Age Reshapes Belowground–Aboveground Coordination and Forage Production in Cultivated Leymus chinensis Grasslands

by Jiale Na, Tao Li, Ruozhuang Zhao, Zhaoxu Nie, Jian Zhang, Qi Luo, Ruiying Ma, Sitong Qu, Haishuang Liu and Kai Gao

Agriculture 2026, 16(17), 1811; https://doi.org/10.3390/agriculture16171811 (registering DOI) - 24 Aug 2026

Cultivated grasslands provide an important strategy for reducing pressure on degraded natural grasslands and increasing high-quality forage supply. However, how stand-age-related variation affects belowground–aboveground coordination and forage production in cultivated Leymus chinensis remains unclear. Here, we compared a natural L. chinensis stand (NL) [...] Read more.

Cultivated grasslands provide an important strategy for reducing pressure on degraded natural grasslands and increasing high-quality forage supply. However, how stand-age-related variation affects belowground–aboveground coordination and forage production in cultivated Leymus chinensis remains unclear. Here, we compared a natural L. chinensis stand (NL) with 3-, 4-, and 5-year-old cultivated ‘Zhongke No. 1’ stands by integrating plant trait measurements, biomass assessment, nutrient analysis, forage quality evaluation, correlation analysis, and structural equation modeling. (1) Compared with NL, cultivated stands generally showed greater vegetative growth and aboveground biomass, lower fiber concentrations, and higher soluble sugar concentration and relative feed value. (2) Root and aboveground traits exhibited distinct stand-age-related patterns, with the 3Y stand showing greater root exploration traits, the 4Y stand achieving the highest aboveground biomass (4.61 t/hm²), and the 5Y stand exhibiting greater root thickening and ramet density. (3) Carbon, nitrogen, and phosphorus concentrations varied among stand ages, indicating shifts in nutrient status during stand development. (4) Correlation analysis and PLS-SEM revealed that root architecture, nutrient status, and aboveground morphology were closely associated with forage yield and quality variation. These findings highlight the importance of stand-age-dependent belowground–aboveground coordination for optimizing the management and utilization of cultivated L. chinensis grasslands. Full article

(This article belongs to the Section Crop Production)

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16 pages, 3996 KB

Open AccessArticle

Computed Tomography-Based Assessment of Bone Morphometry and Mineral Concentrations in Broilers Fed Diets Supplemented with Probiotics and Phytogenic Additives

by Kameliya Zhelyazkova, Pavlina Hristakieva, Stayka Laleva, Lazarin Lazarov, Nikolay Ivanov, Ivelina Alexandrova, Ivan Slavov, Radina Vasileva and Magdalena Oblakova

Agriculture 2026, 16(17), 1810; https://doi.org/10.3390/agriculture16171810 - 24 Aug 2026

The present study was conducted to investigate the effect of dietary supplementation with the probiotic Zoovit LL—alone or in combination with a phytogenic blend—on bone morphometry and mineral concentrations in broilers by computed tomography (CT) and ICP-MS analysis. A total of 180 one-day-old [...] Read more.

The present study was conducted to investigate the effect of dietary supplementation with the probiotic Zoovit LL—alone or in combination with a phytogenic blend—on bone morphometry and mineral concentrations in broilers by computed tomography (CT) and ICP-MS analysis. A total of 180 one-day-old male ROSS 308 chicks were randomly assigned to three groups with three replicates of 20 chicks each: control (standard diet), Zoovit LL (0.25%) and Zoovit LL (0.25%) in combination with Silybum marianum, Urtica dioica and Taraxacum officinale in a total amount of 1%. After 42 days, two birds were averaged within each replicate, resulting in three independent experimental units per treatment (n = 3 per group). Femurs were examined by CT for bone length, cortical bone thickness, diameters and radiodensity, while determination of calcium, phosphorus and magnesium concentrations in the sternum was performed by ICP-MS. No statistically significant effects of the nutritional supplements were found on the bone parameters assessed by computed tomography or on the mineral concentrations in the sternum. The results show that under the conditions of the present study, the addition of the probiotic alone or in combination with the phytogenic mixture did not lead to statistically significant changes in the specific parameters studied. Full article

(This article belongs to the Special Issue Functional Nutrition Strategies and Feed Additives to Improve Health and Performance in Broiler and Rabbit Production)

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15 pages, 2637 KB

Open AccessArticle

Transcriptomic Analysis of the Uterine Mucosa Reveals a Gene Regulatory Network Associated with Reduced Eggshell Strength in Late-Laying Hens

by Hailu Fan, Lei Wang, Xin He, Siyu Wan, Bo Zhang and Hao Zhang

Agriculture 2026, 16(17), 1809; https://doi.org/10.3390/agriculture16171809 - 24 Aug 2026

The decline in eggshell strength (ESS) during the late laying period is an important issue that needs to be addressed in the laying hen industry. Given that eggshell formation depends heavily on uterine mucosa secretions, understanding the underlying transcriptomic changes is essential. To [...] Read more.

