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Agriculture
Volume 15
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Agriculture, Volume 15, Issue 15 (August-1 2025) – 146 articles

Cover Story (view full-size image): How do young farmer apprentices learn how to farm and take care of animals? How can their periods of on-farm practice be shaped to best support their education and future as farmers? A pilot study based on interviews of 24 farmer students and 10 host farmers suggested how farmer apprentices’ learning during their on-farm apprentice period was influenced by different views and interests. We discovered a potential conflict between the view of the apprentice primarily as a learner, versus primarily as a farm labourer learning through practical work. Thirdly, the on-farm and social environment was suggested as very important for the development of future farmers. View this paper
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24 pages, 8829 KB  
Article
Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model
by Lijun Gao, Tiantian Ran, Hua Zou and Huanhuan Wu
Agriculture 2025, 15(15), 1712; https://doi.org/10.3390/agriculture15151712 - 7 Aug 2025
Viewed by 1089
Abstract
Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for [...] Read more.
Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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22 pages, 9279 KB  
Article
ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments
by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao and Wenfeng Li
Agriculture 2025, 15(15), 1711; https://doi.org/10.3390/agriculture15151711 - 7 Aug 2025
Cited by 1 | Viewed by 896
Abstract
With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further [...] Read more.
With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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14 pages, 706 KB  
Article
Study on the Effects of Irrigation Amount on Spring Maize Yield and Water Use Efficiency Under Different Planting Patterns in Xinjiang
by Ruxiao Bai, Haixiu He, Xinjiang Zhang and Qifeng Wu
Agriculture 2025, 15(15), 1710; https://doi.org/10.3390/agriculture15151710 - 7 Aug 2025
Viewed by 687
Abstract
Planting patterns and irrigation amounts are key factors affecting maize yield. This study adopted a two-factor experimental design, with planting pattern as the main plot and irrigation amount as the subplot, to investigate the effects of irrigation levels under different planting patterns (including [...] Read more.
Planting patterns and irrigation amounts are key factors affecting maize yield. This study adopted a two-factor experimental design, with planting pattern as the main plot and irrigation amount as the subplot, to investigate the effects of irrigation levels under different planting patterns (including uniform row spacing and alternating wide-narrow row spacing) on spring maize yield and water use efficiency in Xinjiang. Through this approach, the study examined the mechanisms by which planting pattern and irrigation amount influence maize growth, yield formation, and water use efficiency. Experiments conducted at the Agricultural Science Research Institute of the Ninth Division of Xinjiang Production and Construction Corps demonstrated that alternating wide-narrow row spacing combined with moderate irrigation (5400 m3/hm2) significantly optimized maize root distribution, improved water use efficiency, and increased leaf area index and net photosynthetic rate, thereby promoting dry matter accumulation and yield enhancement. In contrast, uniform row spacing under high irrigation levels increased yield but resulted in lower water use efficiency. The study also found that alternating wide-narrow row spacing enhanced maize nutrient absorption from the soil, particularly phosphorus utilization efficiency, by improving canopy structure and root expansion. This pattern exhibited comprehensive advantages in resource utilization, providing a theoretical basis and technical pathway for achieving water-saving and high-yield maize production in arid regions, which holds significant importance for promoting sustainable agricultural development. Full article
(This article belongs to the Section Crop Production)
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16 pages, 387 KB  
Article
Effects of Increasing Dietary Inclusion of White Lupin on Growth Performance, Meat Quality, and Fatty Acid Profile on Growing-Fattening Pigs
by Georgeta Ciurescu, Mihaela Dumitru, Nicoleta Aurelia Lefter and Dan-Traian Râmbu
Agriculture 2025, 15(15), 1709; https://doi.org/10.3390/agriculture15151709 - 7 Aug 2025
Viewed by 568
Abstract
This study investigated the possibility of partial replacement of genetically modified soybean meal (SBM) with raw white lupin (WL) seeds in growing pigs’ diets and determined its impact on performance [body weight (BW), average daily gain (ADG), and average daily feed intake (ADFI)], [...] Read more.
This study investigated the possibility of partial replacement of genetically modified soybean meal (SBM) with raw white lupin (WL) seeds in growing pigs’ diets and determined its impact on performance [body weight (BW), average daily gain (ADG), and average daily feed intake (ADFI)], meat quality, and fatty acid profile (FA). A total of 54 male crossbred pigs [(Topigs Large White × Norsvin Landrace) × Duroc], aged 12 weeks, with an initial average BW of 30.30 ± 0.77 kg, were divided into three dietary groups of 18 piglets each. The control group (CON) was fed a standardized SBM-based complete feed. In the experimental groups (WL1 and WL2) the SBM was replaced with increasing levels of WL seeds [WL1-5.0% and WL2-10.0% (grower period, 30–60 kg BW), and WL1-7.0% and WL2-14.0% (finisher period, 61–110 kg BW)]. All diets were formulated to be isocaloric and isonitrogenous with similar content of total lysine and sulphur amino acids, calcium, and available phosphorus. At the end of 83 days’ fattening trial, the animals were slaughtered. Longissimus dorsi muscle (LD) was sampled for analyses of the physicochemical traits. The results show that increasing the dietary raw WL concentration decreased final BW (p = 0.039), ADG (p < 0.0001), and ADFI (p = 0.004) throughout the experimental period, especially in the second phase of feeding. Dietary treatments did not affect the pigs’ blood biochemical constituents. Concerning LD muscle characteristics, the redness color (a*) and collagen content was higher (p < 0.0001) in the WL1/WL2 vs. CON group. Beneficial decrease in the values of some textural attributes (hardness, gumminess, chewiness, and resilience) of LD in the WL1/WL2 vs. CON group was registered. The use of WL had a significant effect on the content of FAs, especially for eicosapentaenoic (p = 0.014) and n-3 PUFA (p = 0.045), which were higher than those fed the CON diet. In conclusion, WL could be used as a replacement of SBM in growing–finishing pigs’ diets, with significant improvements in the meat fatty acid profile and technological properties. Full article
(This article belongs to the Topic The New Era of Food and Agriculture: Sustainable Strategies to Achieve Zero Hunger)
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21 pages, 610 KB  
Article
The Effectiveness of Subsidizing Investments in Polish Agriculture: A Propensity Score Matching Approach
by Cezary Klimkowski
Agriculture 2025, 15(15), 1708; https://doi.org/10.3390/agriculture15151708 - 7 Aug 2025
Viewed by 620
Abstract
Evaluation of the effectiveness of state policy instruments is a permanent element of economic science. This paper addresses the issue of investment support under the Common Agricultural Policy (CAP). Using data on Polish farms from 2015–2023, a Propensity Score Matching–Difference in Differences (PSM-DiD) [...] Read more.
