Search Results (1,474)

The integration of a real-time image processing system using multiple webcams with a variable rate spraying system mounted on the back of an unmanned tractor presents an effective solution to the labor shortage in agriculture. This research aims to design and fabricate a low-cost, variable-rate, remote-controlled sprayer specifically for use in sugarcane fields. The primary method involves the modification of a 15-horsepower tractor, which will be equipped with a remote-control system to manage both the driving and steering functions. A foldable remote-controlled spraying arm is installed at the rear of the unmanned tractor. The system operates by using a webcam mounted on the spraying arm to capture high-angle images above the sugarcane canopy. These images are recorded and processed, and the data is relayed to the spraying control system. As a result, chemicals can be sprayed on the sugarcane accurately and efficiently based on the insights gained from image processing. Tests were conducted at various nozzle heights of 0.25 m, 0.5 m, and 0.75 m. The average system efficiency was found to be 85.30% at a pressure of 1 bar, with a chemical spraying rate of 36 L per hour and a working capacity of 0.975 hectares per hour. The energy consumption recorded was 0.161 kWh, while fuel consumption was measured at 6.807 L per hour. In conclusion, the development of the remote-controlled variable rate sprayer mounted on an unmanned tractor enables immediate and precise chemical application through remote control. This results in high-precision spraying and uniform distribution, ultimately leading to cost savings, particularly by allowing for adjustments in nozzle height from a minimum of 0.25 m to a maximum of 0.75 m from the target. Full article

In this study, we aimed to address the lack of systematic research on key collision dynamics parameters (elastic restitution coefficient) in the full mechanization of rapeseed operations, which hinders the development of precision agriculture. In this present work, the restitution coefficient of rapeseed was systematically investigated, and a predictive model (R² = 0.959) was also established by using Box–Behnken design response surface methodology (BBD-RSM). The results show that the collision restitution coefficient varies in the range of 0.539–0.649, with the key influencing factors ranked as follows: moisture content (M_c) > material layer thickness (L) > drop height (H). The EDEM simulation methodology was adopted to validate the experimental results, and the results show that there is a minimal relative error (−1% < δ < 1%) between the measured and simulated rebound heights, indicating that the established model shows a reliable prediction performance. Moreover, by comprehensively analyzing stress, strain, and energy during the collision process between rapeseed and Q235 steel, it can be concluded that the process can be divided into five stages—free fall, collision compression, collision recovery, rebound oscillation, and rebound stabilization. The maximum stress (1.19 × 10⁻² MPa) and strain (6.43 × 10⁻⁶ mm) were observed at the beginning of the collision recovery stage, which can provide some theoretical and practical basis for optimizing and designing rapeseed machines, thus achieving the goals of precise control, harvest loss reduction, and increased yields. Full article

The search for sustainable, economically viable, and effective plant protection strategies against pathogenic bacteria, fungi, and viruses is a major challenge in modern agricultural practices. Chitosan (CS) is an abundant cationic natural biopolymer known for its biocompatibility, low toxicity, and antimicrobial properties. Its potential use in agriculture for pathogen control is a promising alternative to traditional chemical fertilisers and pesticides, which raise concerns regarding public health, environmental protection, and pesticide resistance. This study focused on the preparation of chitosan nanoparticles (CS-NPs) through cross-linking with organic molecules, such as tannic acid (TA). Various formulations were explored for the development of stable nanoscale particles having encapsulation capabilities towards low compounds of varying polarity and with potential agricultural applications relevant to plant health and growth. The solution properties of the NPs were assessed using dynamic and electrophoretic light scattering (DLS and ELS); their morphology was observed through atomic force microscopy (AFM), while analytical ultracentrifugation (AUC) measurements provided insights into their molar mass. Their properties proved to be primarily influenced by the concentration of CS, which significantly affected its intrinsic conformation. Additional structural insights were obtained via infrared and UV–Vis spectroscopic measurements, while detailed fluorescence analysis with the use of three different probes, as model cargo molecules, provided information regarding the hydrophobic and hydrophilic microdomains within the particles. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

Over the past years, increasing attention has been drawn to the adverse effects of agricultural pesticide use on pollinators, with honeybees being especially vulnerable. The aim of this study was to evaluate the levels of residues detectable and/or quantifiable of neonicotinoid pesticides and other pesticides in biological materials (bees, bee brood, etc.) and beehive products (honey, pollen, etc.) applied as seed dressings in rapeseed and sunflower plants in two growing seasons (2020–2021) in fields located in three agro-climatic regions in Romania. The study involved the comparative sampling of hive products (honey, pollen, adult bees, and brood) from experimental and control apiaries, followed by pesticide residue analysis in an accredited laboratory (Primoris) using validated chromatographic techniques (LC-MS/MS and GC-MS). Toxicological analyses of 96 samples, including bees, bee brood, honey, and pollen, confirmed the presence of residues in 46 samples, including 10 bee samples, 10 bee brood samples, 18 honey samples, and 8 pollen bread samples. The mean pesticide residue concentrations detected in hive products were 0.032 mg/kg in honey, 0.061 mg/kg in pollen, 0.167 mg/kg in bees, and 0.371 mg/kg in bee brood. The results highlight the exposure of honeybee colonies to multiple sources of pesticide residue contamination, under conditions where legal recommendations for the controlled application of agricultural treatments are not followed. The study provides relevant evidence for strengthening the risk assessment framework and underscores the need for adopting stricter monitoring and regulatory measures to ensure the protection of honeybee colony health. Full article

Measuring methane (CH₄) emissions from dairy systems is crucial for advancing sustainable agricultural practices aimed at mitigating climate change. However, current CH₄ measurement techniques are primarily designed for controlled research settings and are not readily scalable to diverse production environments. Thus, there is a need to develop accessible, production-level methods for estimating CH₄ emissions. This review examines the relationship between enteric CH₄ emissions and milk fatty acid (FA) composition, highlights key FA groups with potential as biomarkers for indirect CH₄ estimation, and outlines critical factors of predictive model development. Several milk FAs exhibit strong and consistent correlations to CH₄ emissions, supporting their utility as predictive biomarkers. Saturated and branched-chain FAs are generally positively associated with CH₄ emissions, while unsaturated FAs, including linolenic acid, conjugated linoleic acids, and odd-chain FAs, are typically negatively associated. Variability in the strength and direction of correlations across studies is often attributable to differences in diet or lactation stage. Similarly, differences in experimental design, data processing, and model development contribute to much of the variation observed in predictive equations across studies. Future research should aim to (1) identify milk FAs that consistently correlate with CH₄ emissions regardless of diet, (2) develop robust and standardized prediction models, and (3) prioritize the external validation of prediction models across herds and production systems. Full article

The western Shanxi Loess region, as a typical semi-arid ecologically fragile zone, faces severe soil and water resource constraints. The apple–peanut intercropping system can significantly improve water productivity and economic benefits through complementary resource utilization, representing an effective approach for sustainable agricultural development in the region. This study took the apple–peanut intercropping system as the research object (apple variety: ‘Yanfu 8’; peanut variety: ‘Huayu 38’), setting three peanut planting densities (D1: 27,500 plants/ha; D2: 18,333 plants/ha; D3: 10,833 plants/ha) and two water regulation measures—W1 (irrigation upper limit at 85% of field capacity, FC) and W2 (65% FC), with non-irrigated controls (CK) at different planting densities for comparison. This study systematically analyzed the synergistic regulation effects of intercropping density and water management on system water use and comprehensive benefits. Results showed that medium planting density combined with medium irrigation (W2D2 treatment) could maximize intercropping advantages, effectively improving the intercropping system’s soil water content (SWC), yield (GY), and water use efficiency (WUE). This research provides a theoretical basis for precision irrigation in fruit–crop intercropping systems in semi-arid regions. However, based on the significant water-saving and yield-increasing effects observed in the current experimental year, follow-up studies should verify its stability through multi-year fixed-position observation data. Full article