The decline in eggshell strength (ESS) during the late laying period is an important issue that needs to be addressed in the laying hen industry. Given that eggshell formation depends heavily on uterine mucosa secretions, understanding the underlying transcriptomic changes is essential. To investigate this, eggshell phenotypes were compared between the peak laying period (30 weeks of age, 30 W) and the late laying period (65 weeks of age, 65 W) of White Leghorn laying hens. Furthermore, RNA sequencing of the uterine mucosa was performed, and the sequencing data were quality-controlled, aligned to the chicken reference genome (GRCg7b), quantified using StringTie, and analysed for differential expression using DESeq2. WGCNA was subsequently performed to identify candidate genes associated with eggshell quality. The results showed that ESS decreased from 31.31 ± 3.34 N in the 30 W group to 24.65 ± 2.11 N in the 65 W group (p < 0.001). Transcriptomic analysis identified 903 differentially expressed genes (DEGs) in the uterine mucosa, and these DEGs were significantly enriched in pathways related to eggshell mineralisation. By integrating WGCNA, protein–protein interaction (PPI) network analysis, and trait correlation analysis, six candidate genes were identified: GNAS, BMPR2, EDN2, ANXA1, PLCB2, and CX3CL1. In conclusion, this study identifies new candidate genes and provides a theoretical basis for elucidating the molecular mechanisms underlying the decline in eggshell quality in late-laying hens. Full article

(This article belongs to the Section Farm Animal Production)

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41 pages, 1808 KB

Open AccessReview

Intelligent Agents for Smart Agriculture: Architectures, Applications, and Future Challenges

by Wenzheng Tao, Qiwei Sang, Cong Chen and Qirong Mao

Agriculture 2026, 16(17), 1808; https://doi.org/10.3390/agriculture16171808 - 23 Aug 2026

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural [...] Read more.

Intelligent agents are emerging as an important system-level paradigm for smart agriculture. This review focuses on modern agricultural intelligent agents driven by large language models and related multimodal foundation models and examines how this emerging field is reshaping the organization of intelligent agricultural systems. It first clarifies the conceptual boundaries of agricultural intelligent agents and distinguishes them from traditional multi-agent systems, agent-based modeling, agricultural foundation models, and static retrieval-augmented question-answering systems. It then synthesizes their architectural foundations, key capabilities, application scenarios, deployment challenges, and future research directions. The reviewed literature indicates that agricultural intelligent agents are moving beyond isolated perception, prediction, and response generation toward the goal-oriented coordination of agricultural knowledge, dynamic data, external tools, and decision-making processes across agricultural task chains. They are beginning to support more integrated forms of knowledge services, crop monitoring and diagnosis, decision support, and farm-level collaborative management. Nevertheless, their transition from prototype systems to dependable and deployable agricultural systems remains constrained by context-aware knowledge grounding, heterogeneous data and tool integration, long-horizon reliability, the stability of multi-agent collaboration, and system security. This review further introduces an assessment perspective based on evidence reported in the original studies, comparing representative agricultural intelligent agents in terms of task decomposition, agronomic evidence applicability, tool-use validity, workflow reliability, multi-agent coordination, and deployment-related evidence. By distinguishing demonstrated capabilities from unevaluated dimensions, this review provides a structured framework for understanding the current status of agricultural intelligent agents and for guiding their future development toward reliable, deployable, and domain-oriented intelligent systems for smart agriculture. Full article

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

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31 pages, 1595 KB

Open AccessReview

The Influence of Fusarium Infection and Associated Mycotoxin Contamination on the Technological Value and Chemical Composition of Wheat Grain

by Grażyna Podolska, Edyta Aleksandrowicz, Krzysztof Dziedzic and Anna Szafrańska

Agriculture 2026, 16(17), 1807; https://doi.org/10.3390/agriculture16171807 - 23 Aug 2026

Wheat is one of the world’s most important cereal crops, and its technological quality is essential for the production of flour, dough and bakery products. Fusarium infection and the associated accumulation of mycotoxins may adversely affect grain composition, processing performance and food safety. [...] Read more.

Wheat is one of the world’s most important cereal crops, and its technological quality is essential for the production of flour, dough and bakery products. Fusarium infection and the associated accumulation of mycotoxins may adversely affect grain composition, processing performance and food safety. This review summarizes current knowledge on the influence of Fusarium infection and associated mycotoxin contamination on the chemical composition and technological quality of wheat. A literature search was conducted in the Web of Science Core Collection, and eligible studies were included in the qualitative synthesis. The reviewed studies demonstrate that Fusarium infection generally reduces grain quality, gluten functionality, dough rheological properties and baking performance, although the magnitude and direction of changes depend on the Fusarium species, wheat cultivar, infection model and mycotoxin concentration. Considerable heterogeneity among experimental designs limits direct comparison of individual studies. By integrating evidence across grain, flour, dough and bread quality parameters, this review provides a comprehensive and comparative synthesis of the effects of different Fusarium species and associated mycotoxins on wheat technological quality and identifies major areas requiring further investigation. Full article

