Landscape heterogeneity often increases natural enemy abundance, yet its effects on crop pest suppression are strikingly inconsistent across empirical studies. We developed a trait-based simulation framework to identify the general mechanisms linking landscape structure to realized pest load. Across >150 in silico experiments, we show that landscape attributes influence biological control by altering two early-season predator metrics: (i) the immigration rate from the surrounding landscape and (ii) the rate at which colonizing predators accumulate energy after arriving in the crop. These two conditions form universal causal pathways from landscape structure to pest suppression, but their relative importance depends on pest traits. Suppression of slow-growing, diffusely distributed pests requires high early-season energy accumulation by predators; rapidly growing and aggregated pests are controlled by increased predator immigration alone; suppression of rapid but diffuse pests requires both, and is weak even under favorable conditions. These three suppression fingerprints explain why landscape heterogeneity reliably increases natural enemy arrival yet only sometimes reduces pest load. Our results reveal a general, trait-mediated architecture—the conditional ecology of pest suppression—that reconciles inconsistent field findings, predicts when landscape heterogeneity should enhance biological control, and provides a mechanistic framework for using landscape management to support sustainable agriculture.
Agricultural disease diagnosis challenges VLMs, as conventional fine-tuning requires extensive labels, lacks interpretability, and generalizes poorly. While reasoning improves model robustness, existing methods rely on costly expert annotations and rarely address the open-ended, diverse nature of agricultural queries. To address these limitations, we propose \textbf{Agri-R1}, a reasoning-enhanced large model for agriculture. Our framework automates high-quality reasoning data generation via vision-language synthesis and LLM-based filtering, using only 19\% of available samples. Training employs Group Relative Policy Optimization (GRPO) with a novel proposed reward function that integrates domain-specific lexicons and fuzzy matching to assess both correctness and linguistic flexibility in open-ended responses. Evaluated on CDDMBench, our resulting 3B-parameter model achieves performance competitive with 7B- to 13B-parameter baselines, showing a +23.2\% relative gain in disease recognition accuracy, +33.3\% in agricultural knowledge QA, and a +26.10-point improvement in cross-domain generalization over standard fine-tuning. Ablation studies confirm that the synergy between structured reasoning data and GRPO-driven exploration underpins these gains, with benefits scaling as question complexity increases.
Agricultural product branding promotes regional economic development by enhancing brand value and market competitiveness, serving as a vital pathway for increasing farmers’ incomes and advancing the transformation of modern agriculture. This paper transcends one-dimensional analysis by examining the dual perspectives of urban-rural income disparities and regional income gaps, thereby revealing the impact of regional agricultural product branding on income inequality. This study employs panel data from 82 counties in Guangdong Province spanning the years 2010 to 2023, comprising a total of 1148 observations, and treats the Ministry of Agriculture and Rural Affairs’ designation of “famous, special, excellent, and new” agricultural products as a policy hit. Employing a multi-period difference-in-differences model, it empirically examines the impact of regional agricultural product branding (RAPB) on income inequality. The study found the following: (1) RAPB narrowed the urban-rural income gap by 0.92% and Theil decreased significantly by about 15.3% on average. (2) Mechanism analysis indicates that RAPB mitigates income inequality through resource allocation effects, technological progress effects, and human capital accumulation effects. (3) Heterogeneity tests reveal that the inequality-alleviating effect of RAPB is most robust in regions focused on crop cultivation and areas with lower levels of agribusiness vitality, while its effect is weakened in dynamic entrepreneurial and high-yield regions. This study provides a new value metric for evaluating regional brand policies that balance efficiency and equity, revealing their core potential in promoting social fairness and coordinating urban-rural and regional development.
Alexander J. Reisinger, Andrea Albertin, Eban Bean
et al.
