Hasil untuk "Agricultural industries"

Taneli Väisänen, A. Haapala, R. Lappalainen et al.

T. Duckett, S. Pearson, S. Blackmore et al.

Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over £108bn p.a., with 3.9m employees in a truly international industry and exports £20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector.

Shaobo Wang, Yingji Lian, Hongwei Pan et al.

Aeration irrigation (AI) is a promising method for improving crop quality and efficiency. Based on 156 peer-reviewed articles encompassing 1294 data pairs, this meta-analysis quantified the effects of AI on yield, yield components, and fruit quality. The results showed that AI significantly increased crop yield by 18.6 % (95 %CI: 17.2–19.7 %) compared with non-aerated irrigation (NAI). The most substantial benefits were observed in warm temperate climates, clayey and alkaline soils, and in greenhouse and fruit production systems. Yield gains were further enhanced under low nitrogen input and combined organic-inorganic fertilization. This technique also significantly improved grain yield components, increasing productive panicles, thousand kernel weight, grains per panicle, and seed setting rate by 7.2 %, 2.9 %, 4.3 %, and 1.0 %, respectively. For fruits and vegetables, AI enhanced the contents of vitamin C, soluble protein, soluble sugar, and lycopene by 11.6 %, 14.2 %, 11.3 %, and 31.1 %, respectively. It also improved the sugar-acid ratio by 13.1 % and reduced nitrate content by 10.6 %. Random Forest analysis identified soil organic matter, mean annual temperature, and irrigation amount as the dominant factors influencing the effectiveness of AI. Targeted application of AI under specific environmental and soil conditions can support the sustainable intensification of irrigated agriculture by improving both yield and fruit quality.

Mahmood Akbari, Saeed Farahani

Surface irrigation systems, while widespread due to their low operational costs, often suffer from significant inefficiencies driven by inappropriate design and management practices. To address this, the current study proposes a new simulation–optimization model aimed to the design of open-end border irrigation systems, seeking to enhance hydraulic performance under field constraints. The model integrates a modified hydro-empirical SCS simulation framework with the Grey Wolf Optimizer (GWO) algorithm, using border length, slope, inflow discharge, and deficit irrigation Factor as decision variables. Performance evaluation is based on five hydraulic indicators, embedded in a weighted single-objective function. The model was applied to three real case studies representing varying soil textures and irrigation requirements. Results demonstrate that the modified SCS model could simulate all four phases of irrigation as well as determine the subsurface infiltration curve across the field. Also optimization consistently reduced the advance time, aligning infiltration opportunity times across the field, and thereby improved distribution uniformity, and requirement efficiency, while substantially reducing total applied water. The findings also highlight the critical influence of decision variables—particularly inflow discharge and field length—on system performance, and emphasize that shortening the advance phase was the most effective strategy for performance enhancement. Ultimately, the proposed model offers a computationally efficient and hydraulically robust approach to designing border irrigation systems with improved resource efficiency and operational resilience.

Feiyun Wang, Hanlu Jiang, Jincan Wu et al.

To efficiently detect and count maize seedlings in complex field conditions, this study first developed a sample dataset under diverse backgrounds and lighting scenarios and introduced a data augmentation technique called “M_AUG.” YOLOv5s was selected as the base model, enhanced with the Swin Transformer (Swin TR)to improve feature extraction across various scales and complex environments. The model also incorporated multi-scale attention (EMA)to enhance the representation of small samples and positive/negative samples, along with the Asymptotic Feature Pyramid Network (AFPN)to integrate seedling features at different levels. The results showed that the proposed SEA-YOLOv5 achieved mAP0.5 of 98.6 %, mAP0.5–0.95 of 73.2 %. and F1 of 97.1 %, with the parameters count of 5.55 million and a weight size of 11.7 MB. Compared to YOLOv5, SEA-YOLOv5 improved mAP0.5 by 5.8 %, mAP0.5–0.95 by 9.9 %, and F1 by 5.4 %, while reducing the parameter count by 1.46 million and weight size by 2.7 MB. SEA-YOLOv5 was compared with YOLOv7, YOLOv8s, Faster R-CNN, RetinaNet, YOLOv10s, DNE-YOLO, and YOLOv11s, and the results indicated that SEA-YOLOv5 outperformed the comparison models in overall performance. Upon deploying SEA-YOLOv5 on the Jetson Orin NX and conducting seedling detection and counting trials across eight plots, the model achieved a miss rate of just 0.63 % and a frame rate of 74.6 FPS. Thus, it can be concluded that the SEA-YOLOv5 model developed in this study provides high accuracy, a compact design, and strong portability, making it well-suited for real-time detection and counting applications in the field.