(This article belongs to the Special Issue Strategies to Improve the Security and Nutritional Quality of Crop Species—2nd Edition)

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30 pages, 8600 KB

Open AccessArticle

Impact of Biostimulation on Floricane Raspberries Assessed Using Drone-Based Remote Sensing

by Kamil Buczyński and Magdalena Kapłan

Agriculture 2026, 16(17), 1806; https://doi.org/10.3390/agriculture16171806 - 22 Aug 2026

The effects of biostimulants on raspberry remain insufficiently understood, particularly in relation to cultivar-specific responses and the potential of remote sensing for treatment evaluation. The aim of this study was to assess the impact of foliar-applied biostimulants on yield and physiological responses of [...] Read more.

The effects of biostimulants on raspberry remain insufficiently understood, particularly in relation to cultivar-specific responses and the potential of remote sensing for treatment evaluation. The aim of this study was to assess the impact of foliar-applied biostimulants on yield and physiological responses of two floricane raspberry cultivars, Glen Ample and Przehyba, using UAV-based multispectral imaging under field conditions. Five treatment variants were tested, including a control and four biostimulant formulations based on animal-derived amino acids, plant-derived amino acids, seaweed extract, and seaweed extract combined with animal-derived amino acids. Biostimulant application significantly affected yield and fruit number per plant, whereas fruit weight remained unchanged. The highest yield values per plant regardless of cultivar were associated with treatments based on plant-derived amino acids (2578.78 g) and seaweed-containing (2483.36–2544.49 g) formulations, while the lowest values were recorded in the control (2335.66 g) and after the application of animal-derived amino acids (2351.80 g). Multispectral analysis revealed treatment-dependent temporal changes in vegetation indices, with clearer trends emerging after aggregation of relative percentage changes across measurement intervals. These results indicate that biostimulant effectiveness in floricane raspberry is strongly dependent on cultivar, formulation type, and temporal context. UAV-based multispectral imaging proved to be a promising non-destructive tool for tracking physiological responses to biostimulation under field conditions. Full article

(This article belongs to the Special Issue Adapting Horticultural Plant Cultivation Technology and Storage to Changing Conditions)

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34 pages, 4671 KB

Open AccessArticle

Saliency-Guided RT-DETR for Multi-Class Detection in Processing Tomato Sorting

by Xingyu Jiang, Yingjie Zhang, Ximei Wei, Xia Peng and Weitao Chen

Agriculture 2026, 16(17), 1805; https://doi.org/10.3390/agriculture16171805 (registering DOI) - 22 Aug 2026

This study considers multi-class visual detection for processing tomato sorting under controlled laboratory conditions. In densely arranged images, occlusion and visual similarity can weaken the local boundary and texture cues needed to distinguish ripe tomatoes, unripe tomatoes, defective tomatoes, and soil clods. We [...] Read more.

This study considers multi-class visual detection for processing tomato sorting under controlled laboratory conditions. In densely arranged images, occlusion and visual similarity can weaken the local boundary and texture cues needed to distinguish ripe tomatoes, unripe tomatoes, defective tomatoes, and soil clods. We propose SG-RTDETR, an RT-DETRv2 adaptation that combines detail-preserving downsampling, saliency-guided token encoding with residual spatial refill, context-aware feature organization, and adaptive cross-scale fusion. A four-class dataset was constructed from 1000 multi-object images and 400 single-object images used for supplementary representation learning. Across three independent training runs under controlled laboratory evaluation, SG-RTDETR achieved

87.8 \pm 0.4

%

{mAP}_{50 : 95}

,

92.4 \pm 0.4

%

{mAP}_{50}

, and

95.5 \pm 0.3

%

{mAR}_{50 : 95}

(mean ± sample standard deviation). Relative to RT-DETRv2, the mean

{mAP}_{50 : 95}

increased by 3.4 percentage points, while total FLOPs remained at 61.17 G and forward-pass inference throughput decreased from 110.5 to 103.6 FPS. These results indicate an accuracy-oriented trade-off under the laboratory evaluation protocol; validation under realistic postharvest sorting conditions remains necessary. Full article

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

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24 pages, 13354 KB

Open AccessArticle

Effects of Superabsorbent Polymer Inclusion in Broiler Litter on Welfare, Litter Properties, Production Performance, and Meat Quality

by Arkadiusz Matuszewski, Damian Bień, Wiktoria Cieśla, Sławomir Jaworski, Agata Lange, Jakub Urban, Patrycja Kostusiak, Ewa Skibniewska and Arkadiusz Szpicer

Agriculture 2026, 16(17), 1804; https://doi.org/10.3390/agriculture16171804 - 22 Aug 2026

Broiler litter quality is an important determinant of animal welfare, environmental conditions, and production efficiency. This study evaluated the effects of incorporating a biodegradable superabsorbent polymer (SAP) into chopped straw (CS) litter on broiler welfare, production performance, litter characteristics, environmental conditions, and meat [...] Read more.

