Hamid Raza, Mukkram Ali Tahir, Noor Us Sabah
et al.
Forage crops that contain elevated levels of metals are a serious hygiene and safety concern because they act as the main route through which these elements enter the food chain. The aim of this study was to evaluate the potential health risks associated with cadmium contamination in the food chain by applying different assessment indices, focusing on commonly consumed forage crops grown at contaminated sites near District Khushab, Pakistan. Across the winter and summer seasons of 2023–24, a total of water (n = 100), forage (n = 240), soil (n = 240), ruminant blood (n = 100), and milk (n = 100) samples were obtained from two separate locations: Jauharabad (S_1) and Noorpur Thal (S_2) and tested to measure cadmium content with an atomic absorption spectrophotometer. Cadmium concentrations averaged 0.09–0.58 mg kg⁻¹ in soil, 0.04–1.02 mg kg⁻¹ in forage, 0.21–1.25 mg L⁻¹ in water, 0.17–2.98 mg L⁻¹ in cow blood, 0.68–4.68 mg L⁻¹ in buffalo blood, 0.24–1.25 mg L⁻¹ in cow milk, and 0.15–0.96 mg L⁻¹ in buffalo milk. A very strong and statistically significant positive correlation was found between water at site S_1 and soil at site S_2 during the winter season. A highly significant strong positive correlation was observed between sites S_1 and S_2 for the crop T. alexandrium during the winter season. In contrast, P. glaucum showed a strong and significant negative correlation in the summer, while no significant effect of cadmium was detected in Z. mays during the same season. In animals, cadmium levels were highest in blood and lowest in milk. All calculated risk indices including BCF, EF, Eri, HRI, and THQ exceeded 1, highlighting potential health hazards for consumers at both sites across both seasons. Regular monitoring of cadmium and other heavy metals in soil, water, and fodder, along with strict enforcement of regulations on industrial waste disposal and wastewater irrigation, is essential to minimize environmental and health risks. Farmers should be guided to use clean water, adopt low metal accumulating forages, and avoid grazing livestock on contaminated pastures. Promoting soil remediation practices and collaboration among agencies, researchers, and farmers will help reduce metal transfer through the food chain and protect both animal and human health.
Yuri Eduardo Souza Andrade, Jean Marcel Sousa Lira, Rodolfo Soares de Almeida
et al.
O plantio de essências florestais exóticas é uma alternativa econômica viável para pequenas e médias propriedades rurais. No entanto, a atividade ainda é pouco difundida devido às incertezas quanto à viabilidade econômica. Este estudo analisou a viabilidade econômica do plantio de mogno africano na região do Campo das Vertentes, Minas Gerais (MG). Utilizou-se um plantio misto de Khaya grandifoliola e Khaya senegalensis, implantado em outubro de 2020, em uma área de 2,1 hectares, com espaçamento de 3x2 m e três ciclos de desbaste aos 5, 10 e 15 anos, além de corte raso no vigésimo ano. Os indicadores financeiros analisados foram: custo por hectare, valor presente líquido (VPL), taxa interna de retorno (TIR), custo médio de produção (CMP), valor anual equivalente (VAE), e a relação benefício/custo (B/C), acompanhados por uma análise de sensibilidade. Os resultados indicaram que o plantio de mogno africano é economicamente viável. A análise de sensibilidade mostrou que as variáveis "taxa de juros" e "preço de comercialização da madeira" impactam significativamente a viabilidade, enquanto o custo de preparo da área teve uma influência mínima. Portanto, o plantio de mogno africano se apresenta como uma alternativa promissora para investimentos rurais na região.
Md. Asif Hossain, Nabil Subhan, Mantasha Rahman Mahi
et al.
Access to reliable agricultural advisory remains limited in many developing regions due to a persistent language barrier: authoritative agricultural manuals are predominantly written in English, while farmers primarily communicate in low-resource local languages such as Bengali. Although recent advances in Large Language Models (LLMs) enable natural language interaction, direct generation in low-resource languages often exhibits poor fluency and factual inconsistency, while cloud-based solutions remain cost-prohibitive. This paper presents a cost-efficient, cross-lingual Retrieval-Augmented Generation (RAG) framework for Bengali agricultural advisory that emphasizes factual grounding and practical deployability. The proposed system adopts a translation-centric architecture in which Bengali user queries are translated into English, enriched through domain-specific keyword injection to align colloquial farmer terminology with scientific nomenclature, and answered via dense vector retrieval over a curated corpus of English agricultural manuals (FAO, IRRI). The generated English response is subsequently translated back into Bengali to ensure accessibility. The system is implemented entirely using open-source models and operates on consumer-grade hardware without reliance on paid APIs. Experimental evaluation demonstrates reliable source-grounded responses, robust rejection of out-of-domain queries, and an average end-to-end latency below 20 seconds. The results indicate that cross-lingual retrieval combined with controlled translation offers a practical and scalable solution for agricultural knowledge access in low-resource language settings
Agricultural environments present high proportions of spatially dense navigation bottlenecks for long-term navigation and operational planning of agricultural mobile robots. The existing agent-centric multi-robot path planning (MRPP) approaches resolve conflicts from the perspective of agents, rather than from the resources under contention. Further, the density of such contentions limits the capabilities of spatial interleaving, a concept that many planners rely on to achieve high throughput. In this work, two variants of the priority-based Fragment Planner (FP) are presented as resource-centric MRPP algorithms that leverage route fragmentation to enable partial route progression and limit the impact of binary-based waiting. These approaches are evaluated in lifelong simulation over a 3.6km topological map representing a commercial polytunnel environment. Their performances are contrasted against 5 baseline algorithms with varying robotic fleet sizes. The Fragment Planners achieved significant gains in throughput compared with Prioritised Planning (PP) and Priority-Based Search (PBS) algorithms. They further demonstrated a task throughput of 95% of the optimal task throughput over the same time period. This work shows that, for long-term deployment of agricultural robots in corridor-dominant agricultural environments, resource-centric MRPP approaches are a necessity for high-efficacy operational planning.
ABSTRACT Step‐point sampling, conceived by plant ecologist Dr Leonard Cockayne in ca. 1925 as a method for measuring the ground cover of species in New Zealand’s modified tussock grasslands utilises a point marked on the observer’s boot toecap. It is resource‐efficient compared to other vegetation sampling methods and here we report on a unique evaluation of its accuracy and precision. The estimated cover of Taraxacum officinale in a grassy field (11%) was not significantly different from that obtained using line‐point sampling (13%), but higher than that from line‐intercept sampling (5.7%). It was unaffected across an order‐of‐magnitude range of sampling intensity (1600 to 160 observations/ha) and was acceptably precise with ≥ 400 observations/ha, although increased linearly with observation point size. These empirical results were supported by computer simulations enabling both accuracy and precision to be evaluated along with plant architecture. The simulations indicated that step‐point sampling with ca. 400 observations per ha and an observation point diameter ≤ 1.1 cm (1.0 cm 2 ), can provide ground cover estimates for a wide range of broadleaved species in pastures that are sufficiently accurate and precise for common pasture management applications. The method is deployed in the phone application, ‘Grassland Cover Estimator’ available at https://www.agresearch.co.nz/search?q = Grassland + cover + estimator .
Intra-annual variation in rainfall creates significant challenges for agricultural output, particularly in semi-arid monsoon regions. In this study, we present a volatility-in-mean time series modeling framework to examine how rainfall risk influences rice yield forecasts in Maharashtra, India. We construct four distinct measures to capture intra-seasonal rainfall variability and incorporate them into forecasting models using six decades of monthly rainfall data (1962–2021) for the state. These measures are embedded within ARIMAX and GARCH-ARIMAX specifications to jointly assess the effects of rainfall volatility on the mean and variability of yields. Our results show that volatility-based models – especially exponential GARCH (eGARCH) and gjrGARCH variants using higher-order, first-difference-based measures (RV3 and RV4) – consistently deliver superior forecast accuracy and greater robustness compared to simpler ARIMAX or iGARCH configurations. Models relying on contemporaneous rainfall volatility outperform those using lagged measures, underscoring the immediate impact of seasonal climate anomalies. Sensitivity analysis with ±10% perturbations to rainfall risk measures further confirms that GARCH-type models not only improve predictive skill but also enhance stability under plausible input variations, making their inclusion effectively indispensable for climate-sensitive crop forecasting. These findings reinforce the need to embed dynamic meteorological risk indicators in agricultural forecasting frameworks to strengthen early warning systems, support adaptive policy design, and promote resilient, sustainable cropping systems in monsoon-dependent regions.
Soil moisture and salinity (SMS) are two critical factors in crop growth, and monitoring their dynamic has important scientific value and social benefits for preventing land degradation and improving land productivity. However, the current methodology treats soil moisture and salinity as two independent variables to be estimated separately, completely ignoring their joint influence on the microwave signal. In this paper, the Jingdian irrigated region, which is located in northwestern China, is selected as an example, the contents of soil volumetric water and soil salt are measured separately for different seasons in the research area, and they are also retrieved simultaneously by combining Sentinel-1 data and a revised dielectric model of salty soil. The results demonstrate that the Sentinel-1 data can achieve satisfactory results in the simultaneous retrieval of SMS, with R2 values higher than 0.53. The RMSE values in the upward track are less than 0.042 m3/m3 and 3.132 mS/cm, respectively, which are smaller than in the downward track, with the RMSE values less than 0.051 m3/m3 and 3.84 mS/cm, respectively. The average value of soil moisture content in winter is 0.17 m3/m3, which is higher than in spring, with a value of 0.21 m3/m3. The soil salt content increases gradually over the study period, with average values of 1.88 mS/cm in spring and 3.58 mS/cm in winter, respectively. In addition, vegetation, surface roughness, precipitation, and agricultural activities are the main factors affecting the simultaneous retrieval of SMS.
IntroductionRural areas have actively invested in distinctive rural development by leveraging distinctive rural resources to enhance the vitality and sustainability of farmers’ income growth. However, the pathway to achieve this objective remains uncertain.MethodsThis study focuses on the distinctive rural development in the northern region of Jiangsu Province via a dynamic configurational perspective across three dimensions—agricultural development, tourism cultivation, and cultural infrastructure—within the distinctive industrial system framework on farmers’ income enhancement.Results and discussionThe findings reveal that a single factor is not a necessary condition for promoting farmers’ sustainable income growth; however, low levels of annual income from specialized industries constitute a necessary condition for the maintenance of low-income levels among farmers. There are four combination pathways that drive farmers’ sustainable income growth: Composite Value-Added Driven Type, Agriculture-Oriented E-Commerce Driven Type, Agriculture-Oriented Chain-Based Collaborative Type, and Innovation-Driven Agriculture-Tourism Co-Driven Type. Distinctive rural development not only mitigates the factor constraints associated with traditional income growth pathways but also expands new avenues for increasing income, thereby enhancing farmers’ capacity for income sustained growth. This study advances configurational theory by applying dynamic fsQCA to rural development, highlighting nonlinear factor interactions over isolated variables through a holistic lens on resource endowments and innovations in rural economic systems.
Nutrition. Foods and food supply, Food processing and manufacture
Drought stress significantly inhibits the growth of Astragalus mongholicus, leading to reduced biomass, decreased photosynthetic efficiency, and exacerbated oxidative damage. In our study, the accumulation of saponins and flavonoids in Astragalus roots markedly increased under moderate drought stress. These secondary metabolites further reshaped the rhizosphere microbial community structure, significantly increasing its diversity and interaction network complexity. Notably, drought stress enriched beneficial bacterial genera such as Rhizobium and Pseudomonas in the rhizosphere soil. Combined with the isolation of culturable microorganisms and the co-occurrence network of the rhizosphere bacterial community, we constructed a 13-strain synthetic community (SynCom) and simplified it to 7 strains. Compared with the noninoculated control, under moderate drought stress, inoculation with the simplified SynCom significantly increased plant growth, increasing the aboveground fresh weight by 50.10 %, dry weight by 55.29 %, and underground fresh weight by 76.40 %. Similarly, plants treated with the synthetic community presented significant increases in aboveground fresh weight and dry weight compared with those of the noninoculated control, with increases of 46.98 % and 61.54 %, respectively. Moreover, inoculation with the simplified community significantly reduced the content of malondialdehyde (MDA) and improved the catalase (CAT) and peroxidase (POD) activities and leaf photosynthetic parameters (Fv/Fm and Y(II)) of Astragalus. Our findings provide new insight into improving the yield and quality of Astragalus and highlight the potential of synthetic rhizosphere microbial communities for assisting plants in coping with abiotic stress.