Thinh Phan, Hoang Kim Tran, Andrew Lockett et al.

Efficient and continuous tracking of individual broilers is critical for improving poultry management, welfare, and breeding decisions in commercial settings. However, standard Multi-Object Tracking (MOT) techniques face significant challenges in poultry environments due to occlusions, high object similarity, and dense flocks.In this work, we introduce BroilerTrack, a novel multi-camera multi-broiler tracking framework tailored for the poultry industry. Unlike traditional approaches that rely heavily on appearance features, BroilerTrack employs a position-based tracking strategy in a unified coordinate system (unified plane), thereby circumventing identity ambiguity caused by the homogeneous appearance of broilers. Our proposed BroilerTrack system comprises three key modules: Top-view Aggregation, Side-view Distribution, and Identification Assignment, enabling robust identification (ID) consistency across multiple calibrated views. Furthermore, we present a new Multi-View Broiler dataset collected under commercial-like conditions, featuring synchronized footage from six strategically placed cameras (two top-view and four side-view). Notably, our method requires no unified-plane annotations during training and achieves superior performance over state-of-the-art Multi-camera MOT methods on both detection and association metrics. This work provides a scalable, non-intrusive solution for real-time poultry monitoring, with strong potential for applications in behavior analysis, welfare optimization, and automated breeding selection.

Dae-Young Kim, A. Kadam, Surendra K. Shinde et al.

J. Desaeger, Catherine L. Wram, I. Zasada

Abstract The last decade has seen a sharp increase in nematicide research in the agricultural industry. As a result, several new synthetic nematicides have become available to growers, and several more are expected in the near future. This new interest in nematicides is directly related to the growing demand for safer and more selective products, and the increasing regulatory pressure on many of the traditional nematicides. This has led to a ban of several widely used fumigant (e.g. methyl bromide) and non-fumigant (e.g. aldicarb) nematicides. The loss of traditional nematicides, combined with a lack of replacement products and awareness of the damage that nematodes can cause, has not only raised concern among growers, but has also created new opportunities for the crop protection industry. Nematicides have become a priority, and many companies are now allocating significant research dollars to discover new nematicides. The new nematicides are very different from previous products: (i) they are more selective, often only targeting nematodes, and (ii) they are less toxic, and safer to use. This review article describes these new developments by discussing the challenges that are associated with finding new nematicides, reviewing the nature, characteristics, and efficacy of new nematicides, and discussing the impact they could have on future nematode management.

Ioannis Malounas, Diamanto Lentzou, Georgios Xanthopoulos et al.

Automated machine learning (AutoML) is considered the next advancement in artificial intelligence due to its commitment to delivering high-performance end-to-end machine learning pipelines with minimal user input. Although AutoML has demonstrated significant potential in computer vision tasks, as far as current knowledge extends, there is currently no study that has applied AutoML specifically to hyperspectral imaging for fertilization level classification. To address this information gap, the use of AutoML for classifying fertilization levels using a hyperspectral and CIELAB color space dataset was investigated. A comparative analysis was conducted between the performance of an open-source AutoML framework, PyCaret, and traditional machine learning using the PLS-DA algorithm. PyCaret achieved the highest accuracy (1.00) in classifying different fertilization levels using the hyperspectral dataset, while PLS-DA attained an accuracy of 0.91. However, the CIELAB dataset was not as effective for this classification task, achieving an accuracy of only 0.72. It is worth noting that the hyperspectral dataset outperformed the CIELAB dataset in both AutoML and PLS-DA analyses. Finally, the findings suggest that AutoML holds substantial potential to enhance the use of hyperspectral imaging in agriculture, particularly for fertilization tasks.

Mickaël Wilmart

Alors qu’on associe spontanément le vigneron au monde rural, c’est paradoxalement la documentation urbaine qui nous renseigne le mieux sur lui pour les trois derniers siècles du Moyen Âge. Si les sources notariales et seigneuriales ne sont pas toujours précises le concernant avant le xve siècle, les archives urbaines permettent de mieux comprendre l’émergence d’un métier reconnu, en partie façonné par les exigences de la ville. Au xive siècle, les listes produites (de vote ou d’imposition) donnent à voir un groupe socio-professionnel bien identifié. Dans les décennies suivantes, les sources normatives se multiplient à leur sujet ainsi que les conflits sur leur temps de travail. Enfin, la fin du xve siècle voit l’apparition de métiers organisés dans plusieurs villes. Cette lente évolution répond à une exigence de professionnalisation de la part de citadins investissant de plus en plus dans des clos périurbains sans procéder eux-mêmes à leur exploitation.

Ilinka Terziyska

This study serves a dual purpose: firstly, to identify the pivotal architectural components within wine tourism offerings that contribute to a lasting and memorable experience; and secondly, to explore their correlation with the four customer experience realms (4Es). The investigation operates at a micro level, focusing on a specific category of winery – small, family-owned establishments – in an emerging wine destination. Employing a netnography approach, the research design centers on a thematic analysis of visitor reviews sourced from TripAdvisor. The ensuing results are quantified to ascertain the significance of individual factors. A total of 137 reviews spanning the period from June 2013 to May 2022 were manually extracted and coded using MaxQDA. This study’s rationale rests upon two key premises: firstly, the scarcity of research within the specific context of emerging wine destinations from the post-Eastern bloc; and secondly, the limited exploration of the nexus between wine tourism drivers and consumer experiences. The most influential drivers shaping memorable wine tourism encounters that we identified are “people” and “wine”, predominantly associated with the escapist and aesthetic dimensions of experience. These findings diverge markedly from prior research, where the escapist dimension typically received minimal attention. Theoretically, our results enrich understanding by elucidating how the four dimensions of the wine tourist experience intersect with the attributes of the tourism offering, thereby serving as a potential model for future investigations. Moreover, wineries stand to gain valuable insights for product design aimed at enhancing the overall consumer experience.

Jianzhai Wu, Zhangming Ge, Shuqing Han et al.

Abstract The rapid development of traditional agriculture in China was achieved at the expense of high energy consumption and investments. However, the global green development trend made it necessary for the country to transform its agricultural energy utilization. Energy efficiency changes are affected by many factors, particularly industrial agglomeration. In recent years, the Chinese government has introduced a series of policies, including setting major producing regions for grains and advantageous regions for characteristic agricultural product. These have caused significant changes to the spatial layout of the agriculture industry. However, there is still a lack of research on the impact of these changes on agricultural energy efficiency (AEE). In this study, panel data of 30 Chinese provinces from 2000 to 2016 were entered into stochastic frontier models to measure the country’s AEE at the provincial level. A series of spatial econometric models were also used to analyze the impact of agricultural industrial agglomeration on China’s AEE. The results indicated that the country’s AEE exhibited obvious spatial gradients and correlations. After controlling the impacts of spatial correlation and other factors in the models, agricultural industrial agglomeration was found to have an overall positive impact on China’s AEE. In the future, policies should be formulated to increase AEE by establishing agricultural functional areas, strengthening the innovation and sharing of green development technologies at the farm level, and promoting the optimization of energy structures in agricultural and rural areas.