Broiler litter quality is an important determinant of animal welfare, environmental conditions, and production efficiency. This study evaluated the effects of incorporating a biodegradable superabsorbent polymer (SAP) into chopped straw (CS) litter on broiler welfare, production performance, litter characteristics, environmental conditions, and meat quality. A total of 1000 Ross 308 broiler chickens were randomly allocated to four treatments: a control group (CON) and three groups receiving SAP at 30, 60, or 100 g/m² in the pen (H30, H60, or H100). Birds were reared for 42 days under commercial conditions. Growth performance, welfare indicators, gaseous emissions, litter characteristics, slaughter traits, and breast muscle quality were assessed. SAP supplementation had no adverse effects on body weight, feed conversion ratio, mortality, slaughter performance, or meat quality. Although litter dry matter content was lower in the H60 and H100 due to the water-retention capacity, NH₃ concentration at the bird level was significantly reduced. SAP supplementation also improved selected animal welfare indicators, particularly by increasing the proportion of birds with score 0 of footpad dermatitis (FPD) from 46–59% to 69–79%. These findings indicate that biodegradable SAPs represent a promising litter amendment for improving broiler welfare and housing conditions without compromising production performance or meat quality. Full article

(This article belongs to the Special Issue Nutritional, Technological, and Environmental Strategies to Improve Health and Welfare in Broiler Chickens)

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15 pages, 616 KB

Open AccessArticle

Regional Economic Systems and Intellectual Property in Agriculture

by Julia Sergeevna Kolesnikova, Roman Vadimovich Kulagin, Askar Nailevich Mustafin, Ivana Kravčáková Vozárová and Rastislav Kotulič

Agriculture 2026, 16(17), 1803; https://doi.org/10.3390/agriculture16171803 - 22 Aug 2026

The aim of the study is to analyze primary statistics on the use of intellectual property in agriculture in modern Russia, as well as to identify the relationships between human capital, the supply of research labor, and the growth of intellectual capital in [...] Read more.

The aim of the study is to analyze primary statistics on the use of intellectual property in agriculture in modern Russia, as well as to identify the relationships between human capital, the supply of research labor, and the growth of intellectual capital in agriculture and its intensity relative to agricultural output. To conduct the study, a sample of 34 regions of the Russian Federation for the period from 2017 to 2024 was analyzed. By using count model estimation, it was demonstrated that both economic incentives and human capital have a significant impact on the use of breeding achievements in agriculture. However, this impact of economic incentives is valid only for the extensive expansion of the applied objects of intellectual property. The level of human capital in the region consistently stimulates the growth and intensity of innovation activity to the scale of the agricultural industry. At the same time, the increase in the cost of researcher labor has a negative effect on the number of breeding achievements used or their intensity in agricultural output. The authors hope that this paper will contribute to the literature examining complex, multifactorial influences on regional agricultural production. Full article

(This article belongs to the Special Issue Competitiveness, Productivity, and Efficiency in the Agricultural Market)

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26 pages, 3356 KB

Open AccessArticle

Can Agricultural Socialized Services Improve the Subjective Well-Being of Grain-Growing Farmers?—Micro-Evidence from Rice Growers in Southwest China

by Xunxiao Fan, Yifan Wang, Zhan Li, Jinaoze Li, Yang Yu and Yuying Liu

Agriculture 2026, 16(17), 1802; https://doi.org/10.3390/agriculture16171802 - 22 Aug 2026

Whether and how Agricultural Socialized Services (ASS) are associated with grain-growing smallholders’ general self-reported subjective well-being (SWB) remains unclear. Using 894 survey records collected in Sichuan Province, China, from 21 to 31 August 2023, we estimate ordered probit and instrumental variable two-stage least-squares [...] Read more.

Whether and how Agricultural Socialized Services (ASS) are associated with grain-growing smallholders’ general self-reported subjective well-being (SWB) remains unclear. Using 894 survey records collected in Sichuan Province, China, from 21 to 31 August 2023, we estimate ordered probit and instrumental variable two-stage least-squares models. ASS Adoption Breadth is positively associated with SWB. One additional service category is associated with a 2.73-percentage point increase in the probability of reporting the highest SWB category. The IV estimate remains positive and statistically significant (0.077, p < 0.05), conditional on the maintained instrument assumptions. ASS Adoption Breadth is also positively associated with Relative Income Comparison, Log Hired Labor Expenditure, and Plant-protection Drone Adoption. These measured behavioral indicators provide evidence consistent with the hypothesized channels of Perceived Relative Economic Gain, Market-based Labor Substitution, and technology-enabled reduction in direct occupational exposure. They do not directly measure physical workload, pesticide exposure, or health outcomes. The findings inform policies that expand smallholders’ access to agricultural services, but the mechanism interpretations remain provisional. Full article

