Hasil untuk "Agriculture (General)"

G. Fischer, M. Shah, Francesco N Tubiello et al.

H. Murray

David Lagakos, Michael E. Waugh

Srikanth Thudumu, Jason Fisher

Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies. However, current agricultural intelligence systems often lack contextual understanding, explainability, and adaptability, especially for smallholder farmers with limited resources. General-purpose large language models (LLMs), while powerful, typically lack the domain-specific knowledge and contextual reasoning needed for practical decision support in farming. They tend to produce recommendations that are too generic or unrealistic for real-world applications. To address these challenges, we present OpenAg, a comprehensive framework designed to advance agricultural artificial general intelligence (AGI). OpenAg combines domain-specific foundation models, neural knowledge graphs, multi-agent reasoning, causal explainability, and adaptive transfer learning to deliver context-aware, explainable, and actionable insights. The system includes: (i) a unified agricultural knowledge base that integrates scientific literature, sensor data, and farmer-generated knowledge; (ii) a neural agricultural knowledge graph for structured reasoning and inference; (iii) an adaptive multi-agent reasoning system where AI agents specialize and collaborate across agricultural domains; and (iv) a causal transparency mechanism that ensures AI recommendations are interpretable, scientifically grounded, and aligned with real-world constraints. OpenAg aims to bridge the gap between scientific knowledge and the tacit expertise of experienced farmers to support scalable and locally relevant agricultural decision-making.

Xiaobei Zhao, Xingqi Lyu, Xiang Li

Agricultural robots have emerged as powerful members in agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement, resulting in limited mobility and poor adaptability. Vision-and-Language Navigation (VLN) enables robots to navigate to the target destinations following natural language instructions, demonstrating strong performance on several domains. However, none of the existing benchmarks or methods is specifically designed for agricultural scenes. To bridge this gap, we propose Agriculture to Agriculture (A2A) benchmark, containing 1,560 episodes across six diverse agricultural scenes, in which all realistic RGB videos are captured by front-facing camera on a quadruped robot at a height of 0.38 meters, aligning with the practical deployment conditions. Meanwhile, we propose Vision-and-Language Navigation for Agricultural Robots (AgriVLN) baseline based on Vision-Language Model (VLM) prompted with carefully crafted templates, which can understand both given instructions and agricultural environments to generate appropriate low-level actions for robot control. When evaluated on A2A, AgriVLN performs well on short instructions but struggles with long instructions, because it often fails to track which part of the instruction is currently being executed. To address this, we further propose Subtask List (STL) instruction decomposition module and integrate it into AgriVLN, improving Success Rate (SR) from 0.33 to 0.47. We additionally compare AgriVLN with several existing VLN methods, demonstrating the state-of-the-art performance in the agricultural domain.

Xing Hu, Haodong Chen, Qianqian Duan et al.

With the global population increasing and arable land resources becoming increasingly limited, smart and precision agriculture have emerged as essential directions for sustainable agricultural development. Artificial intelligence (AI), particularly deep learning models, has been widely adopted in applications such as crop monitoring, pest detection, and yield prediction. Among recent generative models, diffusion models have demonstrated considerable potential in agricultural image processing, data augmentation, and remote sensing analysis. Compared to traditional generative adversarial networks (GANs), diffusion models exhibit greater training stability and superior image generation quality, effectively addressing challenges such as limited annotated datasets and imbalanced sample distributions in agricultural scenarios. This paper reviews recent advancements in the application of diffusion models within agriculture, focusing on their roles in crop disease and pest detection, remote sensing image enhancement, crop growth prediction, and agricultural resource management. Diffusion models have been found useful in improving tasks like image generation, denoising, and data augmentation in agriculture, especially when environmental noise or variability is present. While their high computational requirements and limited generalizability across domains remain concerns, the approach is gradually proving effective in real-world applications such as precision crop monitoring. As research progresses, these models may help support sustainable agriculture and address emerging challenges in food systems.

Xin Liu, Yongqin Liao, Zhufeng Shi et al.

Streptomycetes are vital microbial resources used in agriculture and biotechnology and are diverse secondary metabolites. The <i>Streptomyces olivoreticuli</i> YNK-FS0020 strain was isolated from the rhizosphere soil in Yunnan’s Wuliangshan Forest; its functions were explored via a series of experiments and genomic analysis. Indoor assays showed that this strain inhibits seven plant pathogens (including <i>Fusarium oxysporum</i> f. sp. <i>cubense</i> Tropical Race 4) and exhibits phosphorus solubilization, siderophore production, and plant-growth promotion. Genomic analysis revealed 47 secondary metabolite biosynthetic gene clusters: 12 shared over 60% similarity with known clusters (4 exhibited 100% similarity, involving antimycin and ectoine), while 19 showed low similarity or unknown functions, indicating the strain’s potential in the development of novel compounds. Genes related to tryptophan-IAA synthesis, phosphate metabolism, and siderophore systems were annotated, while metabolomics detected indole-3-acetic acid and kitasamycin, revealing mechanisms like hormonal regulation and antimicrobial secretion. In summary, YNK-FS0020 has potential for use in plant-growth promotion and disease control, aiding agricultural microbial resource utilization.

Xinyi NING, Yihan CHEN, Minjuan ZHAO

The development of Internet information technology has given digital agricultural technology extension services advantages over earlier agricultural technology extension models, rendering them more conducive to the pursuit of sustainable and environmentally friendly agricultural development. This study leveraged survey data from 1167 farmers in Shaanxi and Gansu Provinces and used the propensity score matching method to elucidate the impact and mechanism of the digital agricultural technology extension service on the adoption of organomineral fertilizer. The results indicate that farmers who had used digital agricultural technology extension services had a 7.2% to 10.2% increase in the probability of adopting organomineral fertilizer compared with their non-user counterparts. In addition, adoption intensity increased from 7.0% to 9.9%. Secondly, digital agricultural technology extension services indirectly influence farmer adoption behavior by shaping their perceptions of benefits and reducing transaction costs. Also, this study examined the heterogeneity in the adoption of organomineral fertilizer facilitated by digital agricultural technology extension services. The findings of this study provide policy recommendations for advancing the use of digital agricultural technology extension services and enhancing organomineral fertilize adoption rates of farmers.

Jones Akuaku, Rita Sam

The African baobab (Adansonia digitata L.) is a priority Pan-African tree species. Insect pests that are associated with and damage domesticated baobab are largely unknown in the production areas of baobab. To identify and document insect pests associated with domesticated African baobab for the first time, mature and young domesticated baobab plants were, respectively, surveyed on the research fields and nursery of the Ho Technical University in Ho, Ghana. The survey targeted all insects found on baobab with the goal of documenting pests that infest baobab plants. Collected insect samples were photographed and searched using Google Lens and the iNaturalist insect identification application for their identification and taxonomic classification. The entomological specimens collected were classified into 7 orders, 11 families, and 16 insect species. The most frequent orders were Hemiptera (37.5%) and Coleoptera (31.25%). The incidence of the remaining orders (Orthoptera, Lepidoptera, Hymenoptera, Araneae, and Dictyoptera) was very low with 6.25% abundance each. Regarding absolute counts, the Coleopteran order had a significantly (p≤0.05) higher number of insects (51.48 ± 7.42955) than the other orders; Araneae (4.70 ± 7.42955), Hemiptera (1.10 ± 7.42955), Dictyoptera (0.45 ± 7.42955), Orthoptera (0.40 ± 7.42955), Hymenoptera (0.30 ± 7.42955), and Lepidoptera (0.05 ± 7.42955). No significant difference was observed among these remaining orders. The cocoa weevil (Araecerus fasciculatus) was the most dominant insect pest. Some beneficial insects were also found on the baobab plants. Monitoring and management interventions, particularly integrated pest management (IPM), that target the identified insect pests can be implemented to ensure the sustainable cultivation of baobab. Further research is required to identify and classify insect pests that may not have been captured and identified in this study.

Sudhir Sornapudi, Rajhans Singh

Computer vision in agriculture is game-changing with its ability to transform farming into a data-driven, precise, and sustainable industry. Deep learning has empowered agriculture vision to analyze vast, complex visual data, but heavily rely on the availability of large annotated datasets. This remains a bottleneck as manual labeling is error-prone, time-consuming, and expensive. The lack of efficient labeling approaches inspired us to consider self-supervised learning as a paradigm shift, learning meaningful feature representations from raw agricultural image data. In this work, we explore how self-supervised representation learning unlocks the potential applicability to diverse agriculture vision tasks by eliminating the need for large-scale annotated datasets. We propose a lightweight framework utilizing SimCLR, a contrastive learning approach, to pre-train a ResNet-50 backbone on a large, unannotated dataset of real-world agriculture field images. Our experimental analysis and results indicate that the model learns robust features applicable to a broad range of downstream agriculture tasks discussed in the paper. Additionally, the reduced reliance on annotated data makes our approach more cost-effective and accessible, paving the way for broader adoption of computer vision in agriculture.

Emmanuel Kojo Gyamfi, Zag ElSayed, Jess Kropczynski et al.

By 2050, it is predicted that there will be 9 billion people on the planet, which will call for more production, lower costs, and the preservation of natural resources. It is anticipated that atypical occurrences and climate change will pose severe risks to agricultural output. It follows that a 70% or more significant rise in food output is anticipated. Smart farming, often known as agriculture 4.0, is a tech-driven revolution in agriculture with the goal of raising industry production and efficiency. Four primary trends are responsible for it: food waste, climate change, population shifts, and resource scarcity. The agriculture industry is changing as a result of the adoption of emerging technologies. Using cutting-edge technology like IoT, AI, and other sensors, smart farming transforms traditional production methods and international agricultural policies. The objective is to establish a value chain that is optimized to facilitate enhanced monitoring and decreased labor expenses. The agricultural sector has seen tremendous transformation as a result of the fourth industrial revolution, which has combined traditional farming methods with cutting-edge technology to increase productivity, sustainability, and efficiency. To effectively utilize the potential of technology gadgets in the agriculture sector, collaboration between governments, private sector entities, and other stakeholders is necessary. This paper covers Agriculture 4.0, looks at its possible benefits and drawbacks of the implementation methodologies, compatibility, reliability, and investigates the several digital tools that are being utilized to change the agriculture industry and how to mitigate the challenges.

Lameya Aldhaheri, Noor Alshehhi, Irfana Ilyas Jameela Manzil et al.

The emerging field of smart agriculture leverages the Internet of Things (IoT) to revolutionize farming practices. This paper investigates the transformative potential of Long Range (LoRa) technology as a key enabler of long-range wireless communication for agricultural IoT systems. By reviewing existing literature, we identify a gap in research specifically focused on LoRa's prospects and challenges from a communication perspective in smart agriculture. We delve into the details of LoRa-based agricultural networks, covering network architecture design, Physical Layer (PHY) considerations tailored to the agricultural environment, and channel modeling techniques that account for soil characteristics. The paper further explores relaying and routing mechanisms that address the challenges of extending network coverage and optimizing data transmission in vast agricultural landscapes. Transitioning to practical aspects, we discuss sensor deployment strategies and energy management techniques, offering insights for real-world deployments. A comparative analysis of LoRa with other wireless communication technologies employed in agricultural IoT applications highlights its strengths and weaknesses in this context. Furthermore, the paper outlines several future research directions to leverage the potential of LoRa-based agriculture 4.0. These include advancements in channel modeling for diverse farming environments, novel relay routing algorithms, integrating emerging sensor technologies like hyper-spectral imaging and drone-based sensing, on-device Artificial Intelligence (AI) models, and sustainable solutions. This survey can guide researchers, technologists, and practitioners to understand, implement, and propel smart agriculture initiatives using LoRa technology.

Mujadded Al Rabbani Alif, Muhammad Hussain

This survey investigates the transformative potential of various YOLO variants, from YOLOv1 to the state-of-the-art YOLOv10, in the context of agricultural advancements. The primary objective is to elucidate how these cutting-edge object detection models can re-energise and optimize diverse aspects of agriculture, ranging from crop monitoring to livestock management. It aims to achieve key objectives, including the identification of contemporary challenges in agriculture, a detailed assessment of YOLO's incremental advancements, and an exploration of its specific applications in agriculture. This is one of the first surveys to include the latest YOLOv10, offering a fresh perspective on its implications for precision farming and sustainable agricultural practices in the era of Artificial Intelligence and automation. Further, the survey undertakes a critical analysis of YOLO's performance, synthesizes existing research, and projects future trends. By scrutinizing the unique capabilities packed in YOLO variants and their real-world applications, this survey provides valuable insights into the evolving relationship between YOLO variants and agriculture. The findings contribute towards a nuanced understanding of the potential for precision farming and sustainable agricultural practices, marking a significant step forward in the integration of advanced object detection technologies within the agricultural sector.

Tiantian Zhao, Guowan Su, Lijun Zhang et al.

Abstract Food‐derived peptides have garnered significant attention in research due to their multifaceted functionalities, abundant availability, efficient utilization of agricultural by‐products, and environmentally sustainable preparation methods. These peptides play a crucial role in human health, yet their precise mechanisms of action remain largely unexplored, posing challenges in their screening, preparation, and effective application utilizing protein‐based raw materials. This review offers an extensive examination of 19 types of bioactive peptides derived from food. The sources of food‐derived bioactive peptides are well concluded and the classifications are made according to their potential health benefit based on five primary systems: general bodily functions, the nervous system, the cardiovascular system, the metabolic system, and the immune system. This review specifically highlights the multifaceted impacts of tasty peptides on human health, extending beyond their gustatory effects. Furthermore, it explores the interplay between various functions of bioactive peptides, noting a progression from basic to advanced functionalities. Antioxidant activity and the modulation of key enzymes are identified as fundamental actions that are interconnected with other functional properties. This implies that a single bioactive peptide could exhibit multiple beneficial effects. The key role of antioxidant capabilities is underscored based on their broad influence and straightforward assessment. This comprehensive analysis aims to deepen the systematic understanding of the diverse benefits offered by various food‐derived peptides.

Muhammad Shoaib Farooq, Shamyla Riaz, Atif Alvi

In the past few years, the Web of Things (WOT) became a beneficial game-changing technology within the Agriculture domain as it introduces innovative and promising solutions to the Internet of Things (IoT) agricultural applications problems by providing its services. WOT provides the support for integration, interoperability for heterogeneous devices, infrastructures, platforms, and the emergence of various other technologies. The main aim of this study is about understanding and providing a growing and existing research content, issues, and directions for the future regarding WOT-based agriculture. Therefore, a systematic literature review (SLR) of research articles is presented by categorizing the selected studies published between 2010 and 2020 into the following categories: research type, approaches, and their application domains. Apart from reviewing the state-of-the-art articles on WOT solutions for the agriculture field, a taxonomy of WOT-base agriculture application domains has also been presented in this study. A model has also presented to show the picture of WOT based Smart Agriculture. Lastly, the findings of this SLR and the research gaps in terms of open issues have been presented to provide suggestions on possible future directions for the researchers for future research.

Mohammad Raziuddin Chowdhury, Md Sakib Ullah Sourav, Rejwan Bin Sulaiman

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper health care, education, living conditions, wages, and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

Ali Leylavi Shoushtari

Agricultural robotics and automation are facing some challenges rooted in the high variability 9 of products, task complexity, crop quality requirement, and dense vegetation. Such a set of 10 challenges demands a more versatile and safe robotic system. Soft robotics is a young yet 11 promising field of research aimed to enhance these aspects of current rigid robots which 12 makes it a good candidate solution for that challenge. In general, it aimed to provide robots 13 and machines with adaptive locomotion (Ansari et al., 2015), safe and adaptive manipulation 14 (Arleo et al., 2020) and versatile grasping (Langowski et al., 2020). But in agriculture, soft 15 robots have been mainly used in harvesting tasks and more specifically in grasping. In this 16 chapter, we review a candidate group of soft grippers that were used for handling and 17 harvesting crops regarding agricultural challenges i.e. safety in handling and adaptability to 18 the high variation of crops. The review is aimed to show why and to what extent soft grippers 19 have been successful in handling agricultural tasks. The analysis carried out on the results 20 provides future directions for the systematic design of soft robots in agricultural tasks.

Md. Toaha, Laboni Mondal

The paper examined the impact of agricultural credit on economic growth in Bangladesh. The annual data of agriculture credit were collected from annual reports of the Bangladesh Bank and other data were collected from the world development indicator (WDI) of the World Bank. By employing Johansen cointegration test and vector error correction model (VECM), the study revealed that there exists a long run relationship between the variables. The results of the study showed that agriculture credit had a positive impact on GDP growth in Bangladesh. The study also found that gross capital formation had a positive, while inflation had a negative association with economic growth in Bangladesh. Therefore, the government and policymakers should continue their effort to increase the volume of agriculture credit to achieve sustainable economic growth.

Ribana Roscher, Lukas Roth, Cyrill Stachniss et al.

In response to the increasing global demand for food, feed, fiber, and fuel, digital agriculture is rapidly evolving to meet these demands while reducing environmental impact. This evolution involves incorporating data science, machine learning, sensor technologies, robotics, and new management strategies to establish a more sustainable agricultural framework. So far, machine learning research in digital agriculture has predominantly focused on model-centric approaches, focusing on model design and evaluation. These efforts aim to optimize model accuracy and efficiency, often treating data as a static benchmark. Despite the availability of agricultural data and methodological advancements, a saturation point has been reached, with many established machine learning methods achieving comparable levels of accuracy and facing similar limitations. To fully realize the potential of digital agriculture, it is crucial to have a comprehensive understanding of the role of data in the field and to adopt data-centric machine learning. This involves developing strategies to acquire and curate valuable data and implementing effective learning and evaluation strategies that utilize the intrinsic value of data. This approach has the potential to create accurate, generalizable, and adaptable machine learning methods that effectively and sustainably address agricultural tasks such as yield prediction, weed detection, and early disease identification

Alakananda Mitra, Sukrutha L. T. Vangipuram, Anand K. Bapatla et al.

The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer malnutrition. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. Agriculture 4.0 is also discussed as a whole. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We have divided open research problems of smart agriculture as future research work in two groups - from a technological perspective and from a networking perspective. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group.