Hasil untuk "Agricultural industries"

R. S. Riseh, M. G. Vazvani, Mohadeseh Hassanisaadi et al.

Cellulose is an organic compound belonging to polysaccharides. This biopolymer is made of glucose subunits. This compound plays an essential role in the structure and strength of plants. This polymer has biodegradable, biocompatible, and renewable properties. Agricultural wastes are excellent sources for cellulose extraction. Agricultural wastes are lignocellulosic materials

P. Naughton, R. Marchant, V. Naughton et al.

Synthetic surfactants are becoming increasingly unpopular in many applications due to previously disregarded effects on biological systems and this has led to a new focus on replacing such products with biosurfactants that are biodegradable and produced from renewal resources. Microbially derived biosurfactants have been investigated in numerous studies in areas including: increasing feed digestibility in an agricultural context, improving seed protection and fertility, plant pathogen control, antimicrobial activity, antibiofilm activity, wound healing and dermatological care, improved oral cavity care, drug delivery systems and anticancer treatments. The development of the potential of biosurfactants has been hindered somewhat by the myriad of approaches taken in their investigations, the focus on pathogens as source species and the costs associated with large‐scale production. Here, we focus on various microbial sources of biosurfactants and the current trends in terms of agricultural and biomedical applications.

Yakun Wang, Zhuo Zhang, Yuanyuan Li et al.

Heavy metal (HM) pollution in agricultural soils has arisen sharply in recent years. However, the impact of main factors on available HMs concentrations in agricultural soils of the three main industries (smelting, chemical and mining industry) is unclear. Herein, soil properties (pH, cation exchange capacity (CEC) and texture (sand, slit, clay)), total and available concentrations were concluded based on the results of 165 research papers from 2000 to 2023 in Web of Science database. In the three industries, the correlation and redundancy analysis were used to study the correlation between main factors and available concentrations, and quantitatively analyzed the contribution of each factor to available concentrations with gradient boosting decision tree model. The results showed that different factors had varying degrees of impact on available metals in the three main industries, and the importance of same factors varied in each industry, as for soil pH, it was most important for available Pb and Zn in the chemical industry, but the total concentrations were most important in the smelting and mining industry. There was no significant correlation between total and available concentrations. Soil properties involved in this paper (especially soil pH) were negatively correlated with available concentrations. This study provides effective guidance for the formulation of soil pollution control and risk assessment standards based on industry classification in the three major industrial impact areas.

Xiaoyang Liu, Xupeng Huang, Rongjin Zhu et al.

To improve the counting accuracy in dense rice seed scenarios, this study proposes a YOLOv5-based dense rice seed counting method that integrates C3CBAM and Soft-NMS. This method integrates the CBAM attention module into the shallow C3 modules of the backbone network to enhance image features. Additionally, it removes the original large and medium-sized object detection heads of YOLOv5 and adds a dedicated detection head for tiny rice seeds. For post-processing of model prediction data, the Soft-NMS algorithm is employed to replace standard Non-Maximum Suppression (NMS) and reduce missed detections. Finally, image acquisition, seed counting, and a user interface are integrated into a single system, enabling rice breeders to conduct seed counting tasks more intuitively and efficiently. Compared with the baseline YOLOv5 model, the recall and mAP@[0.5:0.95] of the improved model increase by 6.4 % and 5.7 %, respectively. Furthermore, this study designs experiments with three levels of seed density. In the intermediate-type rice seed samples, the detection accuracy reaches 100 % under light and moderate density conditions, while it maintains stable counting performance under heavy density conditions with an accuracy above 99.7 %. This work significantly enhances rice seed counting efficiency for researchers and facilitates rice variety improvement studies.

Cong Yao, Yidan Yang, Cai-Xia Li et al.

