Abstract Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount for human livelihood. Today, this role must be satisfied within a context of environmental sustainability and climate change, combined with an unprecedented and still-expanding human population size, while maintaining the viability of agricultural activities to ensure both subsistence and livelihoods. Remote sensing has the capacity to assist the adaptive evolution of agricultural practices in order to face this major challenge, by providing repetitive information on crop status throughout the season at different scales and for different actors. We start this review by making an overview of the current remote sensing techniques relevant for the agricultural context. We present the agronomical variables and plant traits that can be estimated by remote sensing, and we describe the empirical and deterministic approaches to retrieve them. A second part of this review illustrates recent research developments that permit to strengthen applicative capabilities in remote sensing according to specific requirements for different types of stakeholders. Such agricultural applications include crop breeding, agricultural land use monitoring, crop yield forecasting, as well as ecosystem services in relation to soil and water resources or biodiversity loss. Finally, we provide a synthesis of the emerging opportunities that should strengthen the role of remote sensing in providing operational, efficient and long-term services for agricultural applications.
Global agricultural feeds over 7 billion people, but is also a leading cause of environmental degradation. Understanding how alternative agricultural production systems, agricultural input efficiency, and food choice drive environmental degradation is necessary for reducing agriculture’s environmental impacts. A meta-analysis of life cycle assessments that includes 742 agricultural systems and over 90 unique foods produced primarily in high-input systems shows that, per unit of food, organic systems require more land, cause more eutrophication, use less energy, but emit similar greenhouse gas emissions (GHGs) as conventional systems; that grass-fed beef requires more land and emits similar GHG emissions as grain-feed beef; and that low-input aquaculture and non-trawling fisheries have much lower GHG emissions than trawling fisheries. In addition, our analyses show that increasing agricultural input efficiency (the amount of food produced per input of fertilizer or feed) would have environmental benefits for both crop and livestock systems. Further, for all environmental indicators and nutritional units examined, plant-based foods have the lowest environmental impacts; eggs, dairy, pork, poultry, non-trawling fisheries, and non-recirculating aquaculture have intermediate impacts; and ruminant meat has impacts ∼100 times those of plant-based foods. Our analyses show that dietary shifts towards low-impact foods and increases in agricultural input use efficiency would offer larger environmental benefits than would switches from conventional agricultural systems to alternatives such as organic agriculture or grass-fed beef.
In recent years, the adverse effect of climate change on soil properties in the agricultural sector has become a dreadful reality worldwide. Climate change-induced abiotic stresses such as salinity, drought and temperature fluctuations are devastating crops’ physiological responses, productivity and overall yield, which is ultimately posing a serious threat to global food security and agroecosystems. The applications of chemical fertilizers and pesticides contribute towards further deterioration and rapid changes in climate. Therefore, more careful, eco-friendly and sustainable strategies are required to mitigate the impact of climate-induced damage on the agricultural sector. This paper reviews the recently reported damaging impacts of abiotic stresses on various crops, along with two emerging mitigation strategies, biochar and biostimulants, in light of recent studies focusing on combating the worsening impact of the deteriorated environment and climate change on crops’ physiological responses, yields, soil properties and environment. Here, we highlighted the impact of climate change on agriculture and soil properties along with recently emerging mitigation strategies applying biochar and biostimulants, with an aim to protecting the soil, agriculture and environment.
Sanidhya Ghosal, Anurag Sharma, Sushil Ghildiyal
et al.
Distributing government relief efforts after a flood is challenging. In India, the crops are widely affected by floods; therefore, making rapid and accurate crop damage assessment is crucial for effective post-disaster agricultural management. Traditional manual surveys are slow and biased, while current satellite-based methods face challenges like cloud cover and low spatial resolution. Therefore, to bridge this gap, this paper introduced FLNet, a novel deep learning based architecture that used super-resolution to enhance the 10 m spatial resolution of Sentinel-2 satellite images into 3 m resolution before classifying damage. We tested our model on the Bihar Flood Impacted Croplands Dataset (BFCD-22), and the results showed an improved critical "Full Damage" F1-score from 0.83 to 0.89, nearly matching the 0.89 score of commercial high-resolution imagery. This work presented a cost-effective and scalable solution, paving the way for a nationwide shift from manual to automated, high-fidelity damage assessment.
Patrick Michael, Robert J. Reid, Robert W. Fitzpatrick
The long-term roles of live plant roots in mitigating acid sulfate soil stresses remain poorly understood. Three studies, each lasting twelve months, were conducted using Melaleuca armillaris and Phragmites australis. In the first study, alkaline sandy loam soil was mixed into the sulfuric soil to increase the pH to 6.7, and Melaleuca seedlings were planted. In the second and third studies, M. armillaris and P. australis were planted in sulfuric and sulfidic soils and maintained at 75% water-holding capacity and flooded soil conditions. All the studies were set using 300 mm stormwater tubes with sealed bottom ends. The treatments were replicated four times, set up under a glasshouse in a completely randomized design, and harvested after 12 months. The pH and root biomass were measured from the surface, middle, and deep profiles. Results showed that the neutralization obtained by mixing alkaline sandy loam soil with sulfuric soil was stable but deteriorated due to plant root penetration. In the sulfuric soil material (pH <4), M. armillaris produced more roots at the surface than in the deep soil under circumneutral pH and aerobic soil conditions. In sulfidic soil material (pH >4), more roots were produced in the deeper soils. In the sulfuric and sulfidic soil materials, P. australis produced more roots at the surface than at the deep under pH >4 and aerobic conditions. Under anaerobic conditions with a pH >4, root distribution was even. Our findings suggest that common terrestrial and aquatic plants maintain a characteristic distribution of roots to mitigate the stresses of acid sulfate soils.
Background and Aim: Chitosan-based DNA nanoparticles have emerged as a promising next-generation platform for veterinary vaccines, addressing several limitations of conventional attenuated, inactivated, and recombinant formulations. Chitosan is a biodegradable, biocompatible, and low toxicity polymer with mucoadhesive properties that enhance cellular uptake and protect nucleic acids from enzymatic degradation. These characteristics make it an attractive candidate for delivering plasmid DNA encoding viral antigens across diverse animal species. Recent advances demonstrate that chitosan–DNA nanoparticles can induce robust humoral and cellular immune responses, stimulate mucosal immunity, and achieve high levels of protection in terrestrial livestock, poultry, fish, and crustaceans. A wide range of viral pathogens has been targeted using this approach, including Foot-and-Mouth disease virus, Newcastle disease virus, infectious bronchitis virus, spring viremia of carp virus, white spot syndrome virus, and infectious pancreatic necrosis virus. Depending on the species and formulation strategy, nanoparticles have been successfully administered intranasally, intramuscularly, intraperitoneally, or orally, highlighting their versatility for mass vaccination in both terrestrial and aquatic systems. Reported protection rates range from 60% to 100% in mammalian and avian models, while oral nanoparticle vaccines in shrimp and fish have demonstrated sustained immune activation and survival benefits. The ability to incorporate genetic adjuvants, such as cytosine-phosphate-guanine motifs, cytokines, or complement fragments, further enhances the immunogenicity of these platforms. Despite these promising results, several challenges remain. Most studies use small laboratory animals or controlled experimental settings, and data from large-scale field trials in cattle, pigs, and equines remain scarce. The stability of nanoparticle formulations during long-term storage, the scalability of manufacturing processes, and the standardization of dosing regimens require further investigation. Overall, chitosan–DNA nanoparticles represent a safe, flexible, and rapidly adaptable vaccine carrier system with significant potential to transform veterinary immunization. Their capacity to elicit mucosal and systemic immunity, enable needle-free delivery, and support DIVA-compatible vaccine design positions them as a valuable tool for controlling emerging and re-emerging viral diseases in the context of One Health.
Agricultural visual question answering is essential for providing farmers and researchers with accurate and timely knowledge. However, many existing approaches are predominantly developed for evidence-constrained settings such as text-only queries or single-image cases. This design prevents them from coping with real-world agricultural scenarios that often require multi-image inputs with complementary views across spatial scales, and growth stages. Moreover, limited access to up-to-date external agricultural context makes these systems struggle to adapt when evidence is incomplete. In addition, rigid pipelines often lack systematic quality control. To address this gap, we propose a self-reflective and self-improving multi-agent framework that integrates four roles, the Retriever, the Reflector, the Answerer, and the Improver. They collaborate to enable context enrichment, reflective reasoning, answer drafting, and iterative improvement. A Retriever formulates queries and gathers external information, while a Reflector assesses adequacy and triggers sequential reformulation and renewed retrieval. Two Answerers draft candidate responses in parallel to reduce bias. The Improver refines them through iterative checks while ensuring that information from multiple images is effectively aligned and utilized. Experiments on the AgMMU benchmark show that our framework achieves competitive performance on multi-image agricultural QA.
