Hasil untuk "Agriculture"

P. Calvo, L. Nelson, J. Kloepper

BackgroundPlant biostimulants are diverse substances and microorganisms used to enhance plant growth. The global market for biostimulants is projected to increase 12 % per year and reach over $2,200 million by 2018. Despite the growing use of biostimulants in agriculture, many in the scientific community consider biostimulants to be lacking peer-reviewed scientific evaluation.ScopeThis article describes the emerging definitions of biostimulants and reviews the literature on five categories of biostimulants: i. microbial inoculants, ii. humic acids, iii. fulvic acids, iv. protein hydrolysates and amino acids, and v. seaweed extracts.ConclusionsThe large number of publications cited for each category of biostimulants demonstrates that there is growing scientific evidence supporting the use of biostimulants as agricultural inputs on diverse plant species. The cited literature also reveals some commonalities in plant responses to different biostimulants, such as increased root growth, enhanced nutrient uptake, and stress tolerance.

D. Ray, N. Mueller, P. West et al.

Several studies have shown that global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Boosting crop yields to meet these rising demands, rather than clearing more land for agriculture has been highlighted as a preferred solution to meet this goal. However, we first need to understand how crop yields are changing globally, and whether we are on track to double production by 2050. Using ∼2.5 million agricultural statistics, collected for ∼13,500 political units across the world, we track four key global crops—maize, rice, wheat, and soybean—that currently produce nearly two-thirds of global agricultural calories. We find that yields in these top four crops are increasing at 1.6%, 1.0%, 0.9%, and 1.3% per year, non-compounding rates, respectively, which is less than the 2.4% per year rate required to double global production by 2050. At these rates global production in these crops would increase by ∼67%, ∼42%, ∼38%, and ∼55%, respectively, which is far below what is needed to meet projected demands in 2050. We present detailed maps to identify where rates must be increased to boost crop production and meet rising demands.

L. Philippot, J. Raaijmakers, P. Lemanceau et al.

B. Glick

The worldwide increases in both environmental damage and human population pressure have the unfortunate consequence that global food production may soon become insufficient to feed all of the world's people. It is therefore essential that agricultural productivity be significantly increased within the next few decades. To this end, agricultural practice is moving toward a more sustainable and environmentally friendly approach. This includes both the increasing use of transgenic plants and plant growth-promoting bacteria as a part of mainstream agricultural practice. Here, a number of the mechanisms utilized by plant growth-promoting bacteria are discussed and considered. It is envisioned that in the not too distant future, plant growth-promoting bacteria (PGPB) will begin to replace the use of chemicals in agriculture, horticulture, silviculture, and environmental cleanup strategies. While there may not be one simple strategy that can effectively promote the growth of all plants under all conditions, some of the strategies that are discussed already show great promise.

J. Foley, N. Ramankutty, K. Brauman et al.

J. Guo, Xuejun Liu, Y. Zhang et al.

Cropland Acidification in China China is experiencing increasing problems with acid rain, groundwater pollution, and nitrous oxide emissions. Rapid development of industry and transportation has accelerated nitrate (N) emissions to the atmosphere. Consequently, soil degradation, water shortage, and pollution, in addition to atmospheric quality decline are becoming major public concerns across China. Since the 1990s, China has become both the largest consumer of chemical N fertilizers and the highest cereal producer in the world, which has consequences for arable soil acidification. Guo et al. (p. 1008, published online 11 February) present a meta-analysis of a regional acidification phenomenon in Chinese arable soils that is largely associated with higher N fertilization and higher crop production. Such large-scale soil acidification is likely to threaten the sustainability of agriculture and affect the biogeochemical cycles of nutrients and also toxic elements in soils. Intensifying agriculture in China in the past 30 years is the major contributor to soil acidification at the regional scale. Soil acidification is a major problem in soils of intensive Chinese agricultural systems. We used two nationwide surveys, paired comparisons in numerous individual sites, and several long-term monitoring-field data sets to evaluate changes in soil acidity. Soil pH declined significantly (P < 0.001) from the 1980s to the 2000s in the major Chinese crop-production areas. Processes related to nitrogen cycling released 20 to 221 kilomoles of hydrogen ion (H+) per hectare per year, and base cations uptake contributed a further 15 to 20 kilomoles of H+ per hectare per year to soil acidification in four widespread cropping systems. In comparison, acid deposition (0.4 to 2.0 kilomoles of H+ per hectare per year) made a small contribution to the acidification of agricultural soils across China.

D. Cordell, J. Drangert, S. White

S. Carpenter, N. Caraco, D. Correll et al.

K. Renard, G. R. Foster, G. Weesies et al.

R. Adams, C. Rosenzweig, R. Peart et al.

Arafa Yoncalik, Wouter Jansen, Nico Huebel et al.