Water quality is a broad term used to describe a range of physical, chemical, and/or biological characteristics of water. Many different factors contribute to water quality. The decision of whether the quality of a given water body is good or bad, whether it is acceptable or unacceptable, varies from place to place and depends on the intended use of that water body. In Florida, water quality criteria have been established for six different types of water bodies, and these criteria vary by the use of each water body type. This publication defines general terminology and approaches used to describe water quality. It is targeted towards individuals who have an interest in water quality issues but may not have training in the specific details of these issues. Ultimately, this publication will allow the reader to have a deeper understanding of specific water quality issues and regulations.
Precision agriculture, enhanced by artificial intelligence (AI), offers promising tools such as remote sensing, intelligent irrigation, fertilization management, and crop simulation to improve agricultural efficiency and sustainability. Reinforcement learning (RL), in particular, has outperformed traditional methods in optimizing yields and resource management. However, widespread AI adoption is limited by gaps between algorithmic recommendations and farmers' practical experience, local knowledge, and traditional practices. To address this, our study emphasizes Human-AI Interaction (HAII), focusing on transparency, usability, and trust in RL-based farm management. We employ a well-established trust framework - comprising ability, benevolence, and integrity - to develop a novel mathematical model quantifying farmers' confidence in AI-based fertilization strategies. Surveys conducted with farmers for this research reveal critical misalignments, which are integrated into our trust model and incorporated into a multi-objective RL framework. Unlike prior methods, our approach embeds trust directly into policy optimization, ensuring AI recommendations are technically robust, economically feasible, context-aware, and socially acceptable. By aligning technical performance with human-centered trust, this research supports broader AI adoption in agriculture.
The agricultural sector is undergoing a transformation with the integration of advanced technologies, particularly in data-driven decision-making. This work proposes a federated learning framework for smart farming, aiming to develop a scalable, efficient, and secure solution for crop disease detection tailored to the environmental and operational conditions of Minnesota farms. By maintaining sensitive farm data locally and enabling collaborative model updates, our proposed framework seeks to achieve high accuracy in crop disease classification without compromising data privacy. We outline a methodology involving data collection from Minnesota farms, application of local deep learning algorithms, transfer learning, and a central aggregation server for model refinement, aiming to achieve improved accuracy in disease detection, good generalization across agricultural scenarios, lower costs in communication and training time, and earlier identification and intervention against diseases in future implementations. We outline a methodology and anticipated outcomes, setting the stage for empirical validation in subsequent studies. This work comes in a context where more and more demand for data-driven interpretations in agriculture has to be weighed with concerns about privacy from farms that are hesitant to share their operational data. This will be important to provide a secure and efficient disease detection method that can finally revolutionize smart farming systems and solve local agricultural problems with data confidentiality. In doing so, this paper bridges the gap between advanced machine learning techniques and the practical, privacy-sensitive needs of farmers in Minnesota and beyond, leveraging the benefits of federated learning.
Purpose: The study examines the Open Access (OA) landscape of Indian state agricultural universities, focusing on OA growth, leading institutions, prolific authors, preferred sources, funding, APC usage, and trending topics. It aims to identify research gaps, guide future research, and support policymakers in developing effective OA policies Design/methodology/approach The experiment utilized the OpenAlex database to collect global open access (OA) publications from Indian state agricultural universities over the past ten years (2014-2023). Using the Research Organization Registry ID, 97,536 publications were extracted. Data analysis was performed with OpenRefine, and ArcGIS 10.8 and Microsoft Excel were used for visualization. Findings: The global OA research output from state agricultural universities amounted to 65,889 publications across five OA categories: Green OA (7.35%), Diamond OA (6.74%), Gold OA (57.27%), Hybrid OA (9.24%), and Bronze OA (19.41%). Notably, 78.34% of articles were published in 864 low-impact domestic journals. Tamil Nadu Agricultural University produced the most publications in Gold, Diamond, Hybrid, and Bronze OA categories, while Punjab Agricultural University excelled in Green OA and received the highest funding, incurring the most article processing charges (APCs). Collaborative research focusing on agricultural policies, rice water management, soil fertility, and crop productivity had a greater impact. Originality/value The experiment is the first effort to evaluate the OA global academic research outputs of Indian state agriculture universities. The findings offer institutions, state governments, and funding agencies the opportunity to prioritise open-access publishing to promote sustainable agricultural research. Research limitations/implications The study is limited to the publications data indexed in the OpenAlex database.