Liu-Wen Liao, H. Long, Xiaolu Gao et al.

Abstract Understanding the impact of rural aging on agricultural production is critical for implementing a comprehensive rural vitalization strategy in China. In this paper, the planting structure is converted into and measured by the amount of labor employing quantity in the planting industry with the 2000 and 2010 census data and crop planting area data at the county level. By establishing the elasticity coefficient of rural aging - labor quantity and taking the coupling relation between the rural aging and the change in crop production quantity in the Huang-Huai-Hai region into account, the spatio-temporal pattern of rural aging and the recessive transition of land use is analyzed. The results show that the aging level of the rural population in the Huang-Huai-Hai region has maintained an overall growth rate of 27.03% from 2000 to 2010. During the research period, the labor employing quantity in the planting industry decreased by 14.18% and, was mainly distributed among the western Shandong hilly region, the Taihang and Yanshan mountainous region, and the eastern part of the Huang-Huai-Hai Plain agricultural region. The spatial coupling relationship between the rural aging and the changes in labor employing quantity in the planting industry presented diversified patterns. From 2000–2010, the number of counties with three different coupling modes, namely, rural aging increasing and labor employing quantity decreasing, increasing of rural aging and labor employing quantity, and decreasing of rural aging and labor employing quantity, account for 84.90%, 11.11%, and 3.7%, respectively. To achieve the goal of agricultural development, the authors argue that in the dual context of rural aging and population mobilization, agricultural development must cultivate new professional farmers and improve the level of agricultural modernization in China.

Nan Xia, Wenyue Hai, Mengying Tang et al.

Xinjiang is located in the northwest of China, with a vast territory and abundant resources, which holds an important strategic position for China. However, the climate in the region is arid and the ecological environment is fragile. In recent years, with the growth of population and the improvement of economic development level, construction and agricultural land in Xinjiang have gradually expanded, and ecological land has been extensively occupied. The competition between land resources is becoming increasingly fierce, leading to some ecological and environmental problems in the region. In this context, understanding the spatiotemporal changes and driving factors of Xinjiang's production space, living space and ecological space (PLES) is of great practical significance for promoting sustainable development in the region, and can also provide reference for the research of PLES in other arid areas at home and abroad. At present, most of the research on PLES is focused on small-scale areas such as urban agglomerations, rural areas, and watersheds, or non arid areas with relatively superior natural conditions, with less research on large-scale arid areas. In this study, the transfer matrix, the center of gravity movement trajectory, Moran's I and the spatial conflict measurement model were used to illustrate the spatial–temporal evolution of PLES in Xinjiang, and the correlation coefficient was used to determine the main factors affecting the change of PLES. Results show that (1) Xinjiang is dominated by ecological space, which accounts for more than 90% of the total area of Xinjiang and has significant continuity. They are mainly distributed in Tarim, Turpan, and the northern Junggar Basin. The production space area accounts for about 5% of the total area of Xinjiang and is distributed in the vicinity of the Tianshan and Kunlun Mountains. Living space accounts for about 1% of Xinjiang's total area and is scattered around cities. (2) From 2000 to 2020, the change degree of PLES in Xinjiang in the last decade is higher than that in the first decade, most of which is the transformation from ecological space to production space. The three types of spaces influence and restrict each other in the development process. (3) Population, crop sown area, natural conditions, policy and the development level of agricultural science and technology are the main factors affecting the spatial distribution of production. Environmental factors such as altitude, soil erosion and temperature have a great influence on ecological space. Population, the output value of various industries and total retail sales of consumer goods are the main factors affecting residential space. The evolution of Xinjiang PLES is the result of multiple factors. (4) The medium spatial conflict dominates, mainly distributed in Tarim Basin and Junggar Basin. The weak and strong space conflicts are concentrated in the oases in the north of the Tianshan and Kunlun Mountains, and the number is small. From 2000 to 2020, the increasing degree of spatial conflicts in Xinjiang is due to land desertification.

