Search Results (135)

The activities of birds present increasing challenges in agriculture, aviation, and environmental conservation. This has led to economic losses, safety risks, and ecological imbalances. Attempts have been made to address the problem, with traditional deterrent methods proving to be labour-intensive, environmentally unfriendly, and ineffective over time. Advances in artificial intelligence (AI) and the Internet of Things (IoT) present opportunities for enabling automated real-time bird detection and repellence. This study reviews recent developments (2020–2025) in AI-driven bird detection and repellence systems, emphasising the integration of image, audio, and multi-sensor data in IoT and edge-based environments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used, with 267 studies initially identified and screened from key scientific databases. A total of 154 studies met the inclusion criteria and were analysed. The findings show the increasing use of convolutional neural networks (CNNs), YOLO variants, and MobileNet in visual detection, and the growing use of lightweight audio-based models such as BirdNET, MFCC-based CNNs, and TinyML frameworks for microcontroller deployment. Multi-sensor fusion is proposed to improve detection accuracy in diverse environments. Repellence strategies include sound-based deterrents, visual deterrents, predator-mimicking visuals, and adaptive AI-integrated systems. Deployment success depends on edge compatibility, power efficiency, and dataset quality. The limitations of current studies include species-specific detection challenges, data scarcity, environmental changes, and energy constraints. Future research should focus on tiny and lightweight AI models, standardised multi-modal datasets, and intelligent, behaviour-aware deterrence mechanisms suitable for precision agriculture and ecological monitoring. Full article

Addressing the urgent need to decarbonise aviation and valorise agricultural waste, this paper investigates the production of Sustainable Aviation Fuel (SAF) from corn stover. A preliminary evaluation based on a literature review indicates that among various conversion technologies, fast pyrolysis (FP) emerged as the most promising option, offering the highest fuel yield (22.5%) among various pathways, a competitive potential minimum fuel selling price (MFSP) of 1.78 USD/L, and significant greenhouse gas savings of up to 76%. Leveraging Aspen Plus simulation, SAF production via FP was rigorously designed and optimised, focusing on the heat integration strategy within the process to minimise utility consumption and ultimately the total cost. Consequently, the produced fuel exceeded the American Society for Testing and Materials (ASTM) limit for the final boiling point, rendering it unsuitable as a standalone jet fuel. Nevertheless, it achieves regulatory compliance when blended at a rate of up to 10% with conventional jet fuel, marking a practical route for early adoption. Energy optimisation through pinch analysis integrated four hot–cold stream pairs, eliminating external heating, reducing cooling needs by 55%, and improving sustainability and efficiency. Economic analysis revealed that while heat integration slashed utility costs by 84%, the MFSP only decreased slightly from 2.35 USD/L to 2.29 USD/L due to unchanging material costs. Sensitivity analysis confirmed that hydrogen, catalyst, and feedstock pricing are the most influential variables, suggesting targeted reductions could push the MFSP below 2 USD/L. In summary, this work underscores the technical and economic viability of corn stover-derived SAF, providing a promising pathway for sustainable aviation and waste valorisation. While current limitations restrict fuel quality during full substitution, the results affirm the feasibility of SAF blending and present a scalable, low-carbon pathway for future development. Full article

The use of unmanned aerial vehicle (UAV) pesticide spraying technology in precision agriculture is becoming increasingly important. However, traditional spraying methods struggle to address the precision application need caused by the canopy differences of fruit trees in orchards. This study proposes a UAV orchard variable-rate spraying method based on canopy volume. A DJI M300 drone equipped with LiDAR was used to capture high-precision 3D point cloud data of tree canopies. An improved progressive TIN densification (IPTD) filtering algorithm and a region-growing algorithm were applied to segment the point cloud of fruit trees, construct a canopy volume-based classification model, and generate a differentiated prescription map for spraying. A distributed multi-point spraying strategy was employed to optimize droplet deposition performance. Field experiments were conducted in a citrus (Citrus reticulata Blanco) orchard (73 trees) and a litchi (Litchi chinensis Sonn.) orchard (82 trees). Data analysis showed that variable-rate treatment in the litchi area achieved a maximum canopy coverage of 14.47% for large canopies, reducing ground deposition by 90.4% compared to the continuous spraying treatment; variable-rate treatment in the citrus area reached a maximum coverage of 9.68%, with ground deposition reduced by approximately 64.1% compared to the continuous spraying treatment. By matching spray volume to canopy demand, variable-rate spraying significantly improved droplet deposition targeting, validating the feasibility of the proposed method in reducing pesticide waste and environmental pollution and providing a scalable technical path for precision plant protection in orchards. Full article

