Effective pest management is complex due to the need for accurate, context-specific decisions. Recent advancements in large language models (LLMs) open new possibilities for addressing these challenges by providing sophisticated, adaptive knowledge acquisition and reasoning. However, existing LLM-based pest management approaches often rely on a single-agent paradigm, which can limit their capacity to incorporate diverse external information, engage in systematic validation, and address complex, threshold-driven decisions. To overcome these limitations, we introduce PestMA, an LLM-based multi-agent system (MAS) designed to generate reliable and evidence-based pest management advice. Building on an editorial paradigm, PestMA features three specialized agents, an Editor for synthesizing pest management recommendations, a Retriever for gathering relevant external data, and a Validator for ensuring correctness. Evaluations on real-world pest scenarios demonstrate that PestMA achieves an initial accuracy of 86.8% for pest management decisions, which increases to 92.6% after validation. These results underscore the value of collaborative agent-based workflows in refining and validating de
Pest infestations pose a significant risk to both the quality and quantity of stored grain, resulting in substantial economic losses. Accurate and timely pest monitoring is essential, but traditional methods are time-consuming and labor-intensive, and often ineffective at detecting pests beneath the grain surface. In this paper, we introduce a systematic framework: Pest Manager for precise pest counting and identification within the invisible grain pile environment. The framework consists of three components: an improved grain probe trap PestMoni, a pest drop dataset PestSet collected by PestMoni, and a multi-task Transformer-based architecture PestFormer for pest counting and identification. Specifically, PestMoni uses an asymmetric layout of infrared diodes and photodiodes, enabling precise recording of pest drops. Based on PestMoni, we develop PestSet, an infrared-perception dataset for pest drops, including five common grain storage pests. Furthermore, we propose PestFormer, a multi-task model with a conditional modification module to reduce deviations across different PestMonis and thereby normalize the data processing workflow. Extensive experiments validate the design, datas
We formulate an integrated pest management model to control natural pests of the crop through the periodic application of biopesticide and chemical pesticides. In a theoretical analysis of the system pest eradication, a periodic solution is found and established. All the system variables are proved to be bounded. Our main goal is then to ensure that pesticides are optimized, in terms of pesticide concentration and pesticide application frequency, and that the optimum combination of pesticides is found to provide the most benefit to the crop. By using Floquet theory and the small amplitude perturbation method, we prove that the pest eradication periodic solution is locally and globally stable. The acquired results establish a threshold time limit for the impulsive release of various controls as well as some valid theoretical conclusions for effective pest management. Furthermore, after a numerical comparison, we conclude that integrated pest management is more effective than single biological or chemical controls. Finally, we illustrate the analytical results through numerical simulations.
Effective pest management is crucial for enhancing agricultural productivity, especially for crops such as sugarcane and wheat that are highly vulnerable to pest infestations. Traditional pest management methods depend heavily on manual field inspections and the use of chemical pesticides. These approaches are often costly, time-consuming, labor-intensive, and can have a negative impact on the environment. To overcome these challenges, this study presents a lightweight framework for pest detection and pesticide recommendation, designed for low-resource devices such as smartphones and drones, making it suitable for use by small and marginal farmers. The proposed framework includes two main components. The first is a Pest Detection Module that uses a compact, lightweight convolutional neural network (CNN) combined with prototypical meta-learning to accurately identify pests even when only a few training samples are available. The second is a Pesticide Recommendation Module that incorporates environmental factors like crop type and growth stage to suggest safe and eco-friendly pesticide recommendations. To train and evaluate our framework, a comprehensive pest image dataset was develo