Foliar application of biostimulants can be a valid option to reach the goal of sustainable intensification in agriculture, especially in extensive crops such as durum wheat. However, due to the wide range of active ingredients and their mixtures available in the market, the need to select the most efficient product in a specific growing environment is of dramatic importance to achieve remarkable results in yield and grain quality. To analyze the potential of different active ingredients, a field trial was performed in two consecutive growing seasons (2023 and 2024) under Mediterranean climatic conditions. A randomized block design with three replicates was used. Durum wheat cultivar “Iride” was treated with the following five foliar biostimulants in comparison with the untreated control (T0): seaweed and plant extracts (T1); micronized vaterite (T2); culture broth of Pseudomonas protegens (T3); humic and fulvic acids (T4); organic nitrogen fertilizer (N 5%) containing glycine betaine (T5). Biostimulant treatment was applied at the end of tillering and at heading. Root length, chlorophyll content, grain yield, yield components and grain quality were measured and subjected to a one-way analysis of variance. As compared to the control, seaweed and plant extracts as well as micronized vaterite showed the best results in terms of grain yield (29% and 24% increase, respectively), root length (120% and 77% increase, respectively) and grain protein content (one percentage point increase, from approx. 12% to 13%). The results from this study can help Mediterranean farmers and researchers to develop new fertilization protocols to reach the goals of the “Farm to Fork” European strategy. Full article

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

Pesticide exposure gradients between occupational, para-occupational, and general populations remain poorly characterized in North African agricultural contexts. This study evaluates urinary pesticide levels among farmers, indirectly exposed individuals, and a control group in Morocco’s Fez-Meknes region. A cross-sectional survey measured pesticide concentrations using LC-MS/MS in urine samples collected from 154 adults residing in both rural and urban areas. A questionnaire was used to gather information from participants regarding factors that may elevate the risk of pesticide exposure. The results revealed that farmers exhibited the highest concentrations of pesticides in their urine, including compounds classified as Ia/Ib by the World Health Organization. Indirectly exposed individuals showed moderate levels of contamination, with notable detections such as dichlofluanid (22.13 µg/L), while the control group had residual traces of neonicotinoids, notably imidacloprid (2.05 µg/L). Multivariate analyses revealed several sociodemographic factors significantly associated with increased pesticide exposure. The main risk factors identified included low education, residence in an agricultural area, and the consumption of untreated water (wells/rivers). Conversely, wearing personal protective equipment was associated with reduced urinary concentrations. This study highlights intense occupational exposure among farmers, secondary environmental contamination among residents living near treated areas, and the widespread dispersion of pesticide residues into urban areas. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

While significant progress has been made in controlling point source pollution, agricultural non-point source pollution (AGNPSP) has emerged as a major contributor to global water pollution, posing a severe threat to ecological quality. According to China’s Second National Pollution Source Census, AGNPSP constitutes a substantial proportion of water pollution, making its mitigation a critical challenge. Identifying AGNPSP risk zones is essential for targeted management and effective intervention. This study focuses on Yongchuan District, a representative hilly–mountainous area in the Yangtze River Basin. Applying the landscape ecology “source–sink” theory, we selected seven natural factors influencing AGNPSP and constructed a minimum cumulative resistance model using remote sensing post-processing data. An attempt was made to classify the “source” and “sink” landscapes, and ultimately conduct a risk assessment of AGNPSP in Yongchuan District, identifying the key areas for AGNPSP control. Key findings include: 1. Vegetation coverage is the most significant natural factor affecting AGNPSP. 2. Extremely high- and high-risk zones cover 90% of Yongchuan, primarily concentrated in the central and southern regions, indicating severe AGNPSP pressure that demands urgent management. 3. The levels of ammonia nitrogen and total phosphorus in the typical sections are related to the risk levels of the corresponding sections. Consequently, the risk level of AGNPSP directly correlates with the pollutant concentrations measured in the sections. This study provides a robust scientific basis for AGNPSP risk assessment and targeted control strategies, offering valuable insights for pollution management in Yongchuan and similar regions. Full article