The potential for heavy metal (HM) pollution in agricultural soils adjacent to industries with elevated HM emissions has long been recognized. However, industries with relatively lower levels of HM emissions, such as alumina smelting and glass production, may still contribute to the pollution of surrounding agricultural soils through continuous, albeit low-level, emissions. Despite this, this issue has not garnered adequate attention thus far. Therefore, this study aimed to assess the extent of HM pollution in agricultural soils adjacent to an alumina smelting and a glass production factory, identifying contamination levels and potential sources through the analysis of input fluxes, isotope fingerprints, and receptor models. Results showed moderate cadmium (Cd) contamination in surface soil, exceeding standards at a rate of 86.36 %. Further analysis revealed that atmospheric deposition was the primary route for Cd input in both paddy fields (89.20 %) and dryland soils (91.61 %). Additionally, the δ114/110Cd values in surface soils indicated that dust played a role in influencing Cd levels in distant surface soils, while raw materials and slags were identified as primary sources near the factory. Industrial sources were considered the primary contributors of Cd in soil accounting for approximately 73.38 % and 82.67 %, respectively, according to the positive matrix factorization model (PMF) and absolute principal component scores-multiple linear regression model (APCS-MLR). Overall, this study underscores the importance of monitoring HMs from industries with relatively low emissions and provides a scientific basis for effectively managing HMs pollution in agricultural soils, ensuring the preservation of agricultural soil quality.

Yuguo Jiang, Ziyu Zhao, Xinjie Zhao

This paper employs the super-efficiency slack-based measure (SBM) model with undesirable outputs to measure the green development efficiency of the agricultural product circulation industry (APCI) across 26 provinces of China from 2013 to 2022, and applies the kernel density estimation method to reveal its spatio-temporal evolution characteristics. Furthermore, the Tobit model is utilized to analyze the factors influencing the green development efficiency of the APCI. The research reveals that: (1)The green development efficiency of China's APCI is at a medium level. In 2019, a significant spatial demarcation emerged in the green development efficiency of China's agricultural product circulation industry, with the efficiency highland shifting from North/Northeast China to Southern regions, thereby manifesting a new ''high-south, low-north'' efficiency configuration. (2) Interprovincial disparities initially narrowed and subsequently widened. Furthermore, the six major regions exhibited heterogeneous dynamic characteristics, while the green development efficiency of the APCI demonstrated spatial imbalance across provinces. (3) The openness to international market (OIM) exerts a statistically significant positive effect on the green development efficiency of the APCI. Conversely, industrial structure (IS) and agricultural pollution level (API) demonstrate inhibitory effects on APCI's green development efficiency. This study deepens the understanding of APCI's green development efficiency, constructs a systematic measurement framework, expands research perspectives, and provides tools for governments, industries, and enterprises to evaluate efficiency accurately.

Belinda Lopéz-Galán, Tiziana de-Magistris

Abstract This study analyses the influence of geographical origin and taste on honey consumer behavior. First, we explore the influence of geographical origin on consumers’ hedonic evaluation of honey. We then assess the influence of geographical origin and taste on their willingness to pay (WTP) for honey. We conducted a field experiment at a real supermarket. The participants were exposed to two treatments (blind and informed treatment). The findings showed that knowledge about the geographical origin of honey influences consumers’ hedonic evaluations and that the WTP for honey is more strongly influenced by geographical origin than by taste.

Xiangdong Xu, Lin Chen, Hewei Meng et al.

To explore the vibration transmission characteristics of jujube mechanical harvesting, and optimize the relationship between vibration input and dynamic response of jujube branches, the vibration characteristics simulation and layered vibration test of jujube branches were carried out. The jujube branch model was established by means of three-dimensional scanning and reverse reconstruction. The natural frequency and suitable vibration parameter range of the jujube branch model were obtained by simulation. Finally, the stratified vibration field experiment of jujube branch was carried out. The results show that there are multi-order natural frequencies of jujube branch in the range of 0–30 Hz. The typical vibration modes include the overall deformation of jujube branch, the deformation of unilateral branch and the deformation of the end of twigs. The resonance frequencies of the measuring points on different branches are mostly close, but the frequencies of the maximum peaks on different paths are different, which is often related to the branch path. The optimal working parameter combination under layered vibration is: the lower layer excitation frequency and amplitude are 5.80 Hz and 7.00 mm, the upper layer excitation frequency and amplitude are 15.60 Hz and 8.50 mm. Under this parameter combination, the acceleration of the measuring point on the fine branch is closest to the separation acceleration. Under this parameter combination, the average harvest rate is 88.74 %. The research can provide reference for the development of forest fruit vibration harvesting machinery.