Elias Maritan, Evangelos Anastasiou, Vasilis Psiroukis
et al.
Spraying pesticides with uncrewed aerial vehicles (UAVs) in European viticulture is currently only allowed when there are no viable alternatives or if it provides environmental and human health benefits. Using Greece as a case study, this analysis investigated the agroecological performance of UAV spraying in comparison with land-based pesticide application. A multi-objective linear programming model assessed farmer preferences for spraying pesticides with ground equipment or a UAV. Farmers concerned with non-economic goals preferred UAV targeted pesticide application, while production-orientated farmers favoured ground spraying. Depending on disease pressure, UAV spraying generated annual savings of €278–377 ha-1 on a flat vineyard compared to a trailed vine sprayer and €367–538 ha-1 on a steep-slope vineyard compared to a backpack sprayer. However, the estimated costs of custom-hiring UAVs in Greece made UAV spraying less profitable except in conditions of simultaneous extreme labour scarcity and high disease pressure on the steep-slope vineyard. UAV aerial broadcast had an environmental impact comparable to ground spraying, but UAV spot-spraying mitigated ecotoxicological risks of pesticide use by 46–50 %. Both UAV spraying methods substantially reduced human exposure to pesticides. In current regulation, UAV aerial broadcast would only be allowed in steep-slope viticulture if seasonal labour was unavailable. UAV spot-spraying could be permitted on both vineyards, but it would be economically feasible if hiring fees were €43–49 ha-1. The study concludes with recommendations to promote UAV spraying adoption among European farmers thereby contributing to the EU objectives to halve pesticide use and risk while potentially resolving labour availability challenges on abandonment-prone vineyards.
Real-time automation in agriculture shows great potential in perennial trees and vegetables allowing site-specific management by means of machine vision, and real-time processing. A lack of clarity still remains on the actual effectiveness, scalability, and limitations of such technologies in field conditions. This study critically examines the recent advancements in real-time applications in horticultural crops that are controlled using sensing systems for automating several cultivation tasks including (i) crop protection (ii) fertilization (iii) weeding (iv) harvesting and (v) crop load management. These tasks are individually evaluated, while identifying technological gaps and future research directions. Specifically, this study assesses real-time decision-making challenges and evaluates their impact in terms of processing time, resource efficiency, cost-effectiveness, and decision accuracy. The results revealed that real-time applications can increase precision and operational efficiency, while the need for improved communication and interoperability between the sensing systems and implements was highlighted. However, the effectiveness is often influenced by the sensor accuracy, the plant structure, and adaptability to crop systems. Further development of real-time applications in perennial trees and vegetables should be explored by producing artificial intelligence decision models based on plant information and multi-modal sensor systems.
Accurate monitoring of rice phenological transitions plays a pivotal role in enhancing breeding efficiency and optimizing agronomic practices. Current spectral-based approaches frequently encounter limitations in detecting subtle growth stage boundaries within large-scale breeding programs, particularly due to visually imperceptible canopy variations during critical transitional phases. To address this issue, this study introduces a deep learning framework named GrowAI that synergistically combines dynamic plant architecture parameters with hyperspectral canopy signatures for robust phenological identification. Through a two-year breeding experiment, we established a time-series multispectral image dataset covering complete growth cycles. Our methodology innovatively integrates three-dimensional plant height dynamics with canopy optical properties through multimodal fusion architecture. Experimental results demonstrated GrowAI's superior performance, achieving classification accuracies of 0.937 (OA) and 0.927 (F1-score), representing average improvements of 6.9 % and 7.0 % respectively over conventional full-spectrum deep learning approaches. Notably, the framework exhibited exceptional temporal generalizability with cross-year validation accuracy reaching 0.977. Moreover, by accurately tracking the phenological stages of different rice genotypes in the breeding trials, the GrowAI framework can help breeders identify climate-resilient cultivars that have the most suitable phenological characteristics.
Stephane Ngnepiepaye Wembe, Vincent Rousseau, Johann Laconte
et al.
Modern agriculture faces escalating challenges: increasing demand for food, labor shortages, and the urgent need to reduce environmental impact. Agricultural robotics has emerged as a promising response to these pressures, enabling the automation of precise and suitable field operations. In particular, robots equipped with implements for tasks such as weeding or sowing must interact delicately and accurately with the crops and soil. Unlike robots in other domains, these agricultural platforms typically use rigidly mounted implements, where the implement's position is more critical than the robot's center in determining task success. Yet, most control strategies in the literature focus on the vehicle body, often neglecting the acctual working point of the system. This is particularly important when considering new agriculture practices where crops row are not necessary straights. This paper presents a predictive control strategy targeting the implement's reference point. The method improves tracking performance by anticipating the motion of the implement, which, due to its offset from the vehicle's center of rotation, is prone to overshooting during turns if not properly accounted for.
Geospatial foundation models (GFMs) have emerged as a promising approach to overcoming the limitations in existing featurization methods. More recently, Google DeepMind has introduced AlphaEarth Foundation (AEF), a GFM pre-trained using multi-source EOs across continuous time. An annual and global embedding dataset is produced using AEF that is ready for analysis and modeling. The internal experiments show that AEF embeddings have outperformed operational models in 15 EO tasks without re-training. However, those experiments are mostly about land cover and land use classification. Applying AEF and other GFMs to agricultural monitoring require an in-depth evaluation in critical agricultural downstream tasks. There is also a lack of comprehensive comparison between the AEF-based models and traditional remote sensing (RS)-based models under different scenarios, which could offer valuable guidance for researchers and practitioners. This study addresses some of these gaps by evaluating AEF embeddings in three agricultural downstream tasks in the U.S., including crop yield prediction, tillage mapping, and cover crop mapping. Datasets are compiled from both public and private sources to comprehensively evaluate AEF embeddings across tasks at different scales and locations, and RS-based models are trained as comparison models. AEF-based models generally exhibit strong performance on all tasks and are competitive with purpose-built RS-based models in yield prediction and county-level tillage mapping when trained on local data. However, we also find several limitations in current AEF embeddings, such as limited spatial transferability compared to RS-based models, low interpretability, and limited time sensitivity. These limitations recommend caution when applying AEF embeddings in agriculture, where time sensitivity, generalizability, and interpretability is important.
Self Supervised Learning(SSL) has emerged as a prominent paradigm for label-efficient learning, and has been widely utilized by remote sensing foundation models(RSFMs). Recent RSFMs including SatMAE, DoFA, primarily rely on masked autoencoding(MAE), contrastive learning or some combination of them. However, these pretext tasks often overlook the unique temporal characteristics of agricultural landscape, namely nature's cycle. Motivated by this gap, we propose three novel agriculture-specific pretext tasks, namely Time-Difference Prediction(TD), Temporal Frequency Prediction(FP), and Future-Frame Prediction(FF). Comprehensive evaluation on SICKLE dataset shows FF achieves 69.6% IoU on crop mapping and FP reduces yield prediction error to 30.7% MAPE, outperforming all baselines, and TD remains competitive on most tasks. Further, we also scale FF to the national scale of India, achieving 54.2% IoU outperforming all baselines on field boundary delineation on FTW India dataset.
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.
Advances in AI and Robotics have accelerated significant initiatives in agriculture, particularly in the areas of robot navigation and 3D digital twin creation. A significant bottleneck impeding this progress is the critical lack of "in-the-wild" datasets that capture the full complexities of real farmland, including non-rigid motion from wind, drastic illumination variance, and morphological changes resulting from growth. This data gap fundamentally limits research on robust AI models for autonomous field navigation and scene-level dynamic 3D reconstruction. In this paper, we present AgriChrono, a modular robotic data collection platform and multi-modal dataset designed to capture these dynamic farmland conditions. Our platform integrates multiple sensors, enabling remote, time-synchronized acquisition of RGB, Depth, LiDAR, IMU, and Pose data for efficient and repeatable long-term data collection in real-world agricultural environments. We successfully collected 18TB of data over one month, documenting the entire growth cycle of Canola under diverse illumination conditions. We benchmark state-of-the-art 3D reconstruction methods on AgriChrono, revealing the profound challenge of reconstructing high-fidelity, dynamic non-rigid scenes in such farmland settings. This benchmark validates AgriChrono as a critical asset for advancing model generalization, and its public release is expected to significantly accelerate research and development in precision agriculture. The code and dataset are publicly available at: https://github.com/StructuresComp/agri-chrono
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.