Hongzheng Shen, Shilei Li, Kexin Sun et al.

Irrigation and nitrogen (N) input are effective methods of balancing crop yields and greenhouse gas (GHG) emissions. However, the effects of irrigation and N management on the GHG emission of winter wheat production systems have not yet been well documented. Therefore, we conducted 2-year field experiments on winter wheat and studied the effects of irrigation and N reduction/replacement on GHG emissions, grain yield, global warming potential (GWP), and GHG emission intensity (GHGI). The winter wheat was irrigated once or twice with 60 or 90 ​mm each time. Five N reduction/replacement treatments were no N application (CK), 25% traditional urea (U) reduction (75%U), traditional U (U), 25% reduction in polymer-coated U (PCU), and 25% reduction in traditional U combined with organic fertilizers (U ​+ ​OM). The results indicated that N reduction/replacement significantly reduced N2O emissions, but had no significant effect on CH4 emissions. Soil water-filled pore space was the same key factor affecting the emission fluxes of N2O and CH4. Soil temperature significantly affected the fluxes of CH4 (P ​< ​0.001). Irrigation treatment did not exhibit a significant effect on grain yield, but high irrigation input increased the GWP and GHGI. Compared with U, the grain yield of CK, 75%U, PCU, and U ​+ ​OM increased by −46%, −9.2%, −1.8%, and 5.4%, respectively; GWP decreased by 35.9%, 11.6%, 7.3%, and 2.7%, respectively; and GHGI decreased by −22.1%, 3.1%, 6.0%, and 8.0%, respectively. Therefore, sustainable production goals of increasing grain yield and reducing GHG emissions were achieved with N reduction/replacement using U + OM and low irrigation input of 60 ​mm.

Dian Risky Damayanti, Dewi Kurniawati

Semakin kompetitifnya persaingan bisnis dengan banyaknya produk serupa yang dikeluarkan oleh pesaing menjadi latar belakang dalam penelitian. J.CO Donuts & Coffee Jember adalah perusahaan yang meluncurkan donat sebagai produk yang ditawarkannya dan memiliki banyak pesaing. Tujuan penelitian ini dilakukan untuk menguji dan menganalisis pengaruh produk, harga, lokasi dan promosi terhadap keputusan pembelian donat pada J.CO Donuts & Coffee Jember. Sampel sebanyak 50 responden diambil dari populasi konsumen yang melakukan pembelian donat secara langsung. Alat analisis yang digunakan dalam penelitian ini adalah analisis regresi linier berganda dengan bantuan SPSS 23.00 for Windows. Kesimpulan dari hasil penelitian ini adalah  variabel produk, harga, lokasi dan promosi serempak  berpengaruh secara signifikan terhadap keputusan pembelian donat pada J.CO Donuts & Coffee Jember. Secara parsial hasil penelitian ini disimpulkan bahwa variabel produk, harga dan promosi secara parsial berpengaruh tidak signifikan terhadap keputusan pembelian, sedangkan lokasi secara parsial berpengaruh signifikan terhadap keputusan pembelian. Berdasarkan analisis lokasi merupakan variabel yang berpengaruh dominan terhadap keputusan pembelian produk donat pada J.CO Donuts & Coffee Jember.

Tiarma Tiarma, Yossie Yumiati, Ana Nurmalia

Advances in science and technology in agriculture have brought changes in buying and selling transactions of fresh agricultural products such as vegetables. This change encourages the lifestyle of people who want everything practical and the emergence of online stores, Kupesan is the first online store in Bengkulu City that already has an application. Therefore, the purpose of this study was to determine the factors that influence purchasing decisions for vegetables at the online shop "Kupesan". Data collection methods were carried out by surveys, interviews and distributing questionnaires via google form. A sample of 67 people using purposive sampling, namely individuals who may be respondents are people who have purchased vegetable commodities more than once at the online shop "Kupesan". Data analysis using multiple linear regression with the help of SPSS software. The results of this study indicate that the factors of trust and product quality that influence purchasing decisions for vegetables at the online store Kupesan. While the factors of price, convenience and service do not affect the decision to purchase vegetables at the online store Kupesan.

K. Fue, W. Porter, E. Barnes et al.

In this review, we examine opportunities and challenges for 21st-century robotic agricultural cotton harvesting research and commercial development. The paper reviews opportunities present in the agricultural robotics industry, and a detailed analysis is conducted for the cotton harvesting robot industry. The review is divided into four sections: (1) general agricultural robotic operations, where we check the current robotic technologies in agriculture; (2) opportunities and advances in related robotic harvesting fields, which is focused on investigating robotic harvesting technologies; (3) status and progress in cotton harvesting robot research, which concentrates on the current research and technology development in cotton harvesting robots; and (4) challenges in commercial deployment of agricultural robots, where challenges to commercializing and using these robots are reviewed. Conclusions are drawn about cotton harvesting robot research and the potential of multipurpose robotic operations in general. The development of multipurpose robots that can do multiple operations on different crops to increase the value of the robots is discussed. In each of the sections except the conclusion, the analysis is divided into four robotic system categories; mobility and steering, sensing and localization, path planning, and robotic manipulation.

Peilei Qiu, Zhaoxing Zhou, Dongjoong Kim

Background/Objective: The Chinese government is actively developing the rural economy and promoting the poverty alleviation campaign. The economic development of the traditional agricultural areas is essential to people’s basic livelihood and social stability. The tourism industry has been proven to be an effective approach to promote the regional economy. It has become a hot issue as to how to develop the tourism industry in rural areas. Methods/Statistical Analysis: Based on a corresponding index system, a coupling coordination model was established to explore the coupling and coordination development of the agricultural industry and the tourism industry in Henan province, a traditional agricultural area in China. Findings: The result revealed that although the coupling degree between the agricultural and the tourism industry from the year of 2009 to 2018 is relatively stable, the coordination degree shows a continuous rising trend from 0.278 to 0.921. This indicated that the agricultural industry and the tourism industry in Henan province continues to interact with and influence each other, and the comprehensive development level and the coordination degree of the two industries have been constantly improved. Implications: From the result, it can be seen that the integration development of the agricultural industry and the tourism industry could be a new path to develop the economy of rural areas. From the perspective of sustainable development, suggestions are proposed to optimize the agricultural industrial structure, extend tourism industry chain and construct support policy to develop a sustainable mode of the agricultural industry and the tourism industry.

Rui Zhao, Qingyu Guan, Haiping Luo et al.

Heavy metals in agricultural soil receive much attention because they are easily absorbed by crop into the ecosystem. Managing the discharge of heavy metals from the source is an effective way to prevent and control heavy metals pollution. Grouped principal component analysis (GPCA) and Positive Matrix Factorization (PMF) receptor models were utilized in this study to conduct source apportionment, and the former was optimal because of the accuracy of predicting. Based on the source contribution by GPCA/APCS, heavy metals were evaluated by fuzzy synthetic evaluation model and health risk assessment model. The results of source apportionment showed that heavy metals in Zhangye agricultural soil were mainly affected by steel industry, traffic, agrochemicals, manures, mining activities, leather industry and metal processing industry source. Fuzzy synthetic evaluation showed that the pollution levels of Chromium (Cr) derived by leather industry and metal processing industry and Nickel (Ni) derived by steel industry and traffic source were higher. Health risk assessment revealed that the non-carcinogenic and carcinogenic risks of Cr derived by leather industry and metal processing industry and Lead (Pb) derived by steel industry and traffic source were higher.