Pandiyaraju V, Abishek Karthik, Sreya Mynampati
et al.
The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a hybrid deep learning framework recipe for weed detection that utilizes Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) to build robustness to multiple field conditions. A Generative Adversarial Network (GAN)-based augmentation method was imposed to balance class distributions and better generalize the model. Further, a self-supervised contrastive pre-training method helps to learn more features from limited annotated data. Experimental results yield superior results with 99.33% accuracy, precision, recall, and F1-score on multi-benchmark datasets. The proposed model architecture enables local, global, and relational feature representations and offers high interpretability and adaptability. Practically, the framework allows real-time, efficient deployment to edge devices for automated weed detecting, reducing over-reliance on herbicides and providing scalable, sustainable precision-farming options.
Efficient crop-weed segmentation is critical for site-specific weed control in precision agriculture. Conventional CNN-based methods struggle to generalize and rely on RGB imagery, limiting performance under complex field conditions. To address these challenges, we propose a lightweight transformer-CNN hybrid. It processes RGB, Near-Infrared (NIR), and Red-Edge (RE) bands using specialized encoders and dynamic modality integration. Evaluated on the WeedsGalore dataset, the model achieves a segmentation accuracy (mean IoU) of 78.88%, outperforming RGB-only models by 15.8 percentage points. With only 8.7 million parameters, the model offers high accuracy, computational efficiency, and potential for real-time deployment on Unmanned Aerial Vehicles (UAVs) and edge devices, advancing precision weed management.
Monitoring plant health increasingly relies on leaf-mounted sensors that provide real-time physiological data, yet efficiently locating and sampling these sensors in complex agricultural environments remains a major challenge. We present an integrated, adaptive, and scalable system that autonomously detects and interrogates plant sensors using a coordinated suite of low-cost optical components including a LiDAR, liquid lens, monochrome camera, filter wheel, and Fast Steering Mirror (FSM). The system first uses LiDAR to identify the distinct reflective signatures of sensors within the field, then dynamically redirects the camera s field of view via the FSM to target each sensor for hyperspectral imaging. The liquid lens continuously adjusts focus to maintain image sharpness across varying depths, enabling precise spectral measurements. We validated the system in controlled indoor experiments, demonstrating accurate detection and tracking of reflective plant sensors and successful acquisition of their spectral data. To our knowledge, no other system currently integrates these sensing and optical modalities for agricultural monitoring. This work establishes a foundation for adaptive, low-cost, and automated plant sensor interrogation, representing a significant step toward scalable, real-time plant health monitoring in precision agriculture.
This study investigates the spatial boundaries and mechanisms of the effect of scientific and technological innovation (STI) on high-quality agricultural development (HQA) to enhance agricultural practices. By employing a double-fixed spatial Durbin model and analyzing panel data from 167 prefectural-level cities in major grain-producing regions spanning from 2004 to 2021, we revealed significant spatiotemporal variations in the impact of STI on HQA in both local and adjacent cities. Our findings remained robust after rigorous testing. The study identified the spillover range of STI to be 420 km, displaying a distinctive inverted U-shaped trend around 170 km. Mechanism analysis indicates that both agricultural industry upgrades and human capital levels within 420 km amplify the influence of STI on local HQA, with only the latter demonstrating spillover effects. Within 170 km, both factors effectively regulate HQA in adjacent cities, while beyond this distance, only human capital regulatory impact continues to exhibit spillover effects. These insights offer theoretical guidance for designing effective agricultural scientific and technology promotion policies aimed at elevating the quality of HQA.