Procedural generation techniques in 3D rendering engines have revolutionized the creation of complex environments, reducing reliance on manual design. Recent approaches using Large Language Models (LLMs) for 3D scene generation show promise but often lack domain-specific reasoning, verification mechanisms, and modular design. These limitations lead to reduced control and poor scalability. This paper investigates the use of LLMs to generate agricultural synthetic simulation environments from natural language prompts, specifically to address the limitations of lacking domain-specific reasoning, verification mechanisms, and modular design. A modular multi-LLM pipeline was developed, integrating 3D asset retrieval, domain knowledge injection, and code generation for the Unreal rendering engine using its API. This results in a 3D environment with realistic planting layouts and environmental context, all based on the input prompt and the domain knowledge. To enhance accuracy and scalability, the system employs a hybrid strategy combining LLM optimization techniques such as few-shot prompting, Retrieval-Augmented Generation (RAG), finetuning, and validation. Unlike monolithic models, the modular architecture enables structured data handling, intermediate verification, and flexible expansion. The system was evaluated using structured prompts and semantic accuracy metrics. A user study assessed realism and familiarity against real-world images, while an expert comparison demonstrated significant time savings over manual scene design. The results confirm the effectiveness of multi-LLM pipelines in automating domain-specific 3D scene generation with improved reliability and precision. Future work will explore expanding the asset hierarchy, incorporating real-time generation, and adapting the pipeline to other simulation domains beyond agriculture.

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/.

Zhihao Zhan, Yuhang Ming, Shaobin Li et al.

Multi-sensor Simultaneous Localization and Mapping (SLAM) is essential for Unmanned Aerial Vehicles (UAVs) performing agricultural tasks such as spraying, surveying, and inspection. However, real-world, multi-modal agricultural UAV datasets that enable research on robust operation remain scarce. To address this gap, we present AgriLiRa4D, a multi-modal UAV dataset designed for challenging outdoor agricultural environments. AgriLiRa4D spans three representative farmland types-flat, hilly, and terraced-and includes both boundary and coverage operation modes, resulting in six flight sequence groups. The dataset provides high-accuracy ground-truth trajectories from a Fiber Optic Inertial Navigation System with Real-Time Kinematic capability (FINS_RTK), along with synchronized measurements from a 3D LiDAR, a 4D Radar, and an Inertial Measurement Unit (IMU), accompanied by complete intrinsic and extrinsic calibrations. Leveraging its comprehensive sensor suite and diverse real-world scenarios, AgriLiRa4D supports diverse SLAM and localization studies and enables rigorous robustness evaluation against low-texture crops, repetitive patterns, dynamic vegetation, and other challenges of real agricultural environments. To further demonstrate its utility, we benchmark four state-of-the-art multi-sensor SLAM algorithms across different sensor combinations, highlighting the difficulty of the proposed sequences and the necessity of multi-modal approaches for reliable UAV localization. By filling a critical gap in agricultural SLAM datasets, AgriLiRa4D provides a valuable benchmark for the research community and contributes to advancing autonomous navigation technologies for agricultural UAVs. The dataset can be downloaded from: https://zhan994.github.io/AgriLiRa4D.

Anas Abouaomar, Mohammed El hanjri, Abdellatif Kobbane et al.

In this paper, we presents a novel hierarchical federated learning architecture specifically designed for smart agricultural production systems and crop yield prediction. Our approach introduces a seasonal subscription mechanism where farms join crop-specific clusters at the beginning of each agricultural season. The proposed three-layer architecture consists of individual smart farms at the client level, crop-specific aggregators at the middle layer, and a global model aggregator at the top level. Within each crop cluster, clients collaboratively train specialized models tailored to specific crop types, which are then aggregated to produce a higher-level global model that integrates knowledge across multiple crops. This hierarchical design enables both local specialization for individual crop types and global generalization across diverse agricultural contexts while preserving data privacy and reducing communication overhead. Experiments demonstrate the effectiveness of the proposed system, showing that local and crop-layer models closely follow actual yield patterns with consistent alignment, significantly outperforming standard machine learning models. The results validate the advantages of hierarchical federated learning in the agricultural context, particularly for scenarios involving heterogeneous farming environments and privacy-sensitive agricultural data.

Dewi Endah Kharismawati, Toni Kazic

Accurate maize seedling detection is crucial for precision agriculture, yet curated datasets remain scarce. We introduce MSDD, a high-quality aerial image dataset for maize seedling stand counting, with applications in early-season crop monitoring, yield prediction, and in-field management. Stand counting determines how many plants germinated, guiding timely decisions such as replanting or adjusting inputs. Traditional methods are labor-intensive and error-prone, while computer vision enables efficient, accurate detection. MSDD contains three classes-single, double, and triple plants-capturing diverse growth stages, planting setups, soil types, lighting conditions, camera angles, and densities, ensuring robustness for real-world use. Benchmarking shows detection is most reliable during V4-V6 stages and under nadir views. Among tested models, YOLO11 is fastest, while YOLOv9 yields the highest accuracy for single plants. Single plant detection achieves precision up to 0.984 and recall up to 0.873, but detecting doubles and triples remains difficult due to rarity and irregular appearance, often from planting errors. Class imbalance further reduces accuracy in multi-plant detection. Despite these challenges, YOLO11 maintains efficient inference at 35 ms per image, with an additional 120 ms for saving outputs. MSDD establishes a strong foundation for developing models that enhance stand counting, optimize resource allocation, and support real-time decision-making. This dataset marks a step toward automating agricultural monitoring and advancing precision agriculture.