Replacing chemical fertilizers with organic alternatives represents a viable strategy for enhancing agricultural productivity. The optimized integration of both fertilizer types can reduce the chemical input while improving soil conditions. However, the specific impacts of combined organic and inorganic fertilization on soil quality and crop performance require further investigation. To address this, a two-year field experiment was conducted to examine the effects of varying ratios of organic fertilizer substitution on wheat growth, grain yield, nutrient uptake, and soil quality. The results showed that the application of a 100% organic fertilizer combined with a 90% chemical fertilizer significantly increased the wheat biomass and grain yield. In terms of the nutrient uptake efficiency, the aboveground uptake of nitrogen (N), phosphorus (P), and potassium (K) increased significantly by 29.2%, 29.0%, and 56.5%, respectively. The nutrient use efficiency was also improved, with increases of 30.4% for N, 21.1% for P, and 47.7% for K. The partial factor productivity, total nutrient uptake, and the translocation efficiency of N, P, and K were all significantly enhanced. The soil quality was also markedly improved, with increases in both the soil organic matter and nutrient content. In conclusion, substituting chemical fertilizers with organic fertilizers improves the soil moisture and organic matter content, thereby enhancing the total uptake and translocation efficiency of nitrogen, phosphorus, and potassium. This leads to increased nutrient content in wheat grains, resulting in higher yields and improved grain quality. Moreover, this study provides practical guidance for wheat production and supports policy objectives related to sustainable agriculture, reduced chemical fertilizer use, and improved food security. Full article

In mountainous citrus orchards, the application of conventional ground sprayers for the control of citrus red mite (Panonychus citri) is often constrained by complex terrain and low operational efficiency. The Unmanned Aerial Spraying System (UASS), due to its low-altitude, low-volume, and high-maneuverability characteristics, has emerged as a promising alternative for pest management in such challenging environments. To evaluate the spray performance and field efficacy of different UASS types in controlling P. citri, five representative UASS models (JX25, DP, T1000, E-A2021, and T20), four mainstream pesticide formulations, and four novel tank-mix adjuvants were systematically assessed in a field experiment conducted in a typical hilly citrus orchard. The results showed that T20 delivered the best overall spray deposition, with upper canopy coverage reaching 10.63%, a deposition of 3.01 μg/cm², and the highest pesticide utilization (43.2%). E-A2021, equipped with a centrifugal nozzle, produced the finest droplets and highest droplet density (120.3–151.4 deposits/cm²), but its deposition and coverage were lowest due to drift. Nonetheless, it exhibited superior penetration (dIPR 72.3%, dDPR 73.5%), facilitating internal canopy coverage. T1000, operating at higher flight parameters, had the weakest deposition. Formulation type had a limited impact, with microemulsions (MEs) outperforming emulsifiable concentrates (ECs) and suspension concentrates (SCs). All adjuvants improved spray metrics, especially Yimanchu and Silwet, which enhanced pesticide utilization to 46.8% and 46.4% for E-A2021 and DP, respectively. Adjuvant use increased utilization by 4.6–11.9%, but also raised ground losses by 1.5–4.2%, except for Yimanchu, which reduced ground loss by 2.3%. In terms of control effect, the rapid efficacy (1–7 days after application, DAA) of UASS spraying was slightly lower than that of ground sprayers—electric spray gun (ESG), while its residual efficacy (14–25 DAA) was slightly higher. The addition of adjuvants improved both rapid and residual efficacy, making it comparable to or even better than ESG. E-A2021 with 5% abamectin·etoxazole ME (5A·E) and Yimanchu achieved 97.4% efficacy at 25 DAA. Among UASSs, T20 showed the rapid control, while E-A2021 outperformed JX25 and T1000 due to finer droplets effectively targeting P. citri. In residual control (14–25 DAA), JX25 with 45% bifenazate·etoxazole SC (45B·E) was most effective, followed by T20. 5A·E and 45B·E showed better residual efficacy than abamectin-based formulations, which declined more rapidly. Adjuvants significantly extended control duration, with Yimanchu performing best. This study demonstrates that with optimized spraying parameters, nozzle types, and adjuvants, UASSs can match or surpass ground spraying in P. citri control in hilly citrus orchards, providing valuable guidance for precision pesticide application in complex terrain. Full article

Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R² values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms. Full article

The Magallanes region, in southern Chile, is positioned as a strategic hub for the production of green hydrogen (GH2), green ammonia, and synthetic fuels, thanks to its exceptional wind potential and commitment to sustainability. This article analyzes the opportunities and challenges of these energy vectors in the context of global decarbonization, highlighting the key role of the Magallanes region in the energy transition. Green hydrogen production, through wind-powered electrolysis, takes advantage of the region’s constant, high-speed winds, enabling competitive, low-emission generation. In turn, green ammonia, derived from GH2, emerges as a sustainable alternative for the agricultural industry and maritime transport, while synthetic fuels (e-fuels) offer a solution for sectors that are difficult to electrify, such as aviation. The sustainability approach addresses not only emissions reduction but also the responsible use of water resources, the protection of biodiversity, and integration with local communities. The article presents the following structure: (i) introduction, (ii) wind resource potential, (iii) water resource potential, (iv) different forms of hydrogen and its derivatives production (green hydrogen, green ammonia, and synthetic fuels), (v) pilot-scale demonstration plant for Haru Oni GH2 production, (vi) future industrial-scale GH2 production projects, (vii) discussion, and (viii) conclusions. In addition, the article discusses public policies, economic incentives, and international collaborations that promote these projects, positioning Magallanes as a clean energy export hub. Finally, the article concludes that the region can lead the production of green fuels, contributing to global energy security and the fulfillment of the Sustainable Development Goals (SDGs). However, advances in infrastructure, regulation, and social acceptance are required to guarantee a balanced development between technological innovation and environmental conservation. Full article

Apple yield estimation is a critical task in precision agriculture, challenged by complex tree canopy structures, growth stage variability, and orchard heterogeneity. In this study, we apply multi-source feature fusion by combining vegetation indices from UAV remote sensing imagery, structural feature ratios from ground-based fruit tree images, and leaf chlorophyll content (SPAD) to improve apple yield estimation accuracy. The DeepLabv3+ network, optimized with Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA), improved fruit tree image segmentation accuracy. Four structural feature ratios were extracted, visible-light and multispectral vegetation indices were calculated, and feature selection was performed using Pearson’s correlation coefficient analysis. Yield estimation models were constructed using k-nearest neighbors (KNN), partial least squares (PLS), random forest (RF), and support vector machine (SVM) algorithms under both single feature sets and combined feature sets (including vegetation indices, structural feature ratios, SPAD, vegetation indices + SPAD, vegetation indices + structural feature ratios, structural feature ratios + SPAD, and the combination of all three). The optimized CBAM-ECA-DeepLabv3+ model achieved a mean Intersection over Union (mIoU) of 0.89, an 8% improvement over the baseline DeepLabv3+, and outperformed U2Net and PSPNet. The SVM model based on multi-source feature fusion achieved the highest apple yield estimation accuracy in small-scale orchard sample plots (R² = 0.942, RMSE = 12.980 kg). This study establishes a reliable framework for precise fruit tree image segmentation and early yield estimation, advancing precision agriculture applications. Full article

