Search Results (202)

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

Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. However, existing methods often suffer from poor generalization, background interference, and inadequate real-time performance. To overcome these limitations, we developed an end-to-end defect recognition framework, structured as a three-stage process: blade localization using YOLOv5, robust feature extraction via the large vision model DINOv2, and defect classification using a Stochastic Configuration Network (SCN). Unlike conventional CNN-based approaches, the use of DINOv2 significantly improves the capability for representation under complex textures. The experimental results reveal that the proposed method achieved a classification accuracy of 97.8% and an average inference time of 19.65 ms per image, satisfying real-time requirements. Compared to traditional methods, this framework provides a more scalable, accurate, and efficient solution for the intelligent inspection and maintenance of wind turbine blades. Full article

Background: Contemporary agriculture heavily relies on synthetic chemicals to ensure high yields and food security; however, their overuse has led to health issues and the development of pesticide resistance in pests. Researchers are now exploring natural, eco-friendly alternatives for pest control. Methods: This study evaluated two ethanol-based formulations (1.25% and 2.50%, v/v) derived from the tangerine peel (Citrus reticulata L. var. Clementina) against conventional chemical treatments and an untreated control group in the cultivation of potatoes (Solanum tuberosum L. var. Capiro). A randomised block design was used, with three blocks per treatment containing 45 plants. The experiment was conducted during the wet season (February–April 2023). Results: According to visual inspections and yellow traps, following weekly application from days 30 to 105 post-planting to monitor pest (e.g., Frankliniella occidentalis, Aphididae) and beneficial insect (e.g., Coccinellidae, Apis mellifera) populations, the 2.50% formulation performed similarly to chemical treatments against pests, whilst being harmless to beneficial insects. Post-harvest analysis showed that the formulations achieved 73% of conventional yields, with comparable tuber damage and levels of Premnotrypes vorax larvae. Conclusions: Toxicological tests confirmed the eco-friendliness of the formulations, making them suitable for small-scale Andean ‘chakras’ in organic farming and honey production, without the use of chemicals. Full article

This study investigated the perceptions of animal welfare and the consumption of alternative protein sources among future professionals in agronomy, food science, and veterinary medicine. A sample of 769 participants from three faculties [ESALQ (“Luiz de Queiroz” College of Agriculture), FZEA (School of Animal Science and Food Engineering), and FMVZ (School of Veterinary Medicine and Animal Science)] of the University of São Paulo was used. These faculties have different teaching focuses: agronomy, food and animal production, and veterinary, respectively. A relationship between the perception of animal welfare and alternative sources of protein based on the participants’ educational background was verified, specifically: (i) participants from the FZEA (food science) and FMVZ (veterinary) units would be interested in consuming farmed meat and expressed interest in trying it; (ii) students from the ESALQ (agronomy) have a low level of knowledge about animal welfare and are not very interested in knowing how animals are reared, and few participants attribute the presence of the health inspection seal as influencing their purchasing intention; (iii) participants, regardless of their academic background, did not express an intention to reduce their red meat consumption; (iv) the ESALQ was the campus which showed the most skepticism about animal sentience; (v) most participants from the FMVZ and FZEA reported being willing to pay 4–5% more for products that guarantee animal welfare. The findings suggest that the academic context influences individuals’ perceptions and food choices, highlighting the need for educational strategies that foster a greater awareness of animal welfare, encourage the adoption of more sustainable practices, and promote the acceptance of alternative protein sources within the agri-food sector. Full article