Urban forests provide ecosystem services that are public goods with local (shade) to global (carbon sequestration) benefits and occur on both public and private lands. Thus, incentives for private tree owners to invest in tree care may fall short of those of a public forest manager aiming to optimize ecosystem service benefits for society. The management of a forest pest provides a salient focus area because pests threaten public goods provision and pest management generates feedback that mitigates future risks to forests. We use a game theoretic model to determine optimal pest treatment subsidies for a focal privately owned tree and use an optimization approach to guide targeted public treatment of a representative public tree. We find that optimal public subsidies for private tree treatment depend on assessed tree health and on the prevalence of the pest in the community, considerations absent from many existing programs. Next, by applying our pest treatment policies to a community-scale model of emerald ash borer forest pest dynamics, we predict ash mortality under a range of treatment scenarios over a 50-year time horizon. Our results highlight how designing policies that consi
This paper proposes a graph-augmented reasoning framework for tobacco pest and disease management that integrates structured domain knowledge into large language models. Building on GraphRAG, we construct a domain-specific knowledge graph and retrieve query-relevant subgraphs to provide relational evidence during answer generation. The framework adopts ChatGLM as the Transformer backbone with LoRA-based parameter-efficient fine-tuning, and employs a graph neural network to learn node representations that capture symptom-disease-treatment dependencies. By explicitly modeling diseases, symptoms, pesticides, and control measures as linked entities, the system supports evidence-aware retrieval beyond surface-level text similarity. Retrieved graph evidence is incorporated into the LLM input to guide generation toward domain-consistent recommendations and to mitigate hallucinated or inappropriate treatments. Experimental results show consistent improvements over text-only baselines, with the largest gains observed on multi-hop and comparative reasoning questions that require chaining multiple relations.
Effective pest recognition and management are crucial for sustainable agricultural development. However, collecting pest data in real scenarios is often challenging. Compared to other domains, pests exhibit a wide variety of species with complex and diverse morphological characteristics. Existing techniques struggle to effectively model the key visual and high-level semantic features of pests in a fine-grained manner. These limitations hinder the practical application of such methods in real agricultural scenarios. To address these critical challenges, we present a synergistic approach that integrates PestVL-Net, a novel vision-language framework, with two multi-species pest datasets to facilitate fine-grained pest learning. The visual pathway of PestVL-Net utilizes the Recurrent Weighted Key Value (RWKV) architecture, incorporating a saliency-guided adaptive window partitioning scheme to effectively model the fine-grained visual characteristics of pests. Concurrently, the linguistic component generates precise pest semantic descriptions by leveraging Multimodal Large Language Models (MLLMs) priors, critically informed by agricultural expert knowledge and structured via multimodal
In the rapidly evolving field of artificial intelligence (AI), the application of large language models (LLMs) in agriculture, particularly in pest management, remains nascent. We aimed to prove the feasibility by evaluating the content of the pest management advice generated by LLMs, including the Generative Pre-trained Transformer (GPT) series from OpenAI and the FLAN series from Google. Considering the context-specific properties of agricultural advice, automatically measuring or quantifying the quality of text generated by LLMs becomes a significant challenge. We proposed an innovative approach, using GPT-4 as an evaluator, to score the generated content on Coherence, Logical Consistency, Fluency, Relevance, Comprehensibility, and Exhaustiveness. Additionally, we integrated an expert system based on crop threshold data as a baseline to obtain scores for Factual Accuracy on whether pests found in crop fields should take management action. Each model's score was weighted by percentage to obtain a final score. The results showed that GPT-3.4 and GPT-4 outperform the FLAN models in most evaluation categories. Furthermore, the use of instruction-based prompting containing domain-spe
Pesticides serve as a common tool in agricultural pest control but significantly contribute to the climate crisis. To combat this, Integrated Pest Management (IPM) stands as a climate-smart alternative. Despite its potential, IPM faces low adoption rates due to farmers' skepticism about its effectiveness. To address this challenge, we introduce an advanced data analysis framework tailored to enhance IPM adoption. Our framework provides i) robust pest population predictions across diverse environments with invariant and causal learning, ii) interpretable pest presence predictions using transparent models, iii) actionable advice through counterfactual explanations for in-season IPM interventions, iv) field-specific treatment effect estimations, and v) assessments of the effectiveness of our advice using causal inference. By incorporating these features, our framework aims to alleviate skepticism and encourage wider adoption of IPM practices among farmers.
The soybean aphid (\emph{Aphis glycines}) is an invasive insect pest that continues to cause large-scale damage to soybean crops in the North Central United States. The current manuscript proposes several mathematical models for the top-down bio-control of the aphid, as well as control via pesticides and neonicotinoids. The models are motivated empirically, and constructed based on laboratory experiments conducted to test control of aphids by Lacewing larvae, as well as by a parasitic wasp (\emph{Aphidius colemani}). The effectiveness of these models is compared by taking into account factors such as economic injury levels for soybeans, life history traits such as cannibalism amongst the predator, and intraguild predation between competing bio-control agents such as predators and parasitoids. The models predict multiple population peaks and transient chaotic dynamics when a predator and/or insecticides are used. It is observed that parasitoids, in conjunction with predators, are more efficient at stabilizing the population dynamics than insecticide use. They also suggest a combination of predators, parasitoids, and insecticides would be more efficient at suppressing aphid populatio
Biotic pest attacks and infestations are major causes of stored grain losses, leading to significant food and economic losses. Conventional, manual, sampling-based pest recognition methods are labor-intensive, time-consuming, costly, require expertise, and may not even detect hidden infestations. In recent years, the electronic nose (e-nose) approach has emerged as a potential alternative for agricultural grain pest recognition and monitoring. An e-nose mimics human olfactory systems by integrating a sensor array, data acquisition, and analysis for recognizing grain pests by analyzing volatile organic compounds (VOCs) emitted by grain and pests. However, well-documented, curated, and synthesized literature on the use of e-nose technology for grain pest detection is lacking. Therefore, this systematic literature review provides a comprehensive overview of the current state-of-the-art e-nose technology for agricultural grain pest monitoring. The review examines employed sensor technology, targeted pest species type, grain medium, data processing, and pattern recognition techniques. An e-nose is a promising tool that offers a rapid, low-cost, non-destructive solution for detecting, id
The decline of single authorship in peer-reviewed journals within the current collaboration-oriented knowledge production framework has prompted deeper reflection on the shifting power structures in academic systems. This paper aims to explore the underlying institutional logic and evaluation mechanisms contributing to the marginalization of single-author research in the management field. It further investigates how the discourse of collaborative advantage conceals structural power redistribution and ideological disembedding. Through an analysis of authorship data from top-tier journals, a critical reading of institutional incentive texts, and an empirical review of authorial configurations, the study building on the work of Harzing, Wuchty, and Lariviere constructs a three-dimensional causal chain: collaboration incentives, responsibility dilution, and originality weakening. Findings suggest that single authorship is not explicitly excluded but is gradually sidelined from central publication channels by funding policies, review practices, and performance metrics. Independent thought is thus structurally marginalized within institutionalized collaboration. The paper advocates for a
UK woodlands, forests, and urban treescapes are under threat from invasive species, exacerbated by climate change, trade, and transport. Invasive tree pests debilitate their host and disrupt forest ecosystems, thus it is imperative to quantitatively model and predict their spread. Addressing this, we model the spread of an invasive pest using a spatiotemporal reaction-diffusion equation, representing the spatial distribution as a population density field. We solve this intractable equation numerically and, from the solution, we determine first arrival times of the pest at locations in the field. The adopted model permits us to obtain the expansion rate of pest spread directly from the model parameters, which we infer in the Bayesian paradigm, using a Markov chain Monte Carlo scheme. We apply our framework to the ongoing spread of oak processionary moth in the UK, an outbreak which continues to grow despite management efforts. We demonstrate that our approach effectively captures the spread of the pest and that this has occurred at a non-constant expansion rate. The proposed framework is a powerful tool for quantitatively modelling the spread of an invasive tree pest and could under
Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orchestrating the execution of end-to-end processes. The rise of Generative and Agentic Artificial Intelligence (AI) is opening the way for another such wave. However, this wave is poised to be different because it shifts the focus from automation to autonomy and from design-driven management of business processes to data-driven management, leveraging process mining techniques. This position paper, based on a keynote talk at the 2025 Workshop on AI for BPM, outlines how process mining has laid the foundations on top of which agents can sense process states, reason about improvement opportunities, and act to maintain and optimize performance. The paper proposes an architectural vision for Agentic Business Process Management Systems (A-BPMS): a new class of platforms that integrate autonomy, reasoning, and learning into process management and execution. The paper contends that such systems must support a continuum of processes, spanning from
Accurate pest population monitoring and tracking their dynamic changes are crucial for precision agriculture decision-making. A common limitation in existing vision-based automatic pest counting research is that models are typically evaluated on datasets with ground truth but deployed in real-world scenarios without assessing the reliability of counting results due to the lack of ground truth. To this end, this paper proposed a method for comprehensively evaluating pest counting confidence in the image, based on information related to counting results and external environmental conditions. First, a pest detection network is used for pest detection and counting, extracting counting result-related information. Then, the pest images undergo image quality assessment, image complexity assessment, and pest distribution uniformity assessment. And the changes in image clarity caused by stirring during image acquisition are quantified by calculating the average gradient magnitude. Notably, we designed a hypothesis-driven multi-factor sensitivity analysis method to select the optimal image quality assessment and image complexity assessment methods. And we proposed an adaptive DBSCAN clusteri
This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering's transformative potential. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.
We investigate a mathematical model in crop pest controlling, considering plant biomass, pest, and the effect of farming awareness. The pest population is divided into two compartments: susceptible pests and infected pests. We assume that the growth rate of self-aware people is proportional to the density of susceptible pests existing in the crop arena. Impacts of awareness are modeled through the usual mass action term and a saturated term. It is further assumed that self-aware people will adopt chemical and biological control methods, namely integrated pest management. Bio-pesticides are costly and require a long-term process, expensive to impose. However, if chemical pesticides are introduced in the farming system along with bio-pesticides, the process will be faster as well as cost-effective. Also, farming knowledge is equally important. In this article, a mathematical model is derived for controlling crop pests through an awareness-based integrated approach. In order to reduce the negative effects of pesticides, we apply optimal control theory.
Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort. It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required.
The large instantaneous sensitivity, a wide frequency coverage and flexible observation modes with large number of beams in the sky are the main features of the SKA observatory's two telescopes, the SKA-Low and the SKA-Mid, which are located on two different continents. Owing to these capabilities, the SKAO telescopes are going to be a game-changer for radio astronomy in general and pulsar astronomy in particular. The eleven articles in this special issue on pulsar science with the SKA Observatory describe its impact on different areas of pulsar science. In this lead article, a brief description of the two telescopes highlighting the relevant features for pulsar science is presented followed by an overview of each accompanying article, exploring the inter-relationship between different pulsar science use cases.
GREX-PLUS (Galaxy Reionization EXplorer and PLanetary Universe Spectrometer) is a mission candidate for a JAXA strategic L-class mission to be launched in the 2030s. Its primary science goals are two-fold: galaxy formation and evolution, and planetary system formation and evolution. The GREX-PLUS spacecraft will carry a telescope with a 1 m primary mirror aperture cooled down to 50 K. The two science instruments will be onboard: a wide-field camera in the 2--8 $μ$m wavelength band and a high-resolution spectrometer with a wavelength resolution of 30,000 in the 10--18 $μ$m band. The GREX-PLUS wide-field camera aims to detect the first generation of galaxies at redshift $z>15$. The GREX-PLUS high-resolution spectrometer aims to identify the location of the water ``snowline'' in protoplanetary disks. Both instruments will provide unique datasets for a broad range of scientific topics, including galaxy mass assembly, the origin of supermassive blackholes, infrared background radiation, molecular spectroscopy in the interstellar medium, transit spectroscopy of exoplanet atmospheres, planetary atmospheres in the Solar System, and so on. This document is the second version of a collect