Evaluation of the effectiveness of state policy instruments is a permanent element of economic science. This paper addresses the issue of investment support under the Common Agricultural Policy (CAP). Using data on Polish farms from 2015–2023, a Propensity Score Matching–Difference in Differences (PSM-DiD) analysis was conducted to assess changes in the economic results of agricultural producers that invest using this support. The comparison of the economic results achieved by supported investors with both non-investing agricultural producers and unsupported investors is a distinguishing element of this study. The relatively rarely used Competitivness Index (CI), which measures the ratio of earned income to the sum of the alternative use of the owned means of production, was used. The positive change in the CI during the analyzed period was 0.14 higher for supported investors than non-investors. No statistically significant change was found were compared to unsupported investors. A clear increase in income, total fixed assets, liabilities, and the level of production in the population of producers using support in relation to non-investors and investing without CAP support was also observed. However, in relationships with investors using their own funds, these differences were mainly due to the difference in the level of investments and were not statistically significant when introducing a correction regarding the scale of the investment. The obtained results remain in line with the results of research shown by a significant part of economists undertaking a similar issue. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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16 pages, 2990 KB  
Article
Walnut Surface Defect Classification and Detection Model Based on Enhanced YOLO11n
by Xinyi Ma, Zhongjia Hao, Shuangyin Liu and Jingbin Li
Agriculture 2025, 15(15), 1707; https://doi.org/10.3390/agriculture15151707 - 7 Aug 2025
Cited by 1 | Viewed by 663
Abstract
Aiming at the challenges in practical production lines, including the difficulty in accurately capturing external defects on continuously rolling walnuts, distinguishing subtle defects, and differentiating narrow fissures from natural walnut textures, this paper proposes an improved walnut external defect detection model named YOLO11-GME, [...] Read more.
Aiming at the challenges in practical production lines, including the difficulty in accurately capturing external defects on continuously rolling walnuts, distinguishing subtle defects, and differentiating narrow fissures from natural walnut textures, this paper proposes an improved walnut external defect detection model named YOLO11-GME, based on YOLO11n. Firstly, the original backbone network is replaced with the lightweight GhostNetV1 network, enhancing model precision while meeting real-time detection speed requirements. Secondly, a Mixed Local Channel Attention (MLCA) mechanism is incorporated into the neck to strengthen the network’s ability to capture features of subtle defects, thereby improving defect recognition accuracy. Finally, the EIoU loss function is adopted to enhance the model’s localization capability for irregularly shaped defects and reduce false detection rates by improving the scale sensitivity of bounding box regression. Experimental results demonstrate that the improved YOLO11-GME model achieves a mean Average Precision (mAP) of 96.2%, representing improvements of 8.6%, 7%, and 5.8% compared to YOLOv5n, YOLOv8n, and YOLOv10n, respectively, and a 5.9% improvement over the original YOLOv11. Precision rates for the normal, fissure, and inferior categories increased by 8.7%, 5.3%, and 3.7%, respectively. The frame rate remains at 43.92 FPS, approaching the original model’s 51.02 FPS. These results validate that the YOLO11-GME model enhances walnut external defect detection accuracy while maintaining real-time detection speed, providing robust technical support for defect detection and classification in industrial walnut production. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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24 pages, 5248 KB  
Article
Design and Experiment of DEM-Based Layered Cutting–Throwing Perimeter Drainage Ditcher for Rapeseed Fields
by Xiaohu Jiang, Zijian Kang, Mingliang Wu, Zhihao Zhao, Zhuo Peng, Yiti Ouyang, Haifeng Luo and Wei Quan
Agriculture 2025, 15(15), 1706; https://doi.org/10.3390/agriculture15151706 - 7 Aug 2025
Viewed by 487
Abstract
To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss [...] Read more.
To address compacted soils with high power consumption and waterlogging risks in rice–rapeseed rotation areas of the Yangtze River, this study designed a ditching machine combining a stepped cutter head and trapezoidal cleaning blade, where the mechanical synergy between components minimizes energy loss during soil-cutting and -throwing processes. We mathematically modeled soil cutting–throwing dynamics and blade traction forces, integrating soil rheological properties to refine parameter interactions. Discrete Element Method (DEM) simulations and single-factor experiments analyzed impacts of the inner/outer blade widths, blade group distance, and blade opening on power consumption. Results indicated that increasing the inner/outer blade widths (200–300 mm) by expanding the direct cutting area significantly reduced the cutter torque by 32% and traction resistance by 48.6% from reduced soil-blockage drag; larger blade group distance (0–300 mm) initially decreased but later increased power consumption due to soil backflow interference, with peak efficiency at 200 mm spacing; the optimal blade opening (586 mm) minimized the soil accumulation-induced power loss, validated by DEM trajectory analysis showing continuous soil flow. Box–Behnken experiments and genetic algorithm optimization determined the optimal parameters: inner blade width: 200 mm; outer blade width: 300 mm; blade group distance: 200 mm; and blade opening: 586 mm, yielding a simulated power consumption of 27.07 kW. Field tests under typical 18.7% soil moisture conditions confirmed a <10% error between simulated and actual power consumption (28.73 kW), with a 17.3 ± 0.5% reduction versus controls. Stability coefficients for the ditch depth, top/bottom widths exceeded 90%, and the backfill rate was 4.5 ± 0.3%, ensuring effective drainage for rapeseed cultivation. This provides practical theoretical and technical support for efficient ditching equipment in rice–rapeseed rotations, enabling resource-saving design for clay loam soils. Full article
(This article belongs to the Section Agricultural Technology)
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22 pages, 4027 KB  
Article
Parameter Sensitivity Analysis and Irrigation Regime Optimization for Jujube Trees in Arid Regions Using the WOFOST Model
by Shihao Sun, Yingjie Ma, Pengrui Ai, Ming Hong and Zhenghu Ma
Agriculture 2025, 15(15), 1705; https://doi.org/10.3390/agriculture15151705 - 7 Aug 2025
Viewed by 589
Abstract
In arid regions, water scarcity and soil potassium destruction are major constraints on the sustainable development of the jujube industry. In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but [...] Read more.
In arid regions, water scarcity and soil potassium destruction are major constraints on the sustainable development of the jujube industry. In this regard, the use of crop models can compensate for time-consuming and costly field trials to screen for better irrigation regimes, but their predictive accuracy is often compromised by parameter uncertainty. To address this issue, we utilized data from a three-year (2022–2024) field trial (with irrigation at 50%, 75%, and 100% of evapotranspiration and potassium applications of 120, 180, and 240 kg/ha) to simulate the growth process of jujube trees in arid regions using the WOFOST model. In this study, parameter sensitivity analyses were conducted to determine that photosynthetic capacity maximization (Amax), the potassium nutrition index (Kstatus), the water stress factor (SWF), the water–potassium photosynthetic coefficient of synergy (α), and potassium partitioning weight coefficients (βi) were the important parameters affecting the simulated growth process of the crop. Path analysis using segmented structural equations also showed that water stress factor (SWF) and potassium nutrition index (Kstatus) indirectly controlled yield by significantly affecting photosynthesis (path coefficients: 0.72 and 0.75, respectively). The ability of the crop model to simulate the growth process and yield of jujube trees was improved by the introduction of water and potassium parameters (R2 = 0.94–0.96, NRMSE = 4.1–12.2%). The subsequent multi-objective optimization of yield and crop water productivity of dates under different combinations of water and potassium treatments under a bi-objective optimization model based on the NSGA-II algorithm showed that the optimal strategy was irrigation at 80% ETc combined with 300 kg/ha of potassium application. This management model ensures yield and maximizes crop water use efficiency (CWP), thus providing a scientific and efficient irrigation and fertilization regime for jujube trees in arid zones. Full article
(This article belongs to the Section Crop Production)
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19 pages, 9248 KB  
Article
Irrigation Suitability and Interaction Between Surface Water and Groundwater Influenced by Agriculture Activities in an Arid Plain of Central Asia
by Chenwei Tu, Wanrui Wang, Weihua Wang, Farong Huang, Minmin Gao, Yanchun Liu, Peiyao Gong and Yuan Yao
Agriculture 2025, 15(15), 1704; https://doi.org/10.3390/agriculture15151704 - 7 Aug 2025
Viewed by 538
Abstract
Agricultural activities and dry climatic conditions promote the evaporation and salinization of groundwater in arid areas. Long-term irrigation alters the groundwater circulation and environment in arid plains, as well as its hydraulic connection with surface water. A comprehensive assessment of groundwater irrigation suitability [...] Read more.
Agricultural activities and dry climatic conditions promote the evaporation and salinization of groundwater in arid areas. Long-term irrigation alters the groundwater circulation and environment in arid plains, as well as its hydraulic connection with surface water. A comprehensive assessment of groundwater irrigation suitability and its interaction with surface water is essential for water–ecology–agriculture security in arid areas. This study evaluates the irrigation water quality and groundwater–surface water interaction influenced by agricultural activities in a typical arid plain region using hydrochemical and stable isotopic data from 51 water samples. The results reveal that the area of cultivated land increases by 658.9 km2 from 2000 to 2023, predominantly resulting from the conversion of bare land. Groundwater TDS (total dissolved solids) value exhibits significant spatial heterogeneity, ranging from 516 to 2684 mg/L. Cl−, SO42−, and Na+ are the dominant ions in groundwater, with a widespread distribution of brackish water. Groundwater δ18O values range from −9.4‰ to −5.4‰, with the mean value close to surface water. In total, 86% of the surface water samples are good and suitable for agricultural irrigation, while 60% of shallow groundwater samples are marginally suitable or unsuitable for irrigation at present. Groundwater hydrochemistry is largely controlled by intensive evaporation, water–rock interaction, and agricultural activities (e.g., cultivated land expansion, irrigation, groundwater exploitation, and fertilizers). Agricultural activities could cause shallow groundwater salinization, even confined water deterioration, with an intense and frequent exchange between groundwater and surface water. In order to sustainably manage groundwater and maintain ecosystem stability in arid plain regions, controlling cultivated land area and irrigation water amount, enhancing water utilization efficiency, limiting groundwater exploitation, and fully utilizing floodwater resources would be the viable ways. The findings will help to deepen the understanding of the groundwater quality evolution mechanism in arid irrigated regions and also provide a scientific basis for agricultural water management in the context of extreme climatic events and anthropogenic activities. Full article
(This article belongs to the Section Agricultural Water Management)
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34 pages, 3764 KB  
Review
Research Progress and Applications of Artificial Intelligence in Agricultural Equipment
by Yong Zhu, Shida Zhang, Shengnan Tang and Qiang Gao
Agriculture 2025, 15(15), 1703; https://doi.org/10.3390/agriculture15151703 - 7 Aug 2025
Viewed by 1304
Abstract
With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative [...] Read more.
With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article
(This article belongs to the Section Agricultural Technology)
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17 pages, 2649 KB  
Article
Four-Dimensional Hyperspectral Imaging for Fruit and Vegetable Grading
by Laraib Haider Naqvi, Badrinath Balasubramaniam, Jiaqiong Li, Lingling Liu and Beiwen Li
Agriculture 2025, 15(15), 1702; https://doi.org/10.3390/agriculture15151702 - 6 Aug 2025
Viewed by 1093
Abstract
Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality [...] Read more.
Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality assessment and increased waste. To address this, we developed a 4D-grading pipeline that fuses visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging with structured-light 3D scanning to non-destructively evaluate both internal defects and external form. Our contributions are (1) flagging the defects in fruits based on the reflectance information, (2) accurate shape and defect measurement based on the 3D data of fruits, and (3) an interpretable, decision-tree framework that assigns USDA-style quality (Premium, Grade 1/2, Reject) and size (Small–Extra Large) labels. We demonstrate this approach through preliminary results, suggesting that 4D hyperspectral imaging may offer advantages over single-modality methods by providing clear, interpretable decision rules and the potential for adaptation to other produce types. Full article
(This article belongs to the Special Issue Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities)
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24 pages, 2697 KB  
Article
Different Responses to Salinity of Pythium spp. Causing Root Rot on Atriplex hortensis var. rubra Grown in Hydroponics
by Emiliano Delli Compagni, Bruno Bighignoli, Piera Quattrocelli, Irene Nicolini, Marco Battellino, Alberto Pardossi and Susanna Pecchia
Agriculture 2025, 15(15), 1701; https://doi.org/10.3390/agriculture15151701 - 6 Aug 2025
Viewed by 3330
Abstract
Atriplex hortensis var. rubra (red orache, RO) is a halotolerant species rich in nutraceutical compounds, which makes it a valuable crop for human nutrition. This plant could also be exploited for phytoremediation of contaminated soil and wastewater, and for saline aquaponics. A root [...] Read more.