With the increasing urgency for digital transformation in large-scale caged layer farms, traditional methods for monitoring the environment and chicken health, which often rely on human experience, face challenges related to low efficiency and poor real-time performance. In this study, we focused on caged layer chickens and proposed an improved abnormal beak detection model based on the You Only Look Once v8 (YOLOv8) framework. Data collection was conducted using an inspection robot, enhancing automation and consistency. To address the interference caused by chicken cages, an Efficient Multi-Scale Attention (EMA) mechanism was integrated into the Spatial Pyramid Pooling-Fast (SPPF) module within the backbone network, significantly improving the model’s ability to capture fine-grained beak features. Additionally, the standard convolutional blocks in the neck of the original model were replaced with Grouped Shuffle Convolution (GSConv) modules, effectively reducing information loss during feature extraction. The model was deployed on edge computing devices for the real-time detection of abnormal beak features in layer chickens. Beyond local detection, a digital twin remote monitoring system was developed, combining three-dimensional (3D) modeling, the Internet of Things (IoT), and cloud-edge collaboration to create a dynamic, real-time mapping of physical layer farms to their virtual counterparts. This innovative approach not only improves the extraction of subtle features but also addresses occlusion challenges commonly encountered in small target detection. Experimental results demonstrate that the improved model achieved a detection accuracy of 92.7%. In terms of the comprehensive evaluation metric (mAP), it surpassed the baseline model and YOLOv5 by 2.4% and 3.2%, respectively. The digital twin system also proved stable in real-world scenarios, effectively mapping physical conditions to virtual environments. Overall, this study integrates deep learning and digital twin technology into a smart farming system, presenting a novel solution for the digital transformation of poultry farming. Full article

Accurate classification of plant disease by farming robot cameras can increase crop yield and reduce unnecessary agricultural chemicals, which is a fundamental task in the field of sustainable and precision agriculture. However, until now, disease classification has mostly been performed by manual methods, such as visual inspection, which are labor-intensive and often lead to misclassification of disease types. Therefore, previous studies have proposed disease classification methods based on machine learning or deep learning techniques; however, most did not consider real-world plant images with complex backgrounds and incurred high computational costs. To address these issues, this study proposes a computationally effective residual convolutional attention network (RCA-Net) for the disease classification of plants in field images with complex backgrounds. RCA-Net leverages attention mechanisms and multiscale feature extraction strategies to enhance salient features while reducing background noises. In addition, we introduce fractal dimension estimation to analyze the complexity and irregularity of class activation maps for both healthy plants and their diseases, confirming that our model can extract important features for the correct classification of plant disease. The experiments utilized two publicly available datasets: the sugarcane leaf disease and potato leaf disease datasets. Furthermore, to improve the capability of our proposed system, we performed fractal dimension estimation to evaluate the structural complexity of healthy and diseased leaf patterns. The experimental results show that RCA-Net outperforms state-of-the-art methods with an accuracy of 93.81% on the first dataset and 78.14% on the second dataset. Furthermore, we confirm that our method can be operated on an embedded system for farming robots or mobile devices at fast processing speed (78.7 frames per second). Full article

In this paper, we present the latest research results on the analysis of images taken during the condition assessment of solar cells and solar power plants. We aimed to summarize the most recent articles for 2024 and 2025. The annual volume of solar panels produced is expected to increase in the future. As imaging condition assessment technologies develop, the convolutional neural network models must follow this trend. In the field of real-time detection, CNN models will play an extremely important role because the faster any potential faults are identified, the quicker the response time during manufacturing and PV plant inspections. As part of CNN implementation in large PV power plants, IR and RGB imaging modes are very useful to detect failure sources. While IR imaging is useful in detecting heating from faults within PV panels or from nearby wiring, RGB imaging can detect mechanical defects such as broken glass planes, discolorations, and delamination. The implementation of these thus provides a higher chance of detecting solar panel damage and PV farms’ performance degradation or possible failure, resulting in a reduction in power generation interruptions. This will also allow faster and more efficient intervention and decision-making by operators in case of problems. Full article