Simone Figorilli, Lavinia Moscovini, Simone Vasta et al.

Recently banana plantations have been affected by the Black Sigatoka Disease (BSD), producing streaks, lesions and yellow and brown spots on the leaves until the appearance of entire dead parts. The disease causes reductions in yield making it essential to assess infection by monitoring plants status and implementing agronomical measures. This work aims to develop a physical field device to identify the BSD presence. It consists in a 3D printed prototype embedding a smartphone acquiring and processing banana leaves images. An advanced Artificial Intelligence model was trained and implemented for real-time processing. The algorithm is a Convolutional Neural Network (CNN) able to classify the samples into 6 classes representative of different BSD stages infection. The trained model, showing an accuracy of 82 % in training and 78 % in validation, was integrated into a specifically developed mobile application for field use. The Android app allows to acquire, identify the georeferenced infection stage, sync all to a remote dedicated host from which the results can be mapped and exported to a .csv file for easy data management. The distinction between healthy and diseased leaves can be achieved using the Smart BSD device for real-time acquisition, establishing the right intervention strategy.

Joseph T. Harrington, Vsevolod Cheburkanov, Mykyta Kizilov et al.

The increasing demands on modern plant and food production due to climate change, regulatory pressures, and the Sustainable Development Goals necessitate advanced photonic technologies for improved sustainability. Deep ultraviolet resonant Raman (DUVRR) spectroscopy offers precise spectral fingerprinting and potential disinfection capabilities, making it a promising tool for agricultural and food sciences. We developed a cost-effective, portable DUVRR spectroscopy system using a mercury (Hg) lamp as the excitation source at 253.65 \unit{\nano\meter}. The system was tested on diverse samples, including alcohol solvents, organic extracts, and industrial chemicals. The DUVRR system successfully resolved sub-1000 \unit{\per\centi\meter} Raman peaks, enabling detailed spectral fingerprints of various constituents and biomarkers. The system's high sensitivity and specificity ensure precise identification of nutritional values and food quality. The DUV light used in the system, defined here as less than 260 \unit{\nano\meter}, demonstrated potential disinfection properties, adding significant value for food safety applications. The highly sensitive detection capability of our DUVRR system at low powers has significant implications for plant and agricultural sciences. The detailed spectral information enhances the evaluation of nutritional values, food quality, and ripening processes. This dually-functional system is highly valuable for precision farming, food production, and quality control. Our DUVRR spectroscopy system provides a highly sensitive, affordable, and portable method for the spectroscopic evaluation and disinfection of food and agricultural samples. Its ability to resolve detailed Raman peaks below 1000 \unit{\per\centi\meter}, combined with DUV light's disinfection capabilities, makes it a promising tool for advancing sustainability and safety in agriculture and food production.

Arne Henningsen, Guy Low, David Wuepper et al.

Most research questions in agricultural and applied economics are of a causal nature, i.e., how one or more variables (e.g., policies, prices, the weather) affect one or more other variables (e.g., income, crop yields, pollution). Only some of these research questions can be studied experimentally. Most empirical studies in agricultural and applied economics thus rely on observational data. However, estimating causal effects with observational data requires appropriate research designs and a transparent discussion of all identifying assumptions, together with empirical evidence to assess the probability that they hold. This paper provides an overview of various approaches that are frequently used in agricultural and applied economics to estimate causal effects with observational data. It then provides advice and guidelines for agricultural and applied economists who are intending to estimate causal effects with observational data, e.g., how to assess and discuss the chosen identification strategies in their publications.

Nicolas Soncini, Javier Cremona, Erica Vidal et al.

We present a multi-modal dataset collected in a soybean crop field, comprising over two hours of recorded data from sensors such as stereo infrared camera, color camera, accelerometer, gyroscope, magnetometer, GNSS (Single Point Positioning, Real-Time Kinematic and Post-Processed Kinematic), and wheel odometry. This dataset captures key challenges inherent to robotics in agricultural environments, including variations in natural lighting, motion blur, rough terrain, and long, perceptually aliased sequences. By addressing these complexities, the dataset aims to support the development and benchmarking of advanced algorithms for localization, mapping, perception, and navigation in agricultural robotics. The platform and data collection system is designed to meet the key requirements for evaluating multi-modal SLAM systems, including hardware synchronization of sensors, 6-DOF ground truth and loops on long trajectories. We run multimodal state-of-the art SLAM methods on the dataset, showcasing the existing limitations in their application on agricultural settings. The dataset and utilities to work with it are released on https://cifasis.github.io/rosariov2/.