Chakrapani Adhikari, Dhirendra Man Thapa, Keshar Bahadur Shahi
The research was carried on “Incidence of the seed borne infection and management of Alternaria species in wheat cultivars” at Laboratory of Bright Mid-West Agriculture and Forestry Campus, Birendranagar, Surkhet, Nepal from December 2019 to March 2020. The experiment was laid out in Completely Randomize Design (CRD). In the present investigation, seed samples of seven different varieties of wheat (Triticum aestivum L.) seeds viz. Gautam, Virkuti, Aaaditya, Wk1204, NL1172, NL1177 and NL297 were collected from National Wheat Research Program (NWRP) Bhairahawa, Rupandehi, Nepal and Nepal Agriculture Research Council (NARC), Botany division, Khumaltar, Lalitpur, Nepal. Seed samples were categorized by different varieties and treatment in different day’s interval. Seed treatment with, Dhanuka M-45, Acmes tin, Trichoderma viridae and control (without treatment) was done to see their effect on seed borne infection of Alternaria in wheat. The recorded parameters among the sampled plants were germination percentage, disease incidence percentage and incidence of Alternaria on the seeds. The variety ‘Aditya’ germinated maximum (97.00%) and variety NL-297 germinated minimum (0.25%) without treatments. However, Variety ‘Virkuti’ germinated maximum (95.5%) after treatment. Whereas, Variety NL-297 showed no seed-borne infection (0.00%) and variety ‘NL-1177’ showed the maximum disease severity (12.75% at 5DAI, 24.75% at 7DAI and 32.25% at 9DAI respectively) without treatment. Similarly, in treated seeds variety NL-1177 showed the maximum disease incidence percentage (6.75%) at 5DAI, variety Gautam (9.37%) at 7DAI and variety NL-1177 (9.87%) at 9DAI. whereas variety Gautam showed the minimum disease incidence (3.50%) after 9DAI, Variety ‘Virkuti’ (6.62%) at 7DAI and Variety ‘Gautam’ (8.0%) at 9DAI respectively. With the treatment variety Gautam could reduce the disease incidence by 60% at 5DAT, 50.02% at 7DAT and 68.93% at 9DAT.Similarly, variety Virkuti reduce in disease incidence by 13.53% at 5DAT, 15.12% at 7DAT and 10.41% at 9DAT. The Variety NL-1177 reduced disease incidence by 47.05% at 5DAT, 70.22% at 7DAT and 69.39% at 9DAT respectively. The results also showed that the control measure of Trichoderma viridae found significant performance (99.84%) in controlling seed borne pathogens and increasing germination of wheat seeds and the Variety Aditya was found as best in germination (97%) with lower seed borne Alternaria and the variety NL-297 was found non-disease severity (0.00%).
Agricultural water accounts for more than 70 % of the total global water usage, and the scarcity of global freshwater resources will largely limit global agricultural production. Precision irrigation is the key to improving water efficiency and achieving sustainable agriculture. Accurate and rapid access to crop water information is an essential prerequisite for precise irrigation decisions. Traditional moisture detection methods based on soil moisture and crop physiological parameters are faced with the problems of variable field conditions, low efficiency and lack of spatial information, which can be extremely limited in practical applications. By contrast, unmanned aerial vehicle (UAV) remote sensing has the advantages of low cost, small size, flexible data acquisition time, and easy acquisition of high-resolution image data. Therefore, UAV remote sensing has become an easy and efficient method for crop water information monitoring. This study systematically introduces the principles, methods and applications of crop water stress analysis using the UAV technology. First, the mechanism of crop water stress analysed by UAV is elaborated, focusing on the relationship between canopy temperature, evapotranspiration, sun-induced chlorophyll fluorescence (SIF) and crop water stress. Next, various UAV imaging technologies for crop water stress monitoring are presented, including optical sensing systems, red, green and blue (RGB) images, multi-spectral sensing systems, and hyper-spectral sensing systems. Subsequently, the application of machine learning algorithms in the field of UAV monitoring of crop water information is outlined, demonstrating their potential for data processing and analysis. Finally, new directions and challenges in UAV-based crop water information acquisition and processing are synthesised and discussed, with special emphasis on the prospects of data assimilation algorithms and non-stomatal restriction in monitoring crop water information in the future. This study provides a comprehensive comparison and assessment of the mechanisms, technologies and challenges of UAV-based crop water information monitoring, providing insights and references for researchers in related fields.