Soil compaction from mechanized agriculture is a major concern, as frequent machinery use degrades soil structure, reduces porosity, and ultimately impairs crop productivity. Among potential mitigation strategies to enhance soil resilience to machinery-induced compaction, biochar has shown promise in laboratory settings but remains untested under real field conditions. To address this, we monitored soil in a Tuscan vineyard where biochar was applied at 16 and 32 Mg ha⁻¹, compared to control, on both flat and sloped plots. Soil compaction was induced by 20 passes of a wheeled orchard tractor. Soil bulk density (BD) was measured before, immediately after, and one year following the initial passes, during which 19 additional machine passes occurred as part of the vineyard’s routine agronomic management. Initial results showed a significant BD increase (up to 12.8%) across all treatments, though biochar significantly limited soil compaction, regardless of the applied dose. After one year, in which the soil underwent further compaction, BD further increased across all treatments (up to 20.2%), with the steepest increase observed on the sloped terrain. At this stage, the mitigating effect of biochar on soil compaction was no longer evident. Our findings suggest that biochar may offer some short-term relief from compaction, but further investigations are needed to clarify its long-term effectiveness under field conditions. Full article

The integration of water resources, water environment, and water ecology (hereinafter “three-water”) is essential not only for addressing the current water crisis but also for achieving sustainable development. Chaohu Lake is an important water resource and ecological barrier in the middle and lower reaches of the Yangtze River, undertaking such functions as agricultural irrigation, urban water supply, and flood control and storage. Studying the performance of “three-water” in the Chaohu Lake Basin will help to understand the pollution mechanism and governance dilemma in the lake basin. It also provides practical experience and policy references for the ecological protection and high-quality development of the Yangtze River Basin. We used the DPSIR-TOPSIS model to analyze the performance of the river–lake system in the Chaohu Lake Basin and employed an obstacle model to identify factors influencing “three-water.” The results indicated that overall governance and performance of the “three-water” in the Chaohu Lake Basin exhibited an upward trend from 2011 to 2022. Specifically, the obstacle degree of driving force decreased by 19.6%, suggesting that economic development enhanced governance efforts. Conversely, the obstacle degree of pressure increased by 34.4%, indicating continued environmental stress. The obstacle degree of state fluctuated, showing a decrease of 13.2% followed by an increase of 3.8%, demonstrating variability in the effectiveness of water resource, environmental, and ecological management. Additionally, the obstacle degree of impact declined by 12.8%, implying the reduced efficacy of governmental measures in later stages. Response barriers decreased by 5.8%. Variations in the obstacle degree of response reflected differences in response capacities. Spatially, counties and districts at the origins of major rivers and their lake outlets showed lower performance levels in “three-water” management compared to other regions in the basin. Notably, Wuwei City and Feidong County exhibited better governance performance, while Feixi County and Chaohu City showed lower performance levels. Despite significant progress in water resource management, environmental improvement, and ecological restoration, further policy support and targeted countermeasures remain necessary. Counties and districts should pursue coordinated development, leverage the radiative influence of high-performing areas, deepen regional collaboration, and optimize, governance strategies to promote sustainable development. Full article

Agriculture and animal feeding operations are responsible for 87% of ammonia emissions in the UK. Controlling NH₃ concentrations below 20 ppm is crucial to preserve workers’ and livestock’s well-being. Therefore, ammonia control systems are required for maintaining adequate air quality in livestock facilities. This study assessed the ammonia reduction efficiency of a novel air pollution abatement (APA) system used in a pig farm building. The monitoring duration was 11 weeks. The results were compared with the baseline from a previous pig cycle during the same time of year in 2023. A ventilation-controlled room was monitored during a two-phase campaign, and the actual ammonia concentrations were measured at different locations within the site and at the inlet/outlet of the APA system. A 98% ammonia reduction was achieved at the APA outlet through NH₃ absorption in tap water. Ion chromatography analyses of farm water samples revealed NH₃ concentrations of up to 530 ppm within 83 days of APA operation. Further scanning electron microscopy and energy-dispersive X-ray inspections revealed the presence of salts and organic/inorganic matter in the solid residues. This research can contribute to meeting current ammonia regulations (NECRs), also by reusing the process water as a potential nitrogen fertiliser in agriculture. Full article