Rick van Essen, Eldert van Henten, Lammert Kooistra et al.

This paper presents an adaptive path planner for object search in agricultural fields using UAVs. The path planner uses a high-altitude coverage flight path and plans additional low-altitude inspections when the detection network is uncertain. The path planner was evaluated in an offline simulation environment containing real-world images. We trained a YOLOv8 detection network to detect artificial plants placed in grass fields to showcase the potential of our path planner. We evaluated the effect of different detection certainty measures, optimized the path planning parameters, investigated the effects of localization errors, and different numbers of objects in the field. The YOLOv8 detection confidence worked best to differentiate between true and false positive detections and was therefore used in the adaptive planner. The optimal parameters of the path planner depended on the distribution of objects in the field. When the objects were uniformly distributed, more low-altitude inspections were needed compared to a non-uniform distribution of objects, resulting in a longer path length. The adaptive planner proved to be robust against localization uncertainty. When increasing the number of objects, the flight path length increased, especially when the objects were uniformly distributed. When the objects were non-uniformly distributed, the adaptive path planner yielded a shorter path than a low-altitude coverage path, even with a high number of objects. Overall, the presented adaptive path planner allowed finding non-uniformly distributed objects in a field faster than a coverage path planner and resulted in a compatible detection accuracy. The path planner is made available at https://github.com/wur-abe/uav_adaptive_planner.

Xinyu Li

In 2025, intensified Immigration and Customs Enforcement (ICE) raids in Oxnard, California, disrupted the state's \$49 billion agricultural industry, a critical supplier of 75% of U.S. fruits and nuts and one-third of its vegetables. This paper quantifies the economic consequences of these raids on labor markets, crop production, and food prices using econometric modeling. We estimate a 20-40% reduction in the agricultural workforce, leading to \$3-7 billion in crop losses and a 5-12% increase in produce prices. The analysis draws on USDA Economic Research Service data and recent ICE detention figures, which show arrests in Southern California rising from 699 in May to nearly 2,000 in June 2025. The raids disproportionately affect labor-intensive crops like strawberries, exacerbating supply chain disruptions. Policy recommendations include expanding the H-2A visa program and legalizing undocumented workers to stabilize the sector. This study contributes to agricultural economics by providing a data-driven assessment of immigration enforcement's economic toll.

Georg Happich, Alexander Grever, Julius Schöning

The agricultural industry is undergoing a significant transformation with the increasing adoption of autonomous technologies. Addressing complex challenges related to safety and security, components and validation procedures, and liability distribution is essential to facilitate the adoption of autonomous technologies. This paper explores the collaborative groups and initiatives undertaken to address these challenges. These groups investigate inter alia three focal topics: 1) describe the functional architecture of the operational range, 2) define the work context, i.e., the realistic scenarios that emerge in various agricultural applications, and 3) the static and dynamic detection cases that need to be detected by sensor sets. Linked by the Agricultural Operational Design Domain (Agri-ODD), use case descriptions, risk analysis, and questions of liability can be handled. By providing an overview of these collaborative initiatives, this paper aims to highlight the joint development of autonomous agricultural systems that enhance the overall efficiency of farming operations.

M. Thakur, Bo Wang, M. Verma

The increasing global population and limited natural resources are amongst major challenges in the sustainability of agricultural and food industries, together with the rapid shrinking of land and increasing production cost. Based on the application of nanobiosen-sors, natural resources can be utilised more efficiently. Particularly, nanobiosensors can be used in a wide range of applications throughout the agri-food route, ranging from detection of soil condition, crop diseases caused by pest/pathogen, management of severe infections, and diagnostic tools for detection of pests during storage and ensures final quality assurance. Here, we review the various recent applications of nanobiosensors in agricultural and food industries. The advantages and limitations are also discussed to provide useful insights to both academic and industrial researchers. Moreover, recent patents have been discussed to provide the latest trends in biosensors for agri-food industry to maintain sustainable development. © 2022TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCC BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Forough Ataollahi, John W. Piltz, Geoff R. Casburn et al.