Plant-based waste biomass with lignocellulose as an important component is produced in large quantities worldwide every year. The components of lignocellulose that typically exhibit high utilization value include cellulose and hemicellulose, as well as pentoses and hexoses derived from their hydrolysis. As a pretreatment for the hydrolysis process, delignification is a pivotal step to enhance cellulose/hemicellulose accessibility and achieve high yields of fermentable sugars. Additionally, deep eutectic solvents (DESs) are the most widely used solvents for delignification during biomass fractionation due to their clean and environmentally friendly attributes. DESs dissolve lignin by inducing a large amount of β-O-4 bond cleavage and partial carbon–carbon bond cleavage, retaining cellulose in the solid residue, while most of the hemicellulose is hydrolyzed in DES pretreatment. This article provides a comprehensive review of the influence of DESs in the lignocellulose separation process. Key factors such as lignin removal rate, sugar conversion rate, and product chemical structure are critically reviewed to assess the feasibility of employing DESs for lignocellulose separation.
Semantic segmentation of rural roads presents unique challenges due to the unstructured nature of these environments, including irregular road boundaries, mixed surfaces, and diverse obstacles. In this study, we propose an enhanced PP-LiteSeg model specifically designed for rural road segmentation, incorporating a novel Strip Pooling Simple Pyramid Module (SP-SPPM) and a Bottleneck Unified Attention Fusion Module (B-UAFM). These modules improve the model’s ability to capture both global and local features, addressing the complexity of rural roads. To validate the effectiveness of our model, we constructed the Rural Roads Dataset (RRD), which includes a diverse set of rural scenes from different regions and environmental conditions. Experimental results demonstrate that our model significantly outperforms baseline models such as UNet, BiSeNetv1, and BiSeNetv2, achieving higher accuracy in terms of mean intersection over union (MIoU), Kappa coefficient, and Dice coefficient. Our approach enhances segmentation performance in complex rural road environments, providing practical applications for autonomous navigation, infrastructure maintenance, and smart agriculture.
Shuai Xiang, Pieter M. Blok, James Burridge
et al.
Object detection has wide applications in agriculture, but domain shifts of diverse environments limit the broader use of the trained models. Existing domain adaptation methods usually require retraining the model for new domains, which is impractical for agricultural applications due to constantly changing environments. In this paper, we propose DODA ($D$iffusion for $O$bject-detection $D$omain Adaptation in $A$griculture), a diffusion-based framework that can adapt the detector to a new domain in just 2 minutes. DODA incorporates external domain embeddings and an improved layout-to-image approach, allowing it to generate high-quality detection data for new domains without additional training. We demonstrate DODA's effectiveness on the Global Wheat Head Detection dataset, where fine-tuning detectors on DODA-generated data yields significant improvements across multiple domains. DODA provides a simple yet powerful solution for agricultural domain adaptation, reducing the barriers for growers to use detection in personalised environments. The code is available at https://github.com/UTokyo-FieldPhenomics-Lab/DODA.
Bacteria, inhabiting around and in plant roots, confer many beneficial traits to promote plant growth and health. The secretion of root exudates modulates the nutritional state of the rhizosphere and root area, further selecting specific bacteria taxa and shaping the bacteria communities. Many studies of the rhizosphere effects have demonstrated that selection by the plant rhizosphere consistently enriches a set of bacteria taxa, and this is conserved across different plant species. Root selection effects are considered to be stronger than the rhizosphere selection effects, yet studies are limited. Here, we focus on the root selection effects across a group of 11 stress-resistant plant species. We found that the root selection consistently reduced the alpha diversity (represented by total number of observed species, Shannon’s diversity, and phylogenetic diversity) and altered the structure and composition of bacteria communities. Furthermore, root selection tended to enrich for clusters of bacteria genera including Pantoea, Akkermansia, Blautia, Acinetobacter, Burkholderia-Paraburkholderia, Novosphingobium, Massilia, Pseudomonas, Chryseobacterium, and Stenotrophomonas. Our study offers some basic knowledge for understanding the microbial ecology of the plant root, and suggests that several bacteria genera are of interest for future studies.