Mohammad Usman Altam, Md Imtiaz Habib, Tuan Hoang

Retrieval-Augmented Generation (RAG) represents a transformative approach within natural language processing (NLP), combining neural information retrieval with generative language modeling to enhance both contextual accuracy and factual reliability of responses. Unlike conventional Large Language Models (LLMs), which are constrained by static training corpora, RAG-powered systems dynamically integrate domain-specific external knowledge sources, thereby overcoming temporal and disciplinary limitations. In this study, we present the design and evaluation of a RAG-enabled system tailored for Mycophyto, with a focus on advancing agricultural applications related to arbuscular mycorrhizal fungi (AMF). These fungi play a critical role in sustainable agriculture by enhancing nutrient acquisition, improving plant resilience under abiotic and biotic stresses, and contributing to soil health. Our system operationalizes a dual-layered strategy: (i) semantic retrieval and augmentation of domain-specific content from agronomy and biotechnology corpora using vector embeddings, and (ii) structured data extraction to capture predefined experimental metadata such as inoculation methods, spore densities, soil parameters, and yield outcomes. This hybrid approach ensures that generated responses are not only semantically aligned but also supported by structured experimental evidence. To support scalability, embeddings are stored in a high-performance vector database, allowing near real-time retrieval from an evolving literature base. Empirical evaluation demonstrates that the proposed pipeline retrieves and synthesizes highly relevant information regarding AMF interactions with crop systems, such as tomato (Solanum lycopersicum). The framework underscores the potential of AI-driven knowledge discovery to accelerate agroecological innovation and enhance decision-making in sustainable farming systems.

Övgü Gencer

The blue crab (Callinectes sapidus, Rathbun 1896) has become a significant source of raw materials in biotechnology and nanotechnology due to the biomaterials present in its shell. Natural polymers such as chitin and chitosan, derived from the crab's shell, are particularly noteworthy for their environmentally friendly and biologically compatible properties. These biopolymers provide an innovative alternative in the synthesis of quantum dots (QDs). Quantum dots are favored in various applications, including biomedical imaging, environmental sensors, and energy storage, due to their superior optoelectronic properties. Chitosan obtained from blue crab shells acts as both a stabilizer and a coating agent in the green synthesis of quantum dots. This process minimizes the use of toxic chemicals, thus promoting environmental sustainability. Moreover, the antimicrobial and biodegradable properties of chitosan enhance its usability in biomedical applications. For instance, biocompatible carbon-based quantum dots have shown promising results in cancer diagnostics and drug delivery systems. The synthesis of quantum dots using biomaterials is more cost-effective and environmentally friendly compared to traditional methods. Furthermore, utilizing blue crab shells as a waste material contributes to both marine ecosystem preservation and the circular economy. These synthesis methods are reported to create a significant paradigm shift in the field of sustainable technology development. In conclusion, the synthesis of quantum dots using biomaterials derived from blue crabs has the potential to reduce environmental impacts while serving advanced technological applications. This approach significantly contributes to the development of biotechnological innovations and sustainable development goals.

Santu Mondal, Sneha Ray, Aritra Acharyya et al.

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent quantum drift-diffusion (SCQDD) simulations of GaN IMPATT structures designed for a wide frequency range from the microwave frequency bands, up to 5 THz, is used to train the ANN models. The models effectively capture the impact of variations in structural, doping, and biasing parameters on device performance. The proposed ANN approach significantly reduces computational time for predicting breakdown characteristics, power output, and conversion efficiency properties of IMPATT sources, achieving similar accuracy to traditional SCQDD simulations while requiring only 7.8&#x2013;20.1% of the computational time. Mean square errors are observed to be on the order of <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula>, demonstrating the models&#x2019; high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices.

Angels Balaguer, Vinamra Benara, Renato Luiz de Freitas Cunha et al.

There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning. RAG augments the prompt with the external data, while fine-Tuning incorporates the additional knowledge into the model itself. However, the pros and cons of both approaches are not well understood. In this paper, we propose a pipeline for fine-tuning and RAG, and present the tradeoffs of both for multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4. Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results. We propose metrics to assess the performance of different stages of the RAG and fine-Tuning pipeline. We conduct an in-depth study on an agricultural dataset. Agriculture as an industry has not seen much penetration of AI, and we study a potentially disruptive application - what if we could provide location-specific insights to a farmer? Our results show the effectiveness of our dataset generation pipeline in capturing geographic-specific knowledge, and the quantitative and qualitative benefits of RAG and fine-tuning. We see an accuracy increase of over 6 p.p. when fine-tuning the model and this is cumulative with RAG, which increases accuracy by 5 p.p. further. In one particular experiment, we also demonstrate that the fine-tuned model leverages information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%. Overall, the results point to how systems built using LLMs can be adapted to respond and incorporate knowledge across a dimension that is critical for a specific industry, paving the way for further applications of LLMs in other industrial domains.