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

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24 pages, 50628 KB

Open AccessArticle

Improved RT-DETR Model for Simultaneous Detection of Young Pear Fruits and Fruit Stalks in Natural Environments

by Tianzhao Jian, Xiuhua Zhang, Degang Kong, Yongwei Yuan, Shanshan Li and Huayu Liu

Agriculture 2026, 16(16), 1801; https://doi.org/10.3390/agriculture16161801 - 21 Aug 2026

Manual fruit thinning is labor-intensive and inefficient, making the development of intelligent visual detection systems a crucial approach for improving the quality and production efficiency of the pear industry. However, in natural orchard environments, young pear fruits are small in size with slender [...] Read more.

Manual fruit thinning is labor-intensive and inefficient, making the development of intelligent visual detection systems a crucial approach for improving the quality and production efficiency of the pear industry. However, in natural orchard environments, young pear fruits are small in size with slender fruit stalks, and their texture and color characteristics are highly similar to those of tender branches. Furthermore, variations in illumination and occlusions caused by branches and leaves make it difficult for existing detection models to simultaneously and accurately identify fruits and fruit stalks, limiting their application in automated thinning equipment. In this study, Yuluxiang pear was selected as the research object, and image data were collected under diverse field conditions, including forward-lighting, backlighting, close-range shooting, long-range shooting and fruit overlapping. A dedicated dataset containing 3057 images was established. Based on the RT-DETR-R18 network, a lightweight and high-precision fruit–stalk synchronous detection model was proposed. Specifically, the backbone network was reconstructed by integrating GCConv with C2f modules to enhance global feature extraction for slender fruit stalks. The bottleneck structure was optimized using GCConvC3 to reduce feature degradation under occlusion conditions, and an additional 4× down-sampling P2 detection head was introduced to improve the detection capability for small targets. To fully validate the model performance and stability, three types of experiments were conducted in this study: ablation experiments, repeated experiments with different random seeds, and comparative experiments. Ablation experiments verified the cumulative performance improvements brought by the introduced modules. Repeated experiments with different random seeds were performed to explore training randomness-induced performance fluctuations, and the results demonstrated that the proposed model maintains stable overall detection accuracy with minor metric fluctuations. Comparative experiments demonstrated that the proposed model achieved a compact parameter size of only 15.97 M, with a precision of 95.0% for young pear fruit detection and an mAP50 of 83.0% for fruit stalk detection, outperforming all comparative models in overall mAP50. The training convergence curves and Grad-CAM++ visualization results further confirmed the stable optimization process and enhanced feature attention capability of the proposed model. By achieving a favorable balance between detection accuracy and model lightweightness, this approach provides effective technical support for the development of intelligent fruit thinning equipment and vision-based systems for smart pear orchards. Full article

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

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41 pages, 6240 KB

Open AccessArticle

Metaheuristic Optimized Mamdani Fuzzy Inference System for Soil pH Prediction and pH-Based Soil Condition Assessment in Papaya Cultivation

by Carlos-David Echevarría-Lezcano, Juan García-Virgen, Noel García-Díaz, Leonel Soriano-Equigua, Arturo-Iván Jardines-González, Dewar Rico-Bautista, Ana-Claudia Ruiz-Tadeo, Jesús-Alberto Verduzco-Ramírez and Jose L. Alvarez-Flores

Agriculture 2026, 16(16), 1800; https://doi.org/10.3390/agriculture16161800 - 21 Aug 2026

Adequate soil quality is essential for ensuring the productivity and sustainability of agriculture, with soil pH being a key variable due to its influence on nutrient availability, microbial activity, and plant development. This study proposes a Mamdani fuzzy inference system (FIS) optimized through [...] Read more.

Adequate soil quality is essential for ensuring the productivity and sustainability of agriculture, with soil pH being a key variable due to its influence on nutrient availability, microbial activity, and plant development. This study proposes a Mamdani fuzzy inference system (FIS) optimized through metaheuristic algorithms for soil pH prediction in papaya (Carica papaya L.) cultivation, a crop highly sensitive to pH fluctuations within the rhizosphere. Soil temperature and soil moisture were used as independent variables, while the estimated soil pH constituted the dependent variable of the system. Three optimization techniques—genetic algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—were evaluated to optimize the membership functions and fuzzy rule base of the Mamdani FIS. Model performance was assessed through 30 independent runs using 1500 records collected from a commercial papaya plantation. Across the 30 independent runs, the GA-optimized model achieved the best overall predictive performance, with a Mean Absolute Error (MAE) of 0.3636 ± 0.0035, Mean Relative Error (MRE) of 0.0554 ± 0.0004, and mean coefficient of determination (r²) of 0.8699 ± 0.0048. The best observed GA values were an MAE of 0.3305, MRE of 0.0513, and r² of 0.8957. In addition, a web-based decision support platform and an automated Telegram alert system were developed for event-driven pH alert notification. The results confirm that GA-optimized fuzzy systems constitute an effective, interpretable, and practical tool for pH-based soil condition monitoring and agronomic decision support in precision agriculture. Full article