This study presents a comprehensive solution for precise and timely pest monitoring in field environments through the development of an advanced rice pest detection system based on the YOLO-RMD model. Addressing critical challenges in real-time detection accuracy and environmental adaptability, the proposed system integrates three innovative components: (1) a novel Receptive Field Attention Convolution module enhancing feature extraction in complex backgrounds; (2) a Mixed Local Channel Attention module balances local and global features to improve detection precision for small targets in dense foliage; (3) an enhanced multi-scale detection architecture incorporating Dynamic Head with an additional detection head, enabling simultaneous improvement in multi-scale pest detection capability and coverage. The experimental results demonstrate a 3% accuracy improvement over YOLOv8n, achieving 98.2% mean Average Precision at 50% across seven common rice pests while maintaining real-time processing capabilities. This integrated solution addresses the dual requirements of precision and timeliness in field monitoring, representing a significant advancement for agricultural vision systems. The developed framework provides practical implementation pathways for precision pest management under real-world farming conditions. Full article

Aiming at the technical bottleneck of monitoring rice stalk, pest, and grass damage in the middle and lower parts of rice, this paper proposes a UAV-based image information acquisition method and disease prediction algorithm model, which provides an efficient and low-cost solution for the accurate early monitoring of rice diseases, and helps improve the scientific and intelligent level of agricultural disease prevention and control. Firstly, the UAV image acquisition system was designed and equipped with an automatic telescopic rod, 360° automatic turntable, and high-definition image sensing equipment to achieve multi-angle and high-precision data acquisition in the middle and lower regions of rice plants. At the same time, a path planning algorithm and ant colony algorithm were introduced to design the flight layout path of the UAV and improve the coverage and stability of image acquisition. In terms of image information processing, this paper proposes a multi-dimensional data fusion scheme, which combines RGB, infrared, and hyperspectral data to achieve the deep fusion of information in different bands. In disease prediction, the YOLOv8 target detection algorithm and lightweight Transformer network are adopted to determine the detection performance of small targets. The experimental results showed that the average accuracy of the YOLOv8 model (mAP@0.5) in the detection of rice curl disease was 90.13%, which was much higher than that of traditional methods such as Faster R-CNN and SSD. In addition, 1496 disease images and autonomous data sets were collected to verify that the system showed good stability and practicability in field environment. Full article

In commercial poultry farming, automated behavioral monitoring systems hold significant potential for optimizing production efficiency and improving welfare outcomes at scale. The behavioral detection of free-range broilers matters for precision farming and animal welfare. Current research often focuses on either behavior detection or individual tracking, with few studies exploring their connection. To continuously track broiler behaviors, the Only Detect Broilers Once (ODBO) method is proposed by linking behaviors with identity information. This method has a behavior detector, an individual Tracker, and a Connector. First, by integrating SimAM, WIOU, and DIOU-NMS into YOLOv8m, the high-performance YOLOv8-BeCS detector is created. It boosts P by 6.3% and AP by 3.4% compared to the original detector. Second, the designed Connector, based on the tracking-by-detection structure, transforms the tracking task, combining broiler tracking and behavior recognition. Tests on sort-series trackers show HOTA, MOTA, and IDF1 increase by 27.66%, 28%, and 27.96%, respectively, after adding the Connector. Fine-tuning experiments verify the model’s generalization. The results show this method outperforms others in accuracy, generalization, and convergence speed, providing an effective method for monitoring individual broiler behaviors. In addition, the system’s ability to simultaneously monitor individual bird welfare indicators and group dynamics could enable data-driven decisions in commercial poultry farming management. Full article

Accurate identification of rice diseases is crucial for improving rice yield and ensuring food security. In this study, we constructed an image dataset containing six classes of rice diseases captured under real field conditions to address challenges such as complex backgrounds, varying lighting, and symptom similarities. Based on the MobileViT-XXS architecture, we proposed an enhanced model named MobileViT-DAP, which integrates Channel Attention (CA), Efficient Channel Attention (ECA), and PoolFormer blocks to achieve precise classification of rice diseases. The experimental results demonstrated that the improved model achieved superior performance with 0.75 M Params and 0.23 G FLOPs, ensuring computational efficiency while maintaining high classification accuracy. On the testing set, the model achieved an accuracy of 99.61%, a precision of 99.64%, a recall of 99.59%, and a specificity of 99.92%. Compared to traditional lightweight models, MobileViT-DAP showed significant improvements in model complexity, computational efficiency, and classification performance, effectively balancing lightweight design with high accuracy. Furthermore, visualization analysis confirmed that the model’s decision-making process primarily relies on lesion-related features, enhancing its interpretability and reliability. This study provides a novel perspective for optimizing plant disease recognition tasks and contributes to improving plant protection strategies, offering a solution for accurate and efficient disease monitoring in agricultural applications. Full article