This study aimed to evaluate the effects of no tail docking on the performance, health, and behavior of piglets raised under commercial conditions in Brazil. The study included 768 weaned piglets from the Pietrain synthetic line, randomly divided into two groups: DT = the final third part of the tail-docked (n = 384) and NTD = non-tail-docked (n = 384). Tail docking was performed on day two using an electrocautery clipper for piglets from the DT group, and both groups were subjected to standard environmental enrichment with branched chains. In cases of tail biting, a contingency plan was adopted to mitigate this problem by enriching the pen with a sisal rope. Behavioral measurements were performed using scan sampling. Tail biting, reactivity to humans, and health were assessed using a methodology adapted from the Welfare Quality Protocol^®. The piglets were weighed at 140 days of age and inspected according to the parameters established by the Pig Genealogical Registration Service to be used as reproduction animals. The off-test rate was calculated based on the total number of piglets approved for animal use relative to the total number evaluated. During the nursery stage, the NDT piglets showed a trend toward significance (p = 0.07) toward a higher occurrence of tail biting than the DT piglets and exhibited a higher incidence of severe lesions. They also engaged more frequently (p < 0.05) in exploratory behavior, interacting with branched chains and sisal rope, than the DT piglets. During the finishing phase, tail biting was observed only in the NDT piglets (p = 0.001). The NDT piglets that did not require the contingency plan exhibited lower fear responses (p = 0.02) during human interactions in the nursery phase than the DT piglets. Conversely, the NDT piglets that required a contingency plan showed higher fear levels (p < 0.001). Productivity performance was not affected (p > 0.05), and new cases of tail biting ceased after the contingency plan was implemented. The number of animals that died or were removed did not differ between the treatments (p > 0.05). In conclusion, managing piglets with intact tails on commercial farms presents a significant welfare challenge. By contrast, docking the final third of the tail, in accordance with regulations, was associated with fewer negative welfare outcomes, even when best management practices were applied. Full article

In the Philippines, dragon fruit has become an essential, high-value crop and is important to the country’s economy. However, due to inefficient manual inspection methods, farmers need help with quality control and market preparation. Therefore, we developed an automated, real-time visual identification system to detect the maturity, size, and defects of dragon fruits. Advanced deep learning models (EfficientNet and YOLOV8) were trained on a diverse dataset of dragon fruit images collected from online sources and a local farm using DSLR and smartphone cameras. A Raspberry Pi 4B with an HQ camera and wide-angle lens was used as a cost-effective and accessible device for farmers. The developed system showed an accuracy of 93.33% for maturity and size classification, 96.67% for defect detection, and an overall accuracy of 83.33%. Regarding accuracy and reliability, the developed method presents a technological advancement for dragon fruit identification and classification. Full article

Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. The dynamic relevance and sparsity-based feature selector (DRS-FS) improves feature ranking and reduces redundancy. Spectral normalization, spatial–temporal alignment, and dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using federated learning to preserve privacy and capture regional trends. A huge, open-access agricultural dataset from varied environmental circumstances was used for simulation experiments. The suggested approach improves on conventional models like ResNet, DenseNet, and the vision transformer with a 98.9% classification accuracy and 99.3% AUC. The LeViT-ResUNet system is scalable and sustainable for privacy-preserving precision agriculture because of its high generalization, low latency, and communication efficiency. This study lays the groundwork for real-time, intelligent agricultural monitoring systems in diverse, resource-constrained farming situations. Full article

Operation and maintenance (O&M) activities represent a significant share of the levelized cost of energy (LCOE) for offshore wind farms (OWFs), making cost reduction a key priority. Robotic-based solutions, leveraging aerial and underwater vehicles in a cooperative framework, offer the potential to optimize O&M logistics and reduce costs. Additionally, the deployment of persistent autonomous robotic systems can minimize the need for human intervention, enhancing efficiency. This study presents the development of an O&M cost calculator that integrates multiple modules: a weather forecast module to account for meteorological uncertainties, a failure module to model OWF failures, a maintenance module to estimate costs for both planned and unplanned activities, and a power module to quantify downtime-related losses. A forward-looking comparative economic analysis is conducted, assessing the cost-effectiveness of human-based versus robot-based inspection, maintenance, and repair (IMR) activities. The findings highlight the economic viability of robotic solutions in offshore wind O&M, supporting their potential role in reducing operational expenditures and improving energy production efficiency. Full article

Accurately counting chickens in densely packed cages is a major challenge in large-scale poultry farms. Traditional manual counting methods are labor-intensive, costly, and prone to errors due to worker fatigue. Furthermore, current deep learning models often struggle with accuracy in caged environments because they are not well-equipped to handle occlusions. In response, we propose the You Only Look Once-Chicken Counting Algorithm (YOLO-CCA). YOLO-CCA improves the YOLOv8-small model by integrating the CoordAttention mechanism and the Reversible Column Networks backbone. This enhancement improved the YOLOv8-small model’s F1 score to 96.7% (+3%) and average precision_50:95 to 80.6% (+2.8%). Additionally, we developed a threshold-based continuous frame inspection method that records the maximum number of chickens per cage with corresponding timestamps. The data are stored in a cloud database for reliable tracking during robotic inspections. The experiments were conducted in an actual poultry farming environment, involving 80 cages with a total of 493 chickens, and showed that YOLO-CCA raised the chicken recognition rate to 90.9% (+13.2%). When deployed on a Jetson AGX Orin industrial computer using TensorRT, the detection speed increased to 90.9 FPS (+57.6 FPS), although the recognition rate slightly decreased to 93.2% (−2.9%). In summary, YOLO-CCA reduces labor costs, improves counting efficiency, and supports intelligent poultry farming transformation. Full article

Highly pathogenic avian influenza (HPAI), H5N1 strain, began to circulate in the United States on 8 February 2022. The state of Iowa lost the most domestic poultry to HPAI from February 2022–December 2023. This study conducted preliminary evaluations on two environmental risk factors, (inland water surface area, Canada geese abundance) and the availability of the data needed to evaluate them. Higher Canada geese abundance was significantly associated (X² = 4.29, p = 0.04) with HPAI negative counties. Farm location data were unavailable, limiting our analysis. Van den Broeck et al.’s framework was used to evaluate the available data. Outcome data from Animal and Plant Health Inspection Service (APHIS) had the highest data quality score (11). Canada geese and inland water surface area are predictors worth evaluating, but poultry farm location data are needed for a comprehensive evaluation. Full article

Smallholder potato farmers in Ethiopia do not realize the theoretical yield potential of the crop because they do not benefit from the advantages of using quality seed potato of improved varieties. The high disease incidence in seed potatoes has large implications on the potato farming system since the country lacks appropriate seed quality assurance mechanisms. Seed potato quality assurance relies more on the technical support provided by the national research and extension systems than the official seed certification agency. This paper elaborates systematic challenges and opportunities within the potato seed system and poses two research questions: (1) What type of seed quality assurance mechanisms (informal, quality declared, certified) are under implementation in Ethiopia? (2) How does the current seed quality assurance system operate in terms of reliability, accessibility, and quality standards to deliver quality seed potato? The data were collected through face-to-face in-depth key informant interviews with various seed regulatory laboratory managers and technicians in the Oromia, SNNP, and SWEP regions in the main seed- and ware-producing areas of Ethiopia. This was complemented by a comprehensive analysis of relevant documents. The findings show that currently there is no established procedure in place to officially certify early-generation seed potatoes. Two out of six seed quality control laboratories assessed for this study inspected seed potato fields in 2021 but as quality declared seed (QDS), and approved the fields inspected based on visual inspection alone. Our study revealed a weak linkage between early-generation seed (EGS) potato producers, commercial, and QDS seed potato producers, and seed quality control laboratories. Seed potato quality assurance operations were carried out by only a few seed regulatory laboratories with several concerns raised over the effectiveness of quality standards since seed-borne diseases, such as bacterial wilt, have been found at high frequency in the country’s seed potato system. Hence, the current procedures and challenges call for the necessity of upgrading current quality assurance in seed potato certification. Our study underlines the need for policymakers, development partners, and researchers to collaborate and pool efforts to consider transforming the quality declared system to appropriate seed certification. We recommended that institutionalizing novel plant disease diagnostics into seed regulatory frameworks is needed for sustainable potato production and food security in Ethiopia. Full article

The reliable operation of photovoltaic (PV) systems is essential for sustainable energy production, yet their efficiency is often compromised by defects such as bird droppings, cracks, and dust accumulation. Automated defect detection is critical for addressing these challenges in large-scale solar farms, where manual inspections are impractical. This study evaluates three YOLO object detection models—YOLOv5, YOLOv8, and YOLOv11—on a comprehensive dataset to identify solar panel defects. YOLOv5 achieved the fastest inference time (7.1 ms per image) and high precision (94.1%) for cracked panels. YOLOv8 excelled in recall for rare defects, such as bird drops (79.2%), while YOLOv11 delivered the highest mAP@0.5 (93.4%), demonstrating a balanced performance across the defect categories. Despite the strong performance for common defects like dusty panels (mAP@0.5 > 98%), bird drop detection posed challenges due to dataset imbalances. These results highlight the trade-offs between accuracy and computational efficiency, providing actionable insights for deploying automated defect detection systems to enhance PV system reliability and scalability. Full article