(This article belongs to the Special Issue Soil Nutrients and Quality Assessment in Farmland)

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26 pages, 2480 KB

Open AccessSystematic Review

Leveraging Machine Learning to Understand Climate and Extreme Event Impacts on Crop Yields: A Systematic Review (2015–2025)

by Yanyan Ren, Dengpan Xiao, Yang Lu and Xiaoguang Li

Agriculture 2026, 16(16), 1799; https://doi.org/10.3390/agriculture16161799 - 21 Aug 2026

Quantifying the impacts of climate change and extreme climatic events on crop yields is essential for safeguarding global food security. The rapid growth of data availability and advances in computational capacity have established machine learning (ML) as a critical tool for unraveling the [...] Read more.

Quantifying the impacts of climate change and extreme climatic events on crop yields is essential for safeguarding global food security. The rapid growth of data availability and advances in computational capacity have established machine learning (ML) as a critical tool for unraveling the complex, nonlinear relationships between climatic factors and agricultural productivity. This systematic review synthesizes evidence from 137 peer-reviewed studies published between 2015 and 2025 that applied ML models to assess the effects of both long-term climate trends and discrete extreme events on crop yields worldwide. Bibliometric and thematic analyses reveal a rapidly evolving field, with over 85% of studies published since 2020, and a strong concentration on staple cereals—wheat, maize, and rice—in major agricultural regions including China, the United States, and India. Random Forest (RF) was the most commonly used algorithm; ensemble and deep-learning models achieved high predictive accuracy within well-resourced study contexts. Temperature and precipitation extremes emerged as the most frequently examined stressors, with distinct methodological patterns: studies focusing on climate change trends predominantly employed RF and LSTM models, whereas those investigating extreme events increasingly adopted hybrid approaches that integrate ML with process-based crop models. This review highlights the transformative potential of ML while identifying persistent challenges, such as geographical imbalances in research coverage, the need for enhanced interpretability in extreme event attribution, and the critical importance of modeling compound extremes. Future research should prioritize the development of explainable, causally informed, and transferable ML frameworks to support equitable climate adaptation strategies in global agriculture. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

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40 pages, 5796 KB

Open AccessReview

Mechanically Mediated Enzymatic Saccharification of Lignocellulosic Biomass: From Fundamental Mechanisms to Process Intensification

by Bo Feng, Siyu Chen, Qianyi Shangguan, Yaxin Shi, Jiawei Wang, Qijian Niu, Xiuxiu Dong and Guanya Ji

Agriculture 2026, 16(16), 1798; https://doi.org/10.3390/agriculture16161798 - 21 Aug 2026

Mechanical force offers a distinctive nonequilibrium mode of energy input for lignocellulosic biomass valorization through localized, transient action. This review systematically examines the multiscale physicochemical effects of mechanical force, its synergistic coupling with chemical pretreatments, and its role in enhancing enzymatic hydrolysis. The [...] Read more.

Mechanical force offers a distinctive nonequilibrium mode of energy input for lignocellulosic biomass valorization through localized, transient action. This review systematically examines the multiscale physicochemical effects of mechanical force, its synergistic coupling with chemical pretreatments, and its role in enhancing enzymatic hydrolysis. The principal contribution of mechanical force is not merely particle-size reduction, but the exposure of active sites and improvement in substrate accessibility at the molecular level. Coupling mechanical force with chemical pretreatment enables the efficient component fractionation under mild conditions while mitigating irreversible lignin condensation. In high-solids enzymatic hydrolysis, a periodic mechanical energy input can tear fiber bundles, release constrained water, and renew reaction interfaces, thereby allowing enzymes to sustain a high catalytic efficiency at extremely low liquid-to-solid ratios and reducing the dependence on large amounts of free water. An economic analysis indicates that feedstock and enzyme costs dominate the overall process economics. Accordingly, mechanical-force strategies should prioritize the maximized sugar yield and reduced enzyme loading under a controlled energy input. Future research should focus on continuous operation, the balance between mechanical deconstruction and lignin structural integrity, and multidimensional evaluation frameworks that integrate the energy consumption, sugar yield, enzyme dosage, and full-process energy balance. Full article

(This article belongs to the Special Issue Agro-Food Waste Valorization: Sustainable Pathways for Agricultural Applications)

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23 pages, 354 KB

Open AccessArticle

How Do Risk Attitudes Affect Fertilizer Input by Farm Households? Evidence from Maize Growers in Gansu, China

by Hao Li, Tongzheng Ye and Wei-Yew Chang

Agriculture 2026, 16(16), 1797; https://doi.org/10.3390/agriculture16161797 - 21 Aug 2026

Reducing fertilizer usage from the perspective of farm household behavior is important for mitigating agricultural environmental pollution. However, existing studies often treat farmers as independent decision-makers in a risk-free setting and overlook the role of risk, particularly subjective risk, in fertilizer application decisions. [...] Read more.