In rice, infections caused by Pantoea ananatis or Enterobacter asburiae closely resemble the bacterial blight induced by Xanthomonas oryzae pv. oryzae, yet they differ in drug resistance and management strategies. This study explores the potential of combining hyperspectral imaging (HSI) with machine learning for the rapid and accurate detection of rice bacterial blight symptoms caused by various pathogens. One-dimensional convolutional neural networks (1DCNNs) were employed to construct a classification model, integrating various spectral preprocessing techniques and feature selection algorithms for comparison. To enhance model robustness and mitigate overfitting due to limited spectral samples, generative adversarial networks (GANs) were utilized to augment the dataset. The results indicated that the 1DCNN model, after feature selection using uninformative variable elimination (UVE), achieved an accuracy of 86.11% and an F1 score of 0.8625 on the five-class dataset. However, the dominance of Pantoea ananatis in mixed bacterial samples negatively impacted classification performance. After removing mixed-infection samples, the model attained an accuracy of 97.06% and an F1 score of 0.9703 on the four-class dataset, demonstrating high classification accuracy across different pathogen-induced infections. Key spectral bands were identified at 420–490 nm, 610–670 nm, 780–850 nm, and 910–940 nm, facilitating pathogen differentiation. This study presents a precise, non-destructive approach to plant disease detection, offering valuable insights into disease prevention and management in precision agriculture. Full article

In recent years, extensive research has been conducted on pesticide application technology using unmanned aerial spraying systems (UASS) due to their efficiency and ability to overcome terrain obstacles. However, the coupling effect between the operational parameters of UASS and their influence on droplet deposition has not been sufficiently studied. A thorough and methodical analysis is essential to assess the deposition performance and drift risk of UASS. This study evaluated the spraying performance of an electric six-rotor UASS in wheat fields in Zibo between 2021 and 2022, focusing on three operational modes determined by flight speed and flow rate. Furthermore, the individual effects of these two parameters on droplet deposition quality and drift risk were explored. Based on the deposition quality of in-swath droplets and the drift degree after application, the results demonstrate that the optimal comprehensive characteristics of droplet deposition occur at a flight speed of 4.5 m/s, a flow rate of 2.025 L/min, and a spray amount of 1 L/ha. The increase in spray flow rate (2.475 L/min) results in a 3.92-fold enhancement in the deposition rate within the spray area compared with that of group of the minimum spray flow rate (1.575 L/min). A higher flight speed (5.5 m/s) improves the uniformity of droplet deposition, with the coefficient of variation decreases by 25.2% compared with that of the minimum flight speed (3.5 m/s), and this higher flight speed leads to a drift distance of 28.8 m. Full article

In this paper, we investigate the particle deposition distribution characteristics in granular fertilizer spreading, establish a relationship model between operational parameters and particle deposition distribution, and design an unmanned aerial vehicle (UAV) fertilizer particle deposition prediction system based on neural network decision making, which provides a decision-making basis for the variable fertilizer application model under multifactorial interactions. The particle deposition distribution data under different operating parameters were obtained by EDEM simulation and data superposition methods, and a generalized regression neural network (GRNN) based on a genetic algorithm (GA) was used to establish the prediction model of particle deposition, which was validated by bench test. The results show that the prediction accuracy and training effect of the GA-GRNN model are better than those of the GRNN, with a coefficient of determination of 0.839, and that the results of the GA-GRNN model are closer to the actual data when predicting the effective amplitude of the deposition amount, which is more accurate. The bench-scale validation test shows that the simulation is basically consistent with the actual measured deposition amount, and the deposition curve is normally distributed with a lateral error of about 3%. The results validate the reliability of the data superposition method for particle deposition distribution and the feasibility of the GA-GRNN model in multifactor prediction, which provides a theoretical basis and practical guidance for precision fertilizer application operations using agricultural UAVs. Full article