Hasil untuk "Agriculture"

L. Lipper, P. Thornton, B. Campbell et al.

J. Rockström, John Williams, G. Daily et al.

There is an ongoing debate on what constitutes sustainable intensification of agriculture (SIA). In this paper, we propose that a paradigm for sustainable intensification can be defined and translated into an operational framework for agricultural development. We argue that this paradigm must now be defined—at all scales—in the context of rapidly rising global environmental changes in the Anthropocene, while focusing on eradicating poverty and hunger and contributing to human wellbeing. The criteria and approach we propose, for a paradigm shift towards sustainable intensification of agriculture, integrates the dual and interdependent goals of using sustainable practices to meet rising human needs while contributing to resilience and sustainability of landscapes, the biosphere, and the Earth system. Both of these, in turn, are required to sustain the future viability of agriculture. This paradigm shift aims at repositioning world agriculture from its current role as the world’s single largest driver of global environmental change, to becoming a key contributor of a global transition to a sustainable world within a safe operating space on Earth.

A. Power

Agricultural ecosystems provide humans with food, forage, bioenergy and pharmaceuticals and are essential to human wellbeing. These systems rely on ecosystem services provided by natural ecosystems, including pollination, biological pest control, maintenance of soil structure and fertility, nutrient cycling and hydrological services. Preliminary assessments indicate that the value of these ecosystem services to agriculture is enormous and often underappreciated. Agroecosystems also produce a variety of ecosystem services, such as regulation of soil and water quality, carbon sequestration, support for biodiversity and cultural services. Depending on management practices, agriculture can also be the source of numerous disservices, including loss of wildlife habitat, nutrient runoff, sedimentation of waterways, greenhouse gas emissions, and pesticide poisoning of humans and non-target species. The tradeoffs that may occur between provisioning services and other ecosystem services and disservices should be evaluated in terms of spatial scale, temporal scale and reversibility. As more effective methods for valuing ecosystem services become available, the potential for ‘win–win’ scenarios increases. Under all scenarios, appropriate agricultural management practices are critical to realizing the benefits of ecosystem services and reducing disservices from agricultural activities.

H. Willer, Minou Yussefi-Menzler, N. Sorensen

A. Walter, R. Finger, R. Huber et al.

Pacific Northwest, Charles G. Johnson

H. Jawad, R. Nordin, S. Gharghan et al.

Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data.

D. Coskun, D. T. Britto, W. Shi et al.

T. Mahanty, S. Bhattacharjee, Madhurankhi Goswami et al.

Xuyi Liu, Shun-chao Zhang, Junghan Bae

N. Ahmed, D. De, I. Hussain

Internet of Things (IoT) gives a new dimension in the area of smart farming and agriculture domain. With the use of fog computing and WiFi-based long distance network in IoT, it is possible to connect the agriculture and farming bases situated in rural areas efficiently. To focus on the specific requirements, we propose a scalable network architecture for monitoring and controlling agriculture and farms in rural areas. Compared to the existing IoT-based agriculture and farming solutions, the proposed solution reduces network latency up to a certain extent. In this, a cross-layer-based channel access and routing solution for sensing and actuating is proposed. We analyze the network structure based on coverage range, throughput, and latency.

L. Schreefel, R. Schulte, I. D. de Boer et al.

Abstract Regenerative agriculture (RA) is proposed as a solution towards sustainable food systems. A variety of actors perceive RA differently, and a clear scientific definition is lacking. We reviewed 28 studies to find convergence and divergence between objectives and activities that define RA. Our results show convergence related to objectives that enhance the environment and stress the importance of socio-economic dimensions that contribute to food security. The objectives of RA in relation to socio-economic dimensions, however, are general and lack a framework for implementation. From our analysis, we propose a provisional definition of RA as an approach to farming that uses soil conservation as the entry point to regenerate and contribute to multiple ecosystem services.

T. Hofmann, G. Lowry, S. Ghoshal et al.

Yuzhen Lu, Sierra N. Young

Abstract Computer vision technologies have attracted significant interest in precision agriculture in recent years. At the core of robotics and artificial intelligence, computer vision enables various tasks from planting to harvesting in the crop production cycle to be performed automatically and efficiently. However, the scarcity of public image datasets remains a crucial bottleneck for fast prototyping and evaluation of computer vision and machine learning algorithms for the targeted tasks. Since 2015, a number of image datasets have been established and made publicly available to alleviate this bottleneck. Despite this progress, a dedicated survey on these datasets is still lacking. To fill this gap, this paper makes the first comprehensive but not exhaustive review of the public image datasets collected under field conditions for facilitating precision agriculture, which include 15 datasets on weed control, 10 datasets on fruit detection, and 9 datasets on miscellaneous applications. We survey the main characteristics and applications of these datasets, and discuss the key considerations for creating high-quality public image datasets. This survey paper will be valuable for the research community on the selection of suitable image datasets for algorithm development and identification of where creation of new image datasets is needed to support precision agriculture.

M. Ferrag, Lei Shu, Xing Yang et al.

This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture.

I. Zambon, M. Cecchini, G. Egidi et al.

The present review retraces the steps of the industrial and agriculture revolution that have taken place up to the present day, giving ideas and considerations for the future. This paper analyses the specific challenges facing agriculture along the farming supply chain to permit the operative implementation of Industry 4.0 guidelines. The subsequent scientific value is an investigation of how Industry 4.0 approaches can be improved and be pertinent to the agricultural sector. However, industry is progressing at a much faster rate than agriculture. In fact, already today experts talk about Industry 5.0. On the other hand, the 4.0 revolution in agriculture is still limited to a few innovative firms. For this reason, this work deals with how technological development affects different sectors (industry and agriculture) in different ways. In this innovative background, despite the advantages of industry or agriculture 4.0 for large enterprises, small- and medium-sized enterprises (SMEs) often face complications in such innovative processes due to the continuous development in innovations and technologies. Policy makers should propose strategies, calls for proposals with aim of supporting SMEs to invest on these technologies and making them more competitive in the marketplace.

M. Torky, A. Hassanein

Abstract Blockchain quickly became an important technology in many applications of precision agriculture discipline. The need to develop smart P2P systems capable of verifying, securing, monitoring, and analyzing agricultural data is leading to thinking about building blockchain-based IoT systems in precision agriculture. Blockchain plays the role of pivotal in replacing the classical methods of storing, sorting and sharing agricultural data into a more reliable, immutable, transparent and decentralized manner. In precision farming, the combination of the Internet of Things and the blockchain will move us from only smart farms only to the internet of smart farms and add more control in supply-chains networks. The result of this combination will lead to more autonomy and intelligence in managing precision agriculture in more efficient and optimized ways. This paper exhibits a comprehensive survey on the importance of integrating both blockchain and IoT in developing smart applications in precision agriculture. The paper also proposed novel blockchain models that can be used as important solutions for major challenges in IoT-based precision agricultural systems. In addition, the study reviewed and clearly discussed the main functions and strengths of the common blockchain platforms used in managing various sub-sectors in precision agriculture such as crops, livestock grazing, and food supply chain. Finally, the paper discussed some of the security and privacy challenges, and blockchain-open issues that obstacles developing blockchain-IoT systems in precision agriculture.

Svenja Mohr, R. Kühl

The use of Artificial Intelligence (AI) in agriculture is expected to yield advantages such as savings in production resources, labor costs, and working hours as well as a reduction in soil compaction. However, the economic and ecological benefits of AI systems for agriculture can only be realized if farmers are willing to use them. This study applies the technology acceptance model (TAM) of Davis (1989) and the theory of planned behavior (TPB) of Ajzen (1991) to investigate which behavioral factors are influencing the acceptance of AI in agriculture. The composite model is extended by two additional factors, expectation of property rights over business data and personal innovativeness. A structural equation analysis is used to determine the importance of factors influencing the acceptance of AI systems in agriculture. For this purpose, 84 farmers were surveyed with a letter or an online questionnaire. Results show that the perceived behavioral control has the greatest influence on acceptance, followed by farmers’ personal attitude towards AI systems in agriculture. The modelled relationships explain 59% of the total variance in acceptance. Several options and implications on how to increase the acceptance of AI systems in agriculture are discussed.

Sikai Xu, Jie Wei, Qian Wang et al.

Macrobrachium rosenbergii is an economically important aquaculture species worldwide, but its reproductive efficiency is severely constrained by challenges in ovarian maturation. Methyl farnesoate (MF), a sesquiterpenoid hormone, plays a critical regulatory role in crustacean reproductive development. In this study, juvenile M. rosenbergii with an average body weight of ∼4.2 g were fed diets supplemented with different concentrations of MF (0, 3, 6, and 9 μg/g) for 56 consecutive days. The effects on growth performance, ovarian development, gut microbial composition, and related gene expression were systematically evaluated. The results showed that high-dose MF (9 μg/g) significantly promoted ovarian development, accelerated the transition of oocytes from the Oc2 to Oc4 stage, and markedly increased oocyte diameter. Hemolymph vitellogenin levels were significantly elevated during days 28–42. Concurrently, MF at high doses significantly upregulated the expression of Met, Kr-h1, and Vtg, suggesting that MF may facilitate vitellogenesis via the Met–Kr-h1–Vtg signaling pathway. Gut microbiota analysis revealed that MF reshaped microbial community structure, with the highest diversity observed in the 9 μg/g group, and dominant genera shifted to Enterobacter and Clostridium, potentially enhancing immune and metabolic capacity. In contrast, low-dose MF (3 μg/g) exhibited inhibitory effects on both growth and ovarian development. In summary, this study systematically elucidates the dose-dependent effects of MF on ovarian maturation in M. rosenbergii and reveals potential molecular mechanisms, providing a theoretical basis for efficient crustacean breeding and hormone-replacement strategies.

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.