Reducing fertilizer usage from the perspective of farm household behavior is important for mitigating agricultural environmental pollution. However, existing studies often treat farmers as independent decision-makers in a risk-free setting and overlook the role of risk, particularly subjective risk, in fertilizer application decisions. Drawing on Arrow–Pratt’s theory of risk aversion, this paper develops a decision-making framework for fertilizer input under risk, examines the effect of risk attitudes on fertilizer input and its underlying mechanisms, and further investigates the heterogeneous impacts of risk attitude across different orientations of farmers’ economic benefit pursuits and intergenerational cohorts. The results indicate that: (1) more risk-averse farmers apply more fertilizer, and this finding remains robust after addressing endogeneity and conducting several robustness checks; (2) mediation analysis results indicate that both expected returns and path dependence have significant indirect effects in the association between risk attitude and farmers’ chemical fertilizer input; and (3) the positive effect of risk attitude on fertilizer input is more pronounced among farmers with a short-term orientation in their economic benefit pursuits and among the older-generation farmer groups. These findings offer a new theoretical perspective for understanding farmers’ fertilizer input decisions under risk and provide theoretical and practical implications for designing more effective fertilizer reduction policies that account for heterogeneous economic benefit orientations and intergenerational differences among farmers. Full article

(This article belongs to the Special Issue Farmer Behavior and Sustainable Agricultural Management)

27 pages, 14361 KB

Open AccessArticle

Dual-Sided Green Coffee Bean Defect Inspection Using a Mechatronic System with AI-Powered Computer Vision

by Oscar Sandoval-Gonzalez, Dora Manrique-Santos, Diego Cruz-Jarquin, Otniel Portillo-Rodriguez, Blanca Gonzalez-Sanchez, Ofelia Landeta-Escamilla and Gerardo Aguila-Rodriguez

Agriculture 2026, 16(16), 1796; https://doi.org/10.3390/agriculture16161796 - 21 Aug 2026

Quality control in green coffee bean production is critical for food safety and economic sustainability. A persistent gap in existing automated inspection systems is their inability to capture both sides of each bean, which leads to systematic under-detection of surface defects. This work [...] Read more.

Quality control in green coffee bean production is critical for food safety and economic sustainability. A persistent gap in existing automated inspection systems is their inability to capture both sides of each bean, which leads to systematic under-detection of surface defects. This work presents three contributions: (i) a novel mechatronic apparatus that mechanically guarantees dual-sided imaging of every bean, (ii) a public 12-class dataset of green coffee bean defects, and (iii) an embedded, real-time inspection pipeline validated on low-cost hardware. The apparatus sequentially presents each bean, from a standard 350 g sample, to two 16-megapixel cameras under controlled LED illumination. A dataset of 9600 images spanning 12 classes (11 defects and 1 normal) was generated from expert-classified samples and enriched through data augmentation. Four convolutional neural network (CNN) architectures, VGG-16, VGG-19, ResNet-50 and YOLOv8, were trained and benchmarked using precision, recall, F1-score and mean average precision. YOLOv8 achieved the best overall performance, with a precision of 97.4%, a recall of 99.6%, an F1-score of 0.930 and a mean average precision of 96.5%, outperforming VGG-16 (accuracy 86.07%), VGG-19 (accuracy 67.03%) and ResNet-50 (accuracy 87.76%). Dual-sided acquisition raised mean per-class detection accuracy from 0.727 to 0.908, a relative gain of 25.7% over an equivalent single-sided configuration. Deployed in real-time “track” mode on a Raspberry Pi 4, the system simultaneously classifies defects and counts beans by category, processing a 350 g sample in approximately 38 min. Combining mechanical innovation with lightweight deep learning enables practical, scalable, and cost-effective quality control for laboratories specialized in coffee analysis. Full article

(This article belongs to the Special Issue Nondestructive Quality Evaluation of Agricultural Products)

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26 pages, 24684 KB

Open AccessArticle

Climate and Cropland Jointly Shape Future Habitat Suitability of Major Stored-Product Callosobruchus Pests

by Rasha K. Al-Akeel, Mustafa M. Soliman, Abeer M. Alkhaibari, Amr Mohamed, Ioannis Eleftherianos, Iftekhar Rasool, Mahmoud S. Abdel-Dayem and Hathal M. Al Dhafer

Agriculture 2026, 16(16), 1795; https://doi.org/10.3390/agriculture16161795 - 21 Aug 2026

Stored-product insects threaten global food security, yet the environmental mechanisms governing their responses to climate change remain poorly understood. Existing pest distribution projections rarely integrate diurnal thermal variability with agricultural land use. Here, we show that diurnal thermal variability, together with agricultural land [...] Read more.