This paper discusses the argumentative turn amongst farmers and the other different stakeholders in the case of land disputes, Kebumen, Central Java, Indonesia. While policy makers insisted that the land function conversion from agriculture and tourism to mining was needed to support local development as through the absorption of labors into employment sector, as well as to improve local people’s income, local farmers insisted that the conversion merely uprooting their ownership of land and let them back to periods where they were jobless and lack of source of income decades ago. This paper applied qualitative research supported with observation and interviews with parties involved in the case, to highlight the argumentative turn within land policy, which in the case of Kebumen leads to policy conflict. This paper identifies the elite-driven policy in the land dispute cases in Kebumen has led policy close to discussions with various stakeholders, which are necessary to be heard in the policy making. This finding highlights the idea that policy creates within itself politics that is in-line with the interest of the elites, and yet, resulted in the feedback loop, manifested through the strong resistance of the community.
Political institutions and public administration (General)
Sung Jin Kim, Seung-Hoon Baek, Ki Sung Kang
et al.
Abstract The worldwide persistence of infectious diseases is a significant public health issue. Consequently, studying immunomodulatory ingredients present in natural products, such as ginseng, is important for developing new treatment options. Here, we extracted three different types of polysaccharides from white (P-WG), red (P-RG), and heat-processed (P-HPG) ginseng and analyzed their chemical properties and immunostimulatory activity against RAW 264.7 murine macrophages. Carbohydrates were the main components of all three polysaccharide types, while uronic acid and protein levels were relatively low. Chemical analysis indicated that the content of carbohydrates (total sugar) increased with processing temperature, while that of uronic acid decreased. Treatment with P-WG, P-RG or P-HPG stimulated nitric oxide (NO) production and increased tumor necrosis factor alpha (TNF-α) and interleukin (IL)-6 levels in RAW 264.7 macrophages, with P-WG showing the highest activity among the three polysaccharides. The expression of inducible NO synthase, which affects NO secretion, was highest in the macrophages treated with P-WG. Analysis of intracellular signaling pathways showed that mitogen-activated protein kinases (ERK, JNK, and p38) and NF-kB p65 were strongly phosphorylated by P-WG in macrophages but were only moderately phosphorylated by P-RG and P-HPG. Collectively, these results suggest that the polysaccharides isolated from ginseng undergo different changes in response to heat processing and display different chemical compositions and immune-enhancing activities.
The Ministry of Economy has an interest and demand in exploring how to increase the set of [legally registered] small family farmers in Ukraine and to examine more in details measures that could reduce the scale of the shadow agricultural market in Ukraine. Building upon the above political economy background and demand, we will be undertaking the analysis along the two separate but not totally independents streams of analysis, i.e. sustainable small scale (family) farming development and exploring the scale and measures for reducing the shadow agricultural market in Ukraine
Naresh Kshetri, Chandra Sekhar Bhusal, Dilip Kumar
et al.
Not only in our country and Asia, but the agriculture sector is also lagging all over the world while using new technologies and innovations. Farmers are not getting the accurate price and compensation of their products because of several reasons. The intermediate persons or say middlemen are controlling the prices and product delivery on their own. Due to lack of education, technological advancement, market knowledge, post-harvesting processes, and middleman involvement, farmers are always deprived of their actual pay and efforts. The use of blockchain technology can help such farmers to automate the process with high trust. We have presented our case study and analysis for the Indian sugarcane farming with data collected from farmers. The system implementation, testing, and result analysis has been shown based on the case study. The overall purpose of our research is to emphasize and motivate the agricultural products and benefit the farmers with the use of blockchain technology.