Atriplex hortensis var. rubra (red orache, RO) is a halotolerant species rich in nutraceutical compounds, which makes it a valuable crop for human nutrition. This plant could also be exploited for phytoremediation of contaminated soil and wastewater, and for saline aquaponics. A root rot disease was observed on hydroponically grown RO plants, caused by Pythium deliense and Pythium Cluster B2a sp. Identification was based on morphology, molecular analysis (ITS and COI), and phylogenetic analysis. We assessed disease severity in plants grown in a growth chamber with nutrient solutions containing different NaCl concentrations (0, 7, and 14 g L−1 NaCl). In vitro growth at different salinity levels and temperatures was also evaluated. Both Pythium species were pathogenic but showed different responses. Pythium deliense was significantly more virulent than Pythium Cluster B2a sp., causing a steady reduction in root dry weight (RDW) of 70% across all salinity levels. Pythium Cluster B2a sp. reduced RDW by 50% at 0 and 7 g L−1 NaCl while no symptoms were observed at 14 g L−1 NaCl. Pythium deliense grew best at 7 and 14 g L−1 NaCl, while Pythium Cluster B2a sp. growth was reduced at 14 g L−1 NaCl. Both pathogens had an optimum temperature of 30 °C. This is the first report of Pythium spp. causing root rot on RO grown hydroponically. The effective use of halophytic crops must consider pathogen occurrence and fitness in saline conditions. 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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21 pages, 1788 KB  
Article
Investigation, Prospects, and Economic Scenarios for the Use of Biochar in Small-Scale Agriculture in Tropical
by Vinicius John, Ana Rita de Oliveira Braga, Criscian Kellen Amaro de Oliveira Danielli, Heiriane Martins Sousa, Filipe Eduardo Danielli, Newton Paulo de Souza Falcão, João Guerra, Dimas José Lasmar and Cláudia S. C. Marques-dos-Santos
Agriculture 2025, 15(15), 1700; https://doi.org/10.3390/agriculture15151700 - 6 Aug 2025
Viewed by 1045
Abstract
This study investigates the production and economic feasibility of biochar for smallholder and family farms in Central Amazonia, with potential implications for other tropical regions. The costs of construction of a prototype mobile kiln and biochar production were evaluated, using small-sized biomass from [...] Read more.
This study investigates the production and economic feasibility of biochar for smallholder and family farms in Central Amazonia, with potential implications for other tropical regions. The costs of construction of a prototype mobile kiln and biochar production were evaluated, using small-sized biomass from acai (Euterpe oleracea Mart.) agro-industrial residues as feedstock. The biochar produced was characterised in terms of its liming capacity (calcium carbonate equivalence, CaCO3eq), nutrient content via organic fertilisation methods, and ash analysis by ICP-OES. Field trials with cowpea assessed economic outcomes, as well scenarios of fractional biochar application and cost comparison between biochar production in the prototype kiln and a traditional earth-brick kiln. The prototype kiln showed production costs of USD 0.87–2.06 kg−1, whereas traditional kiln significantly reduced costs (USD 0.03–0.08 kg−1). Biochar application alone increased cowpea revenue by 34%, while combining biochar and lime raised cowpea revenues by up to 84.6%. Owing to high input costs and the low value of the crop, the control treatment generated greater net revenue compared to treatments using lime alone. Moreover, biochar produced in traditional kilns provided a 94% increase in net revenue compared to liming. The estimated externalities indicated that carbon credits represented the most significant potential source of income (USD 2217 ha−1). Finally, fractional biochar application in ten years can retain over 97% of soil carbon content, demonstrating potential for sustainable agriculture and carbon sequestration and a potential further motivation for farmers if integrated into carbon markets. Public policies and technological adaptations are essential for facilitating biochar adoption by small-scale tropical farmers. Full article
(This article belongs to the Special Issue Converting and Recycling of Agroforestry Residues)
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19 pages, 19040 KB  
Article
Multi-Strategy Fusion RRT-Based Algorithm for Optimizing Path Planning in Continuous Cherry Picking
by Yi Zhang, Xinying Miao, Yifei Sun, Zhipeng He, Tianwen Hou, Zhenghan Wang and Qiuyan Wang
Agriculture 2025, 15(15), 1699; https://doi.org/10.3390/agriculture15151699 - 6 Aug 2025
Viewed by 411
Abstract
Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a [...] Read more.
Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm (σ-TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation. Full article
(This article belongs to the Section Agricultural Technology)
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25 pages, 4021 KB  
Article
A Hybrid Path Planning Algorithm for Orchard Robots Based on an Improved D* Lite Algorithm
by Quanjie Jiang, Yue Shen, Hui Liu, Zohaib Khan, Hao Sun and Yuxuan Huang
Agriculture 2025, 15(15), 1698; https://doi.org/10.3390/agriculture15151698 - 6 Aug 2025
Viewed by 939
Abstract
Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path [...] Read more.
Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article
(This article belongs to the Special Issue Perception, Decision-Making, and Control of Agricultural Robots)
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25 pages, 9225 KB  
Article
Enhanced YOLO11n-Seg with Attention Mechanism and Geometric Metric Optimization for Instance Segmentation of Ripe Blueberries in Complex Greenhouse Environments
by Rongxiang Luo, Rongrui Zhao and Bangjin Yi
Agriculture 2025, 15(15), 1697; https://doi.org/10.3390/agriculture15151697 - 6 Aug 2025
Viewed by 522
Abstract
This study proposes an improved YOLO11n-seg instance segmentation model to address the limitations of existing models in accurately identifying mature blueberries in complex greenhouse environments. Current methods often lack sufficient accuracy when dealing with complex scenarios, such as fruit occlusion, lighting variations, and [...] Read more.
This study proposes an improved YOLO11n-seg instance segmentation model to address the limitations of existing models in accurately identifying mature blueberries in complex greenhouse environments. Current methods often lack sufficient accuracy when dealing with complex scenarios, such as fruit occlusion, lighting variations, and target overlap. To overcome these challenges, we developed a novel approach that integrates a Spatial–Channel Adaptive (SCA) attention mechanism and a Dual Attention Balancing (DAB) module. The SCA mechanism dynamically adjusts the receptive field through deformable convolutions and fuses multi-scale color features. This enhances the model’s ability to recognize occluded targets and improves its adaptability to variations in lighting. The DAB module combines channel–spatial attention and structural reparameterization techniques. This optimizes the YOLO11n structure and effectively suppresses background interference. Consequently, the model’s accuracy in recognizing fruit contours improves. Additionally, we introduce Normalized Wasserstein Distance (NWD) to replace the traditional intersection over union (IoU) metric and address bias issues that arise in dense small object matching. Experimental results demonstrate that the improved model significantly improves target detection accuracy, recall rate, and mAP@0.5, achieving increases of 1.8%, 1.5%, and 0.5%, respectively, over the baseline model. On our self-built greenhouse blueberry dataset, the mask segmentation accuracy, recall rate, and mAP@0.5 increased by 0.8%, 1.2%, and 0.1%, respectively. In tests across six complex scenarios, the improved model demonstrated greater robustness than mainstream models such as YOLOv8n-seg, YOLOv8n-seg-p6, and YOLOv9c-seg, especially in scenes with dense occlusions. The improvement in mAP@0.5 and F1 scores validates the effectiveness of combining attention mechanisms and multiple metric optimizations, for instance, segmentation tasks in complex agricultural scenes. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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21 pages, 5253 KB  
Article
Discharge Dynamics Responses in Forced Granular Flow of Rice Particle Beds
by Dan Zhao, Zhuozhuang Li, Xianle Li, Zhiqin Zhang and Dong Liu
Agriculture 2025, 15(15), 1696; https://doi.org/10.3390/agriculture15151696 - 6 Aug 2025
Viewed by 497
Abstract
The discharge behavior of agricultural materials from silos is significantly influenced by external driving forces. Pressurized discharge from silos is an effective method for analyzing localized stress distribution and controlling flow rates. In this study, a combined approach of experiments and Discrete Element [...] Read more.
The discharge behavior of agricultural materials from silos is significantly influenced by external driving forces. Pressurized discharge from silos is an effective method for analyzing localized stress distribution and controlling flow rates. In this study, a combined approach of experiments and Discrete Element Method (DEM) simulations was employed to investigate the forced flow behavior of rice particle beds. Detailed analyses were conducted on flow patterns, velocity distributions, mass flow rates, dynamic arch formation, bottom stress distribution, and load propagation. Furthermore, the dissipative power during discharge was calculated based on the local shear at the silo wall, and a master curve for the dissipative power was presented. This curve facilitates the prediction of energy dissipation during silo discharge under various conditions. The findings provide a foundation and data support for the structural optimization of silos. Full article
(This article belongs to the Special Issue Mathematical Modeling for Technological Processes of Agricultural Products)
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17 pages, 1396 KB  
Article
Dose-Dependent Effect of the Polyamine Spermine on Wheat Seed Germination, Mycelium Growth of Fusarium Seed-Borne Pathogens, and In Vivo Fusarium Root and Crown Rot Development
by Tsvetina Nikolova, Dessislava Todorova, Tzenko Vatchev, Zornitsa Stoyanova, Valya Lyubenova, Yordanka Taseva, Ivo Yanashkov and Iskren Sergiev
Agriculture 2025, 15(15), 1695; https://doi.org/10.3390/agriculture15151695 - 6 Aug 2025
Viewed by 775
Abstract
Wheat (Triticum aestivum L.) is a crucial global food crop. The intensive crop farming, monoculture cultivation, and impact of climate change affect the susceptibility of wheat cultivars to biotic stresses, mainly caused by soil fungal pathogens, especially those belonging to the genus [...] Read more.
Wheat (Triticum aestivum L.) is a crucial global food crop. The intensive crop farming, monoculture cultivation, and impact of climate change affect the susceptibility of wheat cultivars to biotic stresses, mainly caused by soil fungal pathogens, especially those belonging to the genus Fusarium. This situation threatens yield and grain quality through root and crown rot. While conventional chemical fungicides face resistance issues and environmental concerns, biological alternatives like seed priming with natural metabolites are gaining attention. Polyamines, including putrescine, spermidine, and spermine, are attractive priming agents influencing plant development and abiotic stress responses. Spermine in particular shows potential for in vitro antifungal activity against Fusarium. Optimising spermine concentration for seed priming is crucial to maximising protection against Fusarium infection while ensuring robust plant growth. In this research, we explored the potential of the polyamine spermine as a seed treatment to enhance wheat resilience, aiming to identify a sustainable alternative to synthetic fungicides. Our findings revealed that a six-hour seed soak in spermine solutions ranging from 0.5 to 5 mM did not delay germination or seedling growth. In fact, the 5 mM concentration significantly stimulated root weight and length. In complementary in vitro assays, we evaluated the antifungal activity of spermine (0.5–5 mM) against three Fusarium species. The results demonstrated complete inhibition of Fusarium culmorum growth at 5 mM spermine. A less significant effect on Fusarium graminearum and little to no impact on Fusarium oxysporum were found. The performed analysis revealed that the spermine had a fungistatic effect against the pathogen, retarding the mycelium growth of F. culmorum inoculated on the seed surface. A pot experiment with Bulgarian soft wheat cv. Sadovo-1 was carried out to estimate the effect of seed priming with spermine against infection with isolates of pathogenic fungus F. culmorum on plant growth and disease severity. Our results demonstrated that spermine resulted in a reduced distribution of F. culmorum and improved plant performance, as evidenced by the higher fresh weight and height of plants pre-treated with spermine. This research describes the efficacy of spermine seed priming as a novel strategy for managing Fusarium root and crown rot in wheat. Full article
(This article belongs to the Special Issue Fungal Plant Pathogens in Agricultural Crops: Diversity, Detection, and Control)
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17 pages, 2376 KB  
Article
Selection and Characterisation of Elite Mesorhizobium spp. Strains That Mitigate the Impact of Drought Stress on Chickpea
by María Camacho, Francesca Vaccaro, Pilar Brun, Francisco Javier Ollero, Francisco Pérez-Montaño, Miriam Negussu, Federico Martinelli, Alessio Mengoni, Dulce Nombre Rodriguez-Navarro and Camilla Fagorzi
Agriculture 2025, 15(15), 1694; https://doi.org/10.3390/agriculture15151694 - 5 Aug 2025
Viewed by 604
Abstract
The chickpea (Cicer arietinum L.) is a key legume crop in Mediterranean agriculture, valued for its nutritional profile and adaptability. However, its productivity is severely impacted by drought stress. To identify microbial solutions that enhance drought resilience, we isolated seven Mesorhizobium strains [...] Read more.
The chickpea (Cicer arietinum L.) is a key legume crop in Mediterranean agriculture, valued for its nutritional profile and adaptability. However, its productivity is severely impacted by drought stress. To identify microbial solutions that enhance drought resilience, we isolated seven Mesorhizobium strains from chickpea nodules collected in southern Spain and evaluated their cultivar-specific symbiotic performance. Two commercial cultivars (Pedrosillano and Blanco Lechoso) and twenty chickpea germplasms were tested under growth chamber and greenhouse conditions, both with and without drought stress. Initial screening in a sterile substrate using nodulation assays, shoot/root dry weight measurements, and acetylene reduction assays identified three elite strains (ISC11, ISC15, and ISC25) with superior symbiotic performance and nitrogenase activity. Greenhouse trials under reduced irrigation demonstrated that several strain–cultivar combinations significantly mitigated drought effects on plant biomass, with specific interactions (e.g., ISC25 with RR-98 or BT6-19) preserving over 70% of shoot biomass relative to controls. Whole-genome sequencing of the elite strains revealed diverse taxonomic affiliations—ISC11 as Mesorhizobium ciceri, ISC15 as Mesorhizobium mediterraneum, and ISC25 likely representing a novel species. Genome mining identified plant growth-promoting traits including ACC deaminase genes (in ISC11 and ISC25) and genes coding for auxin biosynthesis-related enzymes. Our findings highlight the potential of targeted rhizobial inoculants tailored to chickpea cultivars to improve crop performance under water-limiting conditions. Full article
(This article belongs to the Special Issue Beneficial Microbes for Sustainable Crop Production)
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16 pages, 3291 KB  
Article
A Discrete Element Model for Characterizing Soil-Cotton Seeding Equipment Interactions Using the JKR and Bonding Contact Models
by Xuyang Ran, Long Wang, Jianfei Xing, Lu Shi, Dewei Wang, Wensong Guo and Xufeng Wang
Agriculture 2025, 15(15), 1693; https://doi.org/10.3390/agriculture15151693 - 5 Aug 2025
Viewed by 533
Abstract
Due to the increasing demand for agricultural water, the water availability for winter and spring irrigation of cotton fields has decreased. Consequently, dry seeding followed by irrigation (DSSI) has become a widespread cotton cultivation technique in Xinjiang. This study focused on the interaction [...] Read more.
Due to the increasing demand for agricultural water, the water availability for winter and spring irrigation of cotton fields has decreased. Consequently, dry seeding followed by irrigation (DSSI) has become a widespread cotton cultivation technique in Xinjiang. This study focused on the interaction between soil particles and cotton seeding equipment under DSSI in Xinjiang. The discrete element method (DEM) simulation framework was employed to compare the performance of the Johnson-Kendall-Roberts (JKR) model and Bonding model in simulating contact between soil particles. The models’ ability to simulate the angle of repose was investigated, and shear tests were conducted. The simulation results showed that both models had comparable repose angles, with relative errors of 0.59% for the JKR model and 0.36% for the contact model. However, the contact model demonstrated superior predictive accuracy in simulating direct shear test results, predicting an internal friction angle of 35.8°, with a relative error of 5.8% compared to experimental measurements. In contrast, the JKR model exhibited a larger error. The Bonding model provides a more accurate description of soil particle contact. Subsoiler penetration tests showed that the maximum penetration force was 467.2 N, closely matching the simulation result of 485.3 N, which validates the reliability of the model parameters. The proposed soil simulation framework and calibrated parameters accurately represented soil mechanical properties, providing a robust basis for discrete element modeling and structural optimization of soil-tool interactions in cotton field tillage machinery. Full article
(This article belongs to the Section Agricultural Technology)
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17 pages, 972 KB  
Article
A Preliminary Investigation into Heavy Metal Tolerance in Pseudomonas Isolates: Does the Isolation Site Have an Effect?
by Alessandro De Santis, Antonio Bevilacqua, Angela Racioppo, Barbara Speranza, Maria Rosaria Corbo, Clelia Altieri and Milena Sinigaglia
Agriculture 2025, 15(15), 1692; https://doi.org/10.3390/agriculture15151692 - 5 Aug 2025
Viewed by 697
Abstract
One hundred presumptive Pseudomonas isolates, recovered from 15 sites impacted by anthropogenic activity in the Foggia district (Italy), were screened for key adaptive and functional traits important for environmental applications. The isolates were phenotypically characterized for their ability to grow under combined pH [...] Read more.
One hundred presumptive Pseudomonas isolates, recovered from 15 sites impacted by anthropogenic activity in the Foggia district (Italy), were screened for key adaptive and functional traits important for environmental applications. The isolates were phenotypically characterized for their ability to grow under combined pH (5.0–8.0) and temperature (15–37 °C) conditions, to produce proteolytic enzymes, pigments, and exopolysaccharides, and to tolerate SDS. Moreover, the resistance to six environmentally relevant heavy metals (Cd, Co, Cu, Ni, Zn, As) was qualitatively assessed. The results highlighted wide inter-strain variability, with distinct clusters of isolates showing unique combinations of stress tolerance, enzymatic potential, and resistance profile. PERMANOVA analysis revealed significant effects of both the isolation site and the metal type, as well as their interaction, on the observed resistance patterns. A subset of isolates showed co-tolerance to elevated temperatures and heavy metals. These findings offer an initial yet insightful overview of the adaptive diversity of soil-derived Pseudomonas, laying the groundwork for the rational selection of strains for bioaugmentation in contaminated soils. Full article
(This article belongs to the Special Issue Functional Regulation of Soil Microorganisms in Agricultural Ecosystems: Impact on Agronomical Practices)
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16 pages, 4423 KB  
Article
Assessing the Variation in Maize Water Footprint Under Different Tillage Practices: A Case Study from Jilin Province, China
by Bo Li, Lijie Qin, Mingzhu Lv, Yongcai Dang and Hang Qi
Agriculture 2025, 15(15), 1691; https://doi.org/10.3390/agriculture15151691 - 5 Aug 2025
Viewed by 463
Abstract
Studying the impact of different tillage practices on crop water consumption can help us identify optimal tillage practice choices. The traditional tillage (TT) and conservation tillage (CT) methods are the dominant practices in Jilin Province, China. Few studies have explored the differences in [...] Read more.
Studying the impact of different tillage practices on crop water consumption can help us identify optimal tillage practice choices. The traditional tillage (TT) and conservation tillage (CT) methods are the dominant practices in Jilin Province, China. Few studies have explored the differences in crop water consumption between TT and CT. To address this knowledge gap, this study utilized maize as its research object and employed the water footprint (WF) as the indicator to assess crop water consumption under TT and CT. This study aimed to investigate when differences in water consumption between TT and CT appear and whether the differences are significant. The results of this study demonstrated that the total WF under CT (339.65 m3 t−1) was less than that under TT (378.19 m3 t−1), and the spatial difference was distinct. The total WF exhibited a clear change under different CT durations. At the initial stage of CT implementation, the total WF decreased slightly compared to that under TT. With an increase in CT duration, the total WF was significantly reduced. The findings of this study demonstrate that CT is an effective measure to ensure sustainable crop production and that it could lead policymakers to choose CT to reduce water consumption. Full article
(This article belongs to the Section Agricultural Water Management)
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17 pages, 2283 KB  
Article
A Remote Strawberry Health Monitoring System Performed with Multiple Sensors Approach
by Xiao Du, Jun Steed Huang, Qian Shi, Tongge Li, Yanfei Wang, Haodong Liu, Zhaoyuan Zhang, Ni Yu and Ning Yang
Agriculture 2025, 15(15), 1690; https://doi.org/10.3390/agriculture15151690 - 5 Aug 2025
Viewed by 737
Abstract
Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in [...] Read more.
Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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18 pages, 2839 KB  
Article
Detection of Maize Pathogenic Fungal Spores Based on Deep Learning
by Yijie Ren, Ying Xu, Huilin Tian, Qian Zhang, Mingxiu Yang, Rongsheng Zhu, Dawei Xin, Qingshan Chen, Qiaorong Wei and Shuang Song
Agriculture 2025, 15(15), 1689; https://doi.org/10.3390/agriculture15151689 - 5 Aug 2025
Viewed by 616
Abstract
Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve [...] Read more.
Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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17 pages, 1416 KB  
Article
Humic Substances Promote the Activity of Enzymes Related to Plant Resistance
by Rakiely M. Silva, Fábio L. Olivares, Lázaro E. P. Peres, Etelvino H. Novotny and Luciano P. Canellas
Agriculture 2025, 15(15), 1688; https://doi.org/10.3390/agriculture15151688 - 5 Aug 2025
Viewed by 814
Abstract
The extensive use of pesticides has significant implications for public health and the environment. Breeding crop plants is the most effective and environmentally friendly approach to improve the plants’ resistance. However, it is time-consuming and costly, and it is sometimes difficult to achieve [...] Read more.
The extensive use of pesticides has significant implications for public health and the environment. Breeding crop plants is the most effective and environmentally friendly approach to improve the plants’ resistance. However, it is time-consuming and costly, and it is sometimes difficult to achieve satisfactory results. Plants induce defense responses to natural elicitors by interpreting multiple genes that encode proteins, including enzymes, secondary metabolites, and pathogenesis-related (PR) proteins. These responses characterize systemic acquired resistance. Humic substances trigger positive local and systemic physiological responses through a complex network of hormone-like signaling pathways and can be used to induce biotic and abiotic stress resistance. This study aimed to assess the effect of humic substances on the activity of phenylalanine ammonia-lyase (PAL), peroxidase (POX), and β-1,3-glucanase (GLU) used as a resistance marker in various plant species, including orange, coffee, sugarcane, soybeans, maize, and tomato. Seedlings were treated with a dilute aqueous suspension of humic substances (4 mM C L−1) as a foliar spray or left untreated (control). Leaf tissues were collected for enzyme assessment two days later. Humic substances significantly promoted the systemic acquired resistance marker activities compared to the control in all independent assays. Overall, all enzymes studied in this work, PAL, GLUC, and POX, showed an increase in activity by 133%, 181%, and 149%, respectively. Among the crops studied, citrus and coffee achieved the highest activity increase in all enzymes, except for POX in coffee, which showed a decrease of 29% compared to the control. GLUC exhibited the highest response to HS treatment, the enzyme most prominently involved in increasing enzymatic activity in all crops. Plants can improve their resistance to pathogens through the exogenous application of HSs as this promotes the activity of enzymes related to plant resistance. Finally, we consider the potential use of humic substances as a natural chemical priming agent to boost plant resistance in agriculture Full article
(This article belongs to the Special Issue Biocontrol Agents for Plant Pest Management)
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22 pages, 2542 KB  
Article
Wheat Under Warmer Nights: Shifting of Sowing Dates for Managing Impacts of Thermal Stress
by Roshan Subedi, Mani Naiker, Yash Chauhan, S. V. Krishna Jagadish and Surya P. Bhattarai
Agriculture 2025, 15(15), 1687; https://doi.org/10.3390/agriculture15151687 - 5 Aug 2025
Viewed by 754
Abstract
High nighttime temperature (HNT) due to asymmetric diurnal warming threatens wheat productivity. This study evaluated the effect of HNT on wheat phenology, physiology, and yield through field and controlled environment experiments in Central Queensland, Australia. Two wheat genotypes, Faraday and AVT#6, were assessed [...] Read more.
High nighttime temperature (HNT) due to asymmetric diurnal warming threatens wheat productivity. This study evaluated the effect of HNT on wheat phenology, physiology, and yield through field and controlled environment experiments in Central Queensland, Australia. Two wheat genotypes, Faraday and AVT#6, were assessed under three sowing dates—1 May (Early), 15 June (Mid), and 1 August (Late)—within the recommended sowing window for the region. In a parallel growth chamber study, the plants were exposed to two nighttime temperature regimes, of 15 °C (normal) and 20 °C (high), with consistent daytime conditions from booting to maturity. Late sowing resulted in shortened vegetative growth and grain filling periods and increased exposure to HNT during the reproductive phase. This resulted in elevated floret sterility, lower grain weight, and up to 40% yield loss. AVT#6 exhibited greater sensitivity to HNT despite maturing earlier. Leaf gas exchange analysis revealed increased nighttime respiration (Rn) and reduced assimilation (A), resulting in higher Rn/A ratio for late-sown crops. The results from controlled environment chambers resembled trends of the field experiment, producing lower grain yield and biomass under HNT. Cumulative nighttime hours above 20 °C correlated more strongly with yield losses than daytime heat. These findings highlight the need for HNT-tolerant genotypes and optimized sowing schedules under future climate scenarios. Full article
(This article belongs to the Section Crop Production)
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26 pages, 11494 KB  
Article
Establishment of Hollow Flexible Model with Two Types of Bonds and Calibration of the Contact Parameters for Wheat Straw
by Huinan Huang, Yan Zhang, Guangyu Hou, Baohao Su, Hao Yin, Zijiang Fu, Yangfan Zhuang, Zhijun Lv, Hui Tian and Lianhao Li
Agriculture 2025, 15(15), 1686; https://doi.org/10.3390/agriculture15151686 - 4 Aug 2025
Viewed by 534
Abstract
In view of the lack of accurate model in the discrete element study during straw comprehensive utilization (crushing, mixing, and baling), wheat straw was taken as the research object to calibrate the simulation parameters using EDEM 2023. The intrinsic and contact mechanical parameters [...] Read more.
In view of the lack of accurate model in the discrete element study during straw comprehensive utilization (crushing, mixing, and baling), wheat straw was taken as the research object to calibrate the simulation parameters using EDEM 2023. The intrinsic and contact mechanical parameters of wheat straw were measured, and a test of the angle of repose (AOR), extrusion test and bending test were carried out. On this basis, a discrete element model (DEM) of hollow flexibility by using cylindrical particles was developed. The optimal combination of contact mechanical parameters was obtained through AOR tests based on the Box–Behnken design (BBD), coefficients of static friction, rolling friction, and restitution between wheat straw and wheat straw-45 steel are separately 0.227, 0.136, 0.479, 0.271, 0.093, and 0.482, AOR is 18.66°. Meanwhile, optimal combinations of bond contact parameters were determined by the BBD. The calibrated parameters were used to conduct extrusion and bending tests. Results show that the average values of peak extrusion force and peak bending pressure are 23.20 N and 3.92 N, which have relative discrepancy of 3.25% and 3.59% compared to physical test measurements. The results can provide model reference for the optimization design such as feed processing equipment, baler, and mixer. Full article
(This article belongs to the Section Agricultural Technology)
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14 pages, 551 KB  
Article
Responses of Broiler Breeder Hens to Dietary Digestible Lysine, Methionine+Cystine, and Threonine
by Michele B. de Lima, Nilva K. Sakomura, Cléber F. S. Oliveira, Rita B. Vieira, Jaqueline A. Pavanini and Edney P. da Silva
Agriculture 2025, 15(15), 1685; https://doi.org/10.3390/agriculture15151685 - 4 Aug 2025
Viewed by 772
Abstract
To evaluate the response of broiler breeder hens submitted to different amino acid intakes of methionine+cystine, lysine, and threonine, and to determine the coefficients for egg output and body weight for maintenance. Three studies were performed using 160 broiler breeder hens housed individually [...] Read more.
To evaluate the response of broiler breeder hens submitted to different amino acid intakes of methionine+cystine, lysine, and threonine, and to determine the coefficients for egg output and body weight for maintenance. Three studies were performed using 160 broiler breeder hens housed individually in metabolic cages. A summit diet and a nitrogen-free diet were formulated. The levels ranged from 1.79 to 7.13, 2.49 to 8.3, and 2.04 to 6.79 g/kg of methionine+cystine, lysine, and threonine, respectively. The variables measured were feed intake, amino acid intake, rate of lay, egg weight, and egg output. The broken line model was used to evaluate the responses. It was verified that higher values of the rate of lay, egg weight, and egg output were observed for the higher concentrations of amino acids studied. A significant difference was observed for the variables rate of lay, egg weight, egg output, and body weight (p < 0.05) for the three amino acids evaluated. The amount of each amino acid required to produce one gram per egg was estimated at 12.4 mg, 14.5 mg, and 11.2 mg for methionine+cystine, lysine, and threonine, respectively. The values estimated by coefficient b that represent the amino acid for maintenance requirement were methionine+cystine, lysine, and threonine of 30.2, 32.2, and 42.4 mg/kg BW, respectively. The coefficients may be used to design additional models to study requirements nutrition in broiler breeders, allowing a better understanding of how these birds respond to different dietary amino acids. Full article
(This article belongs to the Section Farm Animal Production)
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23 pages, 908 KB  
Article
Employee Perceptions of ESG Policy Implementation in Urban and Rural Financial Institutions
by Jelena Vapa Tankosić, Nemanja Lekić, Miroslav Čavlin, Vinko Burnać, Milovan Mirkov, Radivoj Prodanović, Gordana Bejatović, Nedeljko Prdić and Borjana Mirjanić
Agriculture 2025, 15(15), 1684; https://doi.org/10.3390/agriculture15151684 - 4 Aug 2025
Viewed by 859
Abstract
The purpose of this research is to examine employee perceptions regarding the implementation of ESG (environmental, social, and governance) practices in financial institutions, with a comparative focus on urban and rural banks in the Republic of Serbia. The study investigates how employees assess [...] Read more.
The purpose of this research is to examine employee perceptions regarding the implementation of ESG (environmental, social, and governance) practices in financial institutions, with a comparative focus on urban and rural banks in the Republic of Serbia. The study investigates how employees assess environmental, social, and governance aspects of ESG, as well as their own role in applying these principles in everyday work. The results reveal statistically significant differences between the two groups; employees in urban banks report greater engagement, more access to training, and stronger involvement in ESG decision-making. These findings suggest the existence of more developed institutional support, infrastructure, and organisational culture in urban banks. In contrast, employees in rural banks highlight the need for enhanced training, clearer ESG guidance, and improved oversight mechanisms. The study underlines the importance of investing in employee development and internal communication, particularly in rural contexts, to improve ESG outcomes. By focusing on employee-level perceptions, this research contributes to the understanding of how organisational and geographic factors influence the implementation of ESG-related practices in financial institutions. Full article
(This article belongs to the Special Issue Sustainability and Energy Economics in Agriculture—2nd Edition)
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Article
Study of the Impact of Agricultural Insurance on the Livelihood Resilience of Farmers: A Case Study of Comprehensive Natural Rubber Insurance
by Jialin Wang, Yanglin Wu, Jiyao Liu and Desheng Zhang
Agriculture 2025, 15(15), 1683; https://doi.org/10.3390/agriculture15151683 - 4 Aug 2025
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Abstract
Against the backdrop of increasingly frequent extreme weather events and heightened market price volatility, investigating the relationship between agricultural insurance and farmers’ livelihood resilience is crucial for ensuring rural socioeconomic stability. This study utilizes field survey data from 1196 households across twelve county-level [...] Read more.
Against the backdrop of increasingly frequent extreme weather events and heightened market price volatility, investigating the relationship between agricultural insurance and farmers’ livelihood resilience is crucial for ensuring rural socioeconomic stability. This study utilizes field survey data from 1196 households across twelve county-level divisions (three cities and nine counties) from China’s Hainan and Yunnan provinces, specifically in natural rubber-producing regions. Using propensity score matching (PSM), we empirically examine agricultural insurance’s impact on household livelihood resilience. The results demonstrate that agricultural insurance increased the effect on farmers’ livelihood resilience by 1%. This effect is particularly pronounced among recently poverty-alleviated households and large-scale farming operations. Furthermore, the analysis highlights the mediating roles of credit availability, adoption of agricultural production technologies, and production initiative in strengthening insurance’s positive impact. Therefore, policies should be refined and expanded, combining agricultural insurance with credit support and agricultural technology extension to leverage their value and ensure the sustainable development of farm households. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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