Stored-product insects threaten global food security, yet the environmental mechanisms governing their responses to climate change remain poorly understood. Existing pest distribution projections rarely integrate diurnal thermal variability with agricultural land use. Here, we show that diurnal thermal variability, together with agricultural land use, is a major determinant of habitat suitability for three globally important Callosobruchus pests across the Middle East. Using optimized species distribution models integrating climate, topography, and cropland under contrasting CMIP6 climate scenarios, we demonstrate that mean diurnal temperature range and cropland consistently emerge as the strongest predictors across all species, revealing the importance of daily thermal fluctuations beyond mean warming alone. Under the low-emission scenario (SSP1-2.6), suitable habitat by mid-century expands substantially for C. chinensis and C. phaseoli, while remaining little changed overall for C. maculatus, for which comparable local expansion and contraction largely offset one another; under the high-emission scenario (SSP5-8.5), gains are reduced, and localized contractions occur, particularly for C. chinensis and C. maculatus, the latter shifting to a slight net loss in total suitable area. Persistent climatic refugia remain along Mediterranean and Red Sea coastal regions, whereas habitat losses are concentrated in the northern Gulf lowlands and Zagros foothills. Our findings identify diurnal thermal variability as an overlooked dimension of stored-product pest ecology and show that integrating agricultural landscapes with climate projections can improve forecasts of future pest risk, providing a framework for climate-informed surveillance, biosecurity, and adaptation. Full article

(This article belongs to the Special Issue Diversity and Ecological Roles of Arthropods in Agricultural Systems)

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35 pages, 3786 KB

Open AccessArticle

Associations Between Spatial Crop Distribution Reconfiguration and Lake Nitrogen and Phosphorus Concentrations in China

by Jing Wan, Zhen Liu, Yazhu Wang, Huixian Wan, Jun He, Yihang Wang, Liyuan Huang and Lin Li

Agriculture 2026, 16(16), 1794; https://doi.org/10.3390/agriculture16161794 - 21 Aug 2026

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for [...] Read more.

Agricultural nonpoint source pollution mainly causes lake eutrophication in China, largely affected by variations in crop distribution. To analyze the multiscale relationships between the long-term evolution of cropping patterns and lake water quality at the macro scale, this study analyzed nationwide datasets for 2000 and 2020 covering 420 relatively large lakes. We systematically examined the spatial restructuring of six major food and cash crops—wheat, rice, maize, soybean, peanut, and rapeseed—and evaluated their multiscale associations with lake total nitrogen (TN) and total phosphorus (TP) concentrations and how these associations changed over time. The results showed the following: (1) From 2000 to 2020, the spatial distributions of the six major crops underwent substantial restructuring. The dominant production areas of rice, wheat, and maize were maintained or further reinforced, whereas soybean, rapeseed, and peanut exhibited varying degrees of regional redistribution and localized concentration. (2) Lake water quality differed between the flood and non-flood seasons. TN exhibited pronounced seasonal differences between the two study periods, whereas temporal changes in TP were generally limited; both nutrients nevertheless showed marked regional heterogeneity among the five major lake regions. (3) The crop–water quality relationship exhibits significant scale dependence and crop-specific variations. The XGBoost model demonstrated a certain degree of out-of-field (OOF) predictive capability for both TN and TP, with OOF R² values of 0.448 and 0.447, respectively. For TN, the highest OOF R² values were observed in the 1000–2000 m buffer zone in both 2000 and 2020; the optimal prediction scale for TP shifted from 1000–2000 m in 2000 to 2000–5000 m in 2020. SHAP results showed that corn maintained a high and relatively stable predictive importance in the TN model, followed by wheat, peanuts, and rice; in the TP model, corn and rapeseed were the crop predictors with the highest relative SHAP importance. PDP results further indicate that there are generally nonlinear or non-monotonic relationships between different crop coverage proportions and TN and TP. (4) Pronounced spatial heterogeneity was observed across the five lake regions. The Eastern Plain Lake Region was characterized by associations involving multiple crops, whereas maize was the most prominent crop in the Northeast Plain and Mountain Lake Region. In the Inner Mongolia–Xinjiang Plateau Lake Region, maize predominated, with wheat and rapeseed also showing notable importance. In the Tibetan Plateau Lake Region, TN was associated with multiple crops, whereas TP was primarily related to maize and rapeseed. The Yunnan–Guizhou Plateau Lake Region exhibited particularly strong scale-dependent differences. This study provides a nationwide analytical framework for comparing the scale differences and regional variations in the statistical associations between the spatial distribution of crops and lake water quality at the specific crop level. The findings can provide a scientific basis for formulating differentiated agricultural nonpoint source pollution control strategies that are adapted to the evolving characteristics of crop planting structures. Full article

(This article belongs to the Section Agricultural Water Management)

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