Sourish Das, Sudeep Shukla, Abbinav Sankar Kailasam
et al.
Agricultural price volatility challenges sustainable finance, planning, and policy, driven by market dynamics and meteorological factors such as temperature and precipitation. In India, the Minimum Support Price (MSP) system acts as implicit crop insurance, shielding farmers from price drops without premium payments. We analyze the impact of climate on price volatility for soybean (Madhya Pradesh), rice (Assam), and cotton (Gujarat). Using ERA5-Land reanalysis data from the Copernicus Climate Change Service, we analyze historical climate patterns and evaluate two scenarios: SSP2.4.5 (moderate case) and SSP5.8.5 (severe case). Our findings show that weather conditions strongly influence price fluctuations and that integrating meteorological data into volatility models enhances risk-hedging. Using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, we estimate conditional price volatility and identify cross-correlations between weather and price volatility movements. Recognizing MSP's equivalence to a European put option, we apply the Black-Scholes model to estimate its implicit premium, quantifying its fiscal cost. We propose this novel market-based risk-hedging mechanism wherein the government purchases insurance equivalent to MSP, leveraging Black-Scholes for accurate premium estimation. Our results underscore the importance of meteorological data in agricultural risk modeling, supporting targeted insurance and strengthening resilience in agricultural finance. This climate-informed financial framework enhances risk-sharing, stabilizes prices, and informs sustainable agricultural policy under growing climate uncertainty.
Leire Benito-Del-Valle, Artzai Picón, Daniel Mugica
et al.
Herbicide field trials require accurate identification of plant species and assessment of herbicide-induced damage across diverse environments. While general-purpose vision foundation models have shown promising results in complex visual domains, their performance can be limited in agriculture, where fine-grained distinctions between species and damage types are critical. In this work, we adapt a general-purpose vision foundation model to herbicide trial characterization. Trained using a self-supervised learning approach on a large, curated agricultural dataset, the model learns rich and transferable representations optimized for herbicide trials images. Our domain-specific model significantly outperforms the best general-purpose foundation model in both species identification (F1 score improvement from 0.91 to 0.94) and damage classification (from 0.26 to 0.33). Under unseen conditions (new locations and other time), it achieves even greater gains (species identification from 0.56 to 0.66; damage classification from 0.17 to 0.27). In domain-shift scenarios, such as drone imagery, it maintains strong performance (species classification from 0.49 to 0.60). Additionally, we show that domain-specific pretraining enhances segmentation accuracy, particularly in low-annotation regimes. An annotation-efficiency analysis reveals that, under unseen conditions, the domain-specific model achieves 5.4% higher F1 score than the general-purpose model, while using 80% fewer labeled samples. These results demonstrate the generalization capabilities of domain-specific foundation models and their potential to significantly reduce manual annotation efforts, offering a scalable and automated solution for herbicide trial analysis.
In 2025, intensified Immigration and Customs Enforcement (ICE) raids in Oxnard, California, disrupted the state's \$49 billion agricultural industry, a critical supplier of 75% of U.S. fruits and nuts and one-third of its vegetables. This paper quantifies the economic consequences of these raids on labor markets, crop production, and food prices using econometric modeling. We estimate a 20-40% reduction in the agricultural workforce, leading to \$3-7 billion in crop losses and a 5-12% increase in produce prices. The analysis draws on USDA Economic Research Service data and recent ICE detention figures, which show arrests in Southern California rising from 699 in May to nearly 2,000 in June 2025. The raids disproportionately affect labor-intensive crops like strawberries, exacerbating supply chain disruptions. Policy recommendations include expanding the H-2A visa program and legalizing undocumented workers to stabilize the sector. This study contributes to agricultural economics by providing a data-driven assessment of immigration enforcement's economic toll.
Accurate estimation of fruit hardness is essential for automated classification and handling systems, particularly in determining fruit variety, assessing ripeness, and ensuring proper harvesting force. This study presents an innovative framework for quantitative hardness assessment utilizing vision-based tactile sensing, tailored explicitly for robotic applications in agriculture. The proposed methodology derives normal force estimation from a vision-based tactile sensor, and, based on the dynamics of this normal force, calculates the hardness. This approach offers a rapid, non-destructive evaluation through single-contact interaction. The integration of this framework into robotic systems enhances real-time adaptability of grasping forces, thereby reducing the likelihood of fruit damage. Moreover, the general applicability of this approach, through a universal criterion based on average normal force dynamics, ensures its effectiveness across a wide variety of fruit types and sizes. Extensive experimental validation conducted across different fruit types and ripeness-tracking studies demonstrates the efficacy and robustness of the framework, marking a significant advancement in the domain of automated fruit handling.
Marios Glytsos, Panagiotis P. Filntisis, George Retsinas
et al.
Accurate 6D object pose estimation is essential for robotic grasping and manipulation, particularly in agriculture, where fruits and vegetables exhibit high intra-class variability in shape, size, and texture. The vast majority of existing methods rely on instance-specific CAD models or require depth sensors to resolve geometric ambiguities, making them impractical for real-world agricultural applications. In this work, we introduce PLANTPose, a novel framework for category-level 6D pose estimation that operates purely on RGB input. PLANTPose predicts both the 6D pose and deformation parameters relative to a base mesh, allowing a single category-level CAD model to adapt to unseen instances. This enables accurate pose estimation across varying shapes without relying on instance-specific data. To enhance realism and improve generalization, we also leverage Stable Diffusion to refine synthetic training images with realistic texturing, mimicking variations due to ripeness and environmental factors and bridging the domain gap between synthetic data and the real world. Our evaluations on a challenging benchmark that includes bananas of various shapes, sizes, and ripeness status demonstrate the effectiveness of our framework in handling large intraclass variations while maintaining accurate 6D pose predictions, significantly outperforming the state-of-the-art RGB-based approach MegaPose.
This study analyzes historical data from five agricultural commodities in the Chinese futures market to explore the correlation, cointegration, and Granger causality between Peanut futures and related futures. Multivariate linear regression models are constructed for prices and logarithmic returns, while dynamic relationships are examined using VAR and DCC-EGARCH models. The results reveal a significant dynamic linkage between Peanut and Soybean Oil futures through DCC-EGARCH, whereas the VAR model suggests limited influence from other futures. Additionally, the application of MLP, CNN, and LSTM neural networks for price prediction highlights the critical role of time step configurations in forecasting accuracy. These findings provide valuable insights into the interconnectedness of agricultural futures markets and the efficacy of advanced modeling techniques in financial analysis.
Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motion-aware tracking and segmentation, and advanced feature extraction using vision transformers for robust behavior recognition. The pipeline addresses challenges including animal occlusions and group housing scenarios as demonstrated in indoor pig monitoring. We validated our system on the Edinburgh Pig Behavior Video Dataset for multiple behavioral tasks. Our temporal model achieved 94.2% overall accuracy, representing a 21.2 percentage point improvement over existing methods. The pipeline demonstrated robust tracking capabilities with 93.3% identity preservation score and 89.3% object detection precision. The modular design suggests potential for adaptation to other contexts, though further validation across species would be required. The open-source implementation provides a scalable solution for behavior monitoring, contributing to precision pig farming and welfare assessment through automated, objective, and continuous analysis.
Integrating deep learning applications into agricultural IoT systems faces a serious challenge of balancing the high accuracy of Vision Transformers (ViTs) with the efficiency demands of resource-constrained edge devices. Large transformer models like the Swin Transformers excel in plant disease classification by capturing global-local dependencies. However, their computational complexity (34.1 GFLOPs) limits applications and renders them impractical for real-time on-device inference. Lightweight models such as MobileNetV3 and TinyML would be suitable for on-device inference but lack the required spatial reasoning for fine-grained disease detection. To bridge this gap, we propose a hybrid knowledge distillation framework that synergistically transfers logit and attention knowledge from a Swin Transformer teacher to a MobileNetV3 student model. Our method includes the introduction of adaptive attention alignment to resolve cross-architecture mismatch (resolution, channels) and a dual-loss function optimizing both class probabilities and spatial focus. On the lantVillage-Tomato dataset (18,160 images), the distilled MobileNetV3 attains 92.4% accuracy relative to 95.9% for Swin-L but at an 95% reduction on PC and < 82% in inference latency on IoT devices. (23ms on PC CPU and 86ms/image on smartphone CPUs). Key innovations include IoT-centric validation metrics (13 MB memory, 0.22 GFLOPs) and dynamic resolution-matching attention maps. Comparative experiments show significant improvements over standalone CNNs and prior distillation methods, with a 3.5% accuracy gain over MobileNetV3 baselines. Significantly, this work advances real-time, energy-efficient crop monitoring in precision agriculture and demonstrates how we can attain ViT-level diagnostic precision on edge devices. Code and models will be made available for replication after acceptance.