This study compared the effect of four levels of K Humate S100R (potassium humate) supplementation on the quality, shelf-life, and nutritional properties of beef. Angus steers (n = 40) were individually housed and fed either 0, 35, 70, or 140 g K Humate S100R/animal/day for 100 days, following a 30 day adjustment period. The steers were slaughtered at the completion of the feeding study. The left m. longissimus lumborum (LL) was collected at 24 h post-mortem and aged for either 2 or 6 weeks before analysis. K Humate S100R supplementation did not affect beef drip loss, cooking loss, shear force, sarcomere length, ultimate pH, intramuscular fat content, or total volatile basic nitrogen concentrations (P > 0.05). Steers supplemented with 70 g/day K Humate S100R produced beef with higher a* values on Days 1 and 3 of retail display (P < 0.05). Beef mineral composition was unchanged by K Humate S100R supplementation (P > 0.05), but there were minor changes to the fatty acid profile. Specifically, the ratio of omega-6 to omega-3 (P < 0.05) and C20:2n-6 concentrations (P < 0.05) increased with supplementation level. Together, these results demonstrate no detrimental effects on beef quality and shelf-life as a result of K Humate S100R supplementation.

Silvia Andrés González-Moralejo

Abstract This study analyzes the changes that have occurred in food logistics in the three years since the emergence of the COVID-19 pandemic and the one year since the war in Ukraine commenced. Food logistics companies are highly sensitive to demand shocks, energy prices, and staff availability. In this study, “first-hand” information was collected in the Iberian Peninsula, and it showed a process of Schumpeterian transformation. This crisis environment in which food logistics companies have been operating has opened a unique opportunity to renew operating procedures and seek new solutions, products, and markets. Therefore, food logistics companies have developed more effective communication strategies and innovative, profitable, and forward-looking commercial strategies to adapt to the new needs of their clients, applied more efficient transport planning and management methods, implemented new technologies to increase automation and digitization in warehouses, transport platforms, and trucks, and boosted market concentration and investment in infrastructure. Therefore, public authorities and top executives must focus on promoting and facilitating these improvements.

Xingjian Li, Lirong Xiang

This work presents a new robotics simulation environment built upon Unreal Engine 5 (UE5) for agricultural image data generation. The simulation utilizes the state-of-the-art real-time rendering engine to provide realistic plant images which are often used in agricultural applications. This study showcases the rendering accuracy of UE5 in comparison to existing tools and assesses its positional accuracy when integrated with Robot Operating Systems (ROS). The results indicate that UE5 achieves an impressive average distance error of 0.021mm when compared to predetermined setpoints in a multi-robot setup involving two UR10 arms.

Alejandro Ramírez-Arroyo, Melisa López, Ignacio Rodríguez et al.

5G cellular networks are now a reality and promise to improve key performance indicators (KPIs), such as Gbps data rates and latencies in the order of milliseconds. While some of these KPIs are achievable in urban scenarios, rural areas often face challenging connectivity conditions due to the lack of terrestrial network (TN) infrastructure. To solve this problem, non-terrestrial networks (NTNs) such as satellite-based solutions, have been introduced to provide coverage in remote regions. Therefore, a multi-connectivity approach can be integrated to simultaneously serve an end-user by merging satellite and cellular links in a joint approach. This study explores, using experimental data, the benefits of both TN-TN and TN-NTN multi-connectivity in rural environments. The results obtained demonstrate that a traditional single-connectivity approach may not be sufficient to provide service to rural environments due to the KPIs requirements given several use cases within these rural areas. The multi-connectivity strategy, which jointly integrates 5G and satellite networks, meets the network availability requirements for latency, downlink throughput, and uplink throughput KPIs at least 98%, 99%, and 95% of the time, respectively, for several use cases, such as precision agriculture, livestock monitoring, and forest management. These include applications like microclimate monitoring, remote operational support, early pest detection, and real-time tracking of livestock transport.