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1. E-Demofarm as an Integrated Framework for Education, Training and Extensión in Precision ViticultureThe concept of the demonstration farm has long been central to agricultural education and technology transfer, providing tangible spaces where innovation meets practice. However, traditional approaches remain constrained by geographic and accessibility limitations and the limited integration of modern digital tools, hindering the large-scale dissemination of innovation and practical training. To address these challenges, the e-Demofarm is designed and investigated within the VTskill project, ... M. Pérez-ruiz, T. Scali, A. Michailidis, D. Sarri, A.M. Mouazen |
2. Making Sense of Unreplicated Farmer Experiments Through Causal PathwaysMost farmers experiment on their own farms every season, yet their conclusions are often drawn from limited data and are prone to misinterpretation. This study explores how scientists can support farmer-led on-farm experimentation (OFE) using analytical frameworks that complement — rather than disrupt — farmers' natural learning processes. The research evaluated a nitrogen-fixing inoculant (NFI) containing Klebsiella variicola and Kosakonia sacchari... L. Longchamps, P. Lanza, A. Yore, K. Rohrbaugh |
3. Are Agronomy Programs Preparing Professionals for Digital Agriculture? A Nationwide Curriculum Analysis in BrazilThe digital transformation of agriculture has accelerated the adoption of precision agriculture, artificial intelligence, and data-driven management tools, thereby increasing the demand for professionals equipped with technological and computational competencies. In this context, higher education in Agronomy plays a strategic role in preparing graduates to operate effectively in increasingly digitalized production systems. This study aimed to evaluate the presence of Artificial Intelligence (... L. Silva, D. Gabriel |
4. The Agdatabox Platform Supports Teaching and Research in Precision Agriculture.The AgDataBox platform is an initiative being developed by Brazilian institutions to support small producers and service providers seeking to work with Precision and Digital Agriculture. Among its objectives, it aims to integrate data, software, and methodologies for those wishing to work with precision and digital agriculture through APIs that provide not only data storage support but also the availability of various services, such as automated functionalities for generating thematic maps an... C.L. Bazzi, R. Sobjak, K. Schenatto, E.G. Souza, E. Cely bonilla |
5. Who is the Agricultural Practitioner of the Future?An agricultural industry that continues to adopt new technologies and rely on data-driven decisions demands a unique skillset from its practitioners that goes beyond traditional agricultural training. Despite this demand, relatively few technology-focused agriculture programs are available at the post-secondary level worldwide. In 2020, Olds College of Agriculture & Technology (Alberta, Canada) created a 2-year diploma in Precision Agriculture (launched in 2020) and a 4-year Bachelor of D... D. Karran, B. Hoffos, F.H. Karp |
6. Caliming: A Decision-Support Tool for Soil Liming RecommendationsSoil acidity is one of the main limiting factors for crop production in tropical regions. In this context, liming stands out as the most efficient strategy for correcting soil acidity, promoting pH increase, neutralization of toxic aluminum, and the supply of calcium (Ca) and magnesium (Mg) to plants. Several methods for calculating lime requirement have been developed over time, ranging from classical approaches consolidated in official recommendation handbooks to more recent methods, such a... G. Castoldi, C.R. Rodrigues, E.S. Araujo, S.K. Amaral |
7. Critical Competencies for Digital and Precision Agriculture: Evidence from Literature and Stakeholder PerspectivesThe transition toward digital and precision agriculture is transforming agricultural production through the integration of artificial intelligence, sensor technologies, geospatial systems, robotics, automation, and advanced data analytics. As agricultural systems become increasingly data-intensive and technology-driven, identifying the competencies required for effective implementation has become essential. Although the literature discusses skill requirements in Agriculture 4.0 contexts, limi... J. Mattar, L. Cuque |
8. A Competency Framework for Digital and Precision Agriculture Professionals: Structure and Core DomainsThe rapid expansion of digital and precision agriculture technologies has significantly increased the complexity of contemporary agricultural production systems. The integration of technologies—including GNSS-guided machinery, geospatial analytics, sensor networks, artificial intelligence, robotics, and advanced analytics—requires professionals capable not only of operating digital tools, but also of understanding agronomic processes, systems integration, and data-driven decision-... J. Mattar, L. Cuque |
9. Agro Extensão: Extensão Rural DigitalUniversity extension plays a fundamental role in bridging the gap between academia and society, especially in the agricultural sector, where technical information can determine the success of an operation. A historical example of this relevance is Operation Tatu (1960), which was essential in transforming the agricultural model in Rio Grande do Sul, promoting the shift from rudimentary practices to a technological approach. This initiative became a landmark demonstration of the potential of u... F. Baudini, A.L. Vian, M. Wrubleski, T. , G. Lima leal |
10. Delineation of Management Zones for the Adoption of Precision and Digital Agriculture in Steep-Sloped Arabica Coffee Production AreasCoffea arabica production in Brazil, particularly in regions of São Paulo and Minas Gerais, occurs in environments with a high diversity of climates, altitudes, and soils. The municipality of Caconde (SP) stands out with approximately 11,000 hectares of coffee, predominantly on small properties with altitudes above 800 meters and steep slopes. These characteristics are conducive to the production of high-quality, value-added coffees. Optimizing the use of natural resources and agricult... E.A. Speranza, C.R. Grego, T. Santos, G.C. Rodrigues, R.Y. Inamasu |
11. Comparative Assessment of Proximal Sensing and UAV Multispectral Data for Coffee Vigor AnalysisThe assessment of vegetative vigor through spectral indices, particularly the Normalized Difference Vegetation Index (NDVI), is widely adopted in precision agriculture as a rapid, indirect, and non-destructive method for evaluating plant physiological status. A broad range of sensing technologies has been employed for this purpose, including proximal sensors and multispectral imaging systems mounted on remotely piloted aircraft (RPAs). In this context, the objective of this study was to evalu... E. Zavala, G. , M.D. Oliveira, H. Khalid, P. |
12. Spatiotemporal Variability of Apple Tree Vegetative Vigor Using Proximal SensingThe largest apple production in Brazil is located in the southern region of the country, which has a subtropical climate, borderline conditions for the production of a fruit native to temperate climates. Thus, excessive vegetative growth frequently occurs, negatively impacting productivity and quality in the orchard. This leads to the diversion of productive resources to ancillary activities, such as the application of growth regulators and green pruning, negatively affecting the producer. Cu... L. Gebler, A. De rossi, J.T. De abreu, E.A. Speranza, A. Sessi, L.D. Marchioretto |
13. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in AgricultureSatellite remote sensing has become a key data source for precision agriculture, particularly for monitoring vegetation dynamics and soil water stress over large areas. Multispectral satellite imagery enables the computation of vegetation indices, including NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which are commonly employed to quantify vegetation health, vigor, and canopy development. However, the practical use of satellite imagery in data-driven agr... A. Heideker, E.A. Speranza, E. Ferreira, D. Silva, C. Kamienski, R. Bianchi |
14. Sensing-Based Correlation Analysis of Surface Elevation and Topsoil Depth in Japanese Rice Paddy FieldProper management of soil physical properties is fundamental for stabilizing crop yields and optimizing resource efficiency in large-scale rice paddy production. Among these properties, field surface elevation and topsoil depth (TD) are critical determinants of water management effectiveness and root zone environments. This study conducted a high-resolution grid-based correlation analysis between surface elevation and TD in a 1.4-ha paddy field in Tottori Prefecture, Japan. To overcome the li... E. Morimoto, J. Lee, K. Nishida, H. Itoh |
15. Optical Chlorophyll Sensor in the Identification of Coffee Cultivars Adapted to the Pitangui-MG RegionThe agronomic performance of coffee plants is directly related to the interaction between genotype and environment, making it essential to identify cultivars best adapted to specific growing conditions. In recent years, the incorporation of tools in agriculture has significantly improved the plant evaluation process. Among these technologies, portable sensors, such as the chlorophyll meter, allow for rapid, non-destructive, and highly sensitive measurements of physiological parameters related... A.D. freitas, C.C. Santana, F. Silva, R. Avelar, T.A. Rodrigues |
16. Sensor-based Optimal Delineation of Management Zones for Plant and Soil IntegrationStrategies for mapping management zones (MZs) use proximal and orbital sensors to optimize use and promote sustainable development. Proximal sensors can infer, among other variables, apparent electrical conductivity (ECa), while orbital sensors provide synthetic soil images (SYSI), elevation, and different color-composition images of soil and plant canopies. However, the literature does not provide clear evidence on the efficiency of these measurements, individually and jointly, in reducing t... A.V. Hereman, J.V. Pozzuto, L.R. Amaral |
17. Remote Sensing for Identifying Soybean Cultivars and Estimating Crop YieldsTraditional methods for cultivar identification and agricultural productivity estimation have been increasingly complemented or replaced by innovative approaches using geotechnologies and Artificial Intelligence (AI). These modern techniques offer greater efficiency, speed, and sustainability in agricultural production systems. Among these advances, Remote Sensing (RS) has stood out as an effective tool for agricultural monitoring. It allows the collection of spectral and biophysical informat... B. Matwijou, J.R. Oliveira, M. Da silva, G.G. Scheidt, A. Lopes de brito filho, M.G. Da silva brochado, F. Morlin carneiro |
18. Estimation of Leaf Area in Hydroponic Microgreenhouses by Digital Image Processing and Color-Based SegmentationMonitoring plant development is a fundamental pillar of precision agriculture, as leaf area is a primary indicator of photosynthetic capacity, transpiration rates, and overall biomass accumulation. In controlled environments such as hydroponic micro-greenhouses, real-time growth monitoring enables timely adjustments to nutrient film technique (NFT) parameters. However, conventional leaf area measurement methods often rely on invasive sampling or expensive, high-maintenance optical equipment, ... M. Andrade, J. Da silva fonseca, R. Toledo, F. Martin carbajal gamarra |
19. Detection of Weed-Related Anomalies in Sugarcane Fields Using Sentinel-2 ImageryWeed infestation is one of the main causes of yield losses in agricultural systems, particularly in large-scale crops such as sugarcane. Conventional weed management, based on uniform herbicide application, often ignores the spatial variability of infestations, resulting in higher production costs and environmental impacts. In this context, remote sensing and machine learning techniques are recently being used as a solution for automation and precision in crop monitoring. In this st... R.P. Amaro, F. Amstalden, C. Berro filho, D.G. Duft |
20. Assessing the Capability of Apparent Soil Electrical Conductivity to Replace Soil Texture in the Delineation of Management Zones: a Case StudyThe delimitation of management zones (MZ) depends on the appropriate selection of information layers used in the clustering process. Traditionally, soil physical attributes with greater temporal stability, such as texture, have been widely employed due to their direct influence on water retention and nutrient availability. However, the acquisition of soil texture data is generally costly, time-consuming, and based on point sampling. In contrast, apparent soil electrical conductivity (ECa) has... M. Gelain, L. De goes sterle, J.P. Molin |
21. Estimation of Weed Germination (Eleusine Indica and Amaranthus Spp.) Using Proximal Remote SensingWeeds are significant obstacles to the yield of commercial crops, leading to considerable economic losses worldwide. These plants compete directly with desired crops for essential resources such as water, light, and nutrients, and they can complicate agricultural management practices. In this context, early identification and understanding of the initial stages of weed development, such as germination, are crucial for enhancing management strategies and reducing both economic and environmenta... T. Prestes pedroso , F. Morlin carneiro, A. Lopes de brito filho, C.M. teixeira fialho, M.G. Da silva brochado, F. Morlin carneiro |
22. Evaluation of Diffuse Reflectance Spectroscopy and Machine Learning Methods for Soil Available Phosphorus and Potassium PredictionPhosphorus (P) and potassium (K) are essential elements for plants. Accurate evaluation of soil available P and K contents is fundamental for precision agriculture and site-specific nutrient management. However, traditional chemical analyses are time-consuming, labor-intensive, and costly. In this context, diffuse reflectance spectroscopy (DRS) has been introduced as a cheaper and rapid alternative; however, its accuracy in estimating soil P and K contents has not been fully proven, particula... C. Guerra martins, D.L. Grando, J. Moura bueno, G. Brunetto, A.A. Kokkonen, L. Peranzoni deponti, L. Bastos |
23. Utilization of Proximal Remote Sensing As a Non-destructive Method for Assessing the Quality of Corn SeedsThe physiological quality of corn seeds plays a key role in crop establishment. It directly influences final productivity. Although germination and vigor tests are well established, they have practical limitations. These tests are time-consuming. They require laboratory infrastructure and can involve destructive procedures. These factors limit their use in situations demanding faster, scalable assessments. In this scenario, proximal remote sensing has gained attention as a practical, non-dest... A.E. Dos reis rodrigues, M.A. Da silva, J.D. Rodrigues oliveira, G. Ribeiro silva, A. Lopes de brito filho, M.G. Da silva brochado, N.G. Krohn, F. Morlin carneiro |
24. Detection of Chlorophyll A and B in Maize Using Visible Reflectance under Conventional and Variable Rate Nitrogen ManagementPlant-environmental interactions regulate physiological aspects that determine crop yield. However, measuring physiological parameters in-field remains a challenge, as sampling often requires time-consuming laboratory analysis. Therefore, this study analyzed the influence of reflectance using a handheld sensor in detect chlorophyll A and B in different nitrogen application strategies before and after sidedressing. The experiment was conducted at a commercial farm in Campo do Meio, Minas Gerai... T. Costa barboza, O.P. Da costa, R.F. Queiroz, L. Lacerda, A. Felipe dos santos, S.G. Silva |
25. Use of a Chlorophyll Meter As a Decision-support Tool for Nitrogen Management in Coffee FieldsCoffee is a crop with high nitrogen (N) demand in Brazil; therefore, improving N use efficiency in coffee plantations can have significant environmental and economic impacts. Decisions regarding N application rates in coffee cultivation are often empirical or based on multiple factors, including expected yield and plant N status as determined by leaf N concentration. Traditionally, leaf N concentration is obtained through laboratory chemical analysis, a process that may take several days and ... D. Almeida, M. Pereira, A. , V. Giroto, G. Portz |
26. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction ModelsSoil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral prep... V. Ormeño, A. Ten caten, J.P. Alves henriques, M.A. Maciel reva, M. Sousa silva |
27. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model PredictionsSpatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fe... V. Ormeño, A. Ten caten, J.P. Alves henriques, M.A. Maciel reva, M. Sousa silva |
28. Validation of UAV-Based NDVI Using Proximal Sensing in Mountain Coffee PlantationsUnmanned aerial vehicles (UAVs) with multispectral sensors have expanded remote sensing applications in precision agriculture, however validation against ground-truth measurements remains critical, especially for perennial crops in complex terrain such as coffee plantations in mountainous regions. Proximal sensing can be employed as a reference for evaluating the quality of data obtained by remote platforms. This study aimed to evaluate the reliability of Normalized Difference Vegetation Inde... B. Costalonga vargas, I.A. Barbosa, D. Queiroz, A.L. Coelho, D.M. Valente, M.C. Moreira |
29. From Pocket to Pixels: Smartphone-Based Proximal Sensing for Quantitative Monitoring of Peach Canopy DynamicsMonitoring peach canopy dynamics is essential for optimizing irrigation scheduling and promoting sustainable water management in intensively managed orchards. In well-watered systems, conventional irrigation practices often overlook spatial and temporal variability in canopy size and leaf area index (LAI), potentially leading to inefficient water use. Canopy size, as reflected by LAI—the ratio of total leaf area to ground area—serves as a key biophysical indicator linked to crop t... L. Katz, K. Genkin, A. Landau, Y. Ness, O. Crane, R. Golan, N. Rotbart, E. Nevo, O. Shapira, E. Fereres, O. Reichmann, A. Brook |
30. Mobile Robotic Soil Moisture Sensing and Mapping for Precision Agriculture in Small-Scale FarmingThe spatial variability of soil moisture is a key factor for efficient irrigation management and agricultural productivity, particularly in small-scale farming systems where access to precision agriculture technologies remains limited due to cost and operational complexity. This study presents the development and preliminary evaluation of a robotic sensing system for georeferenced soil moisture data acquisition, focusing on spatial mapping in small agricultural areas. The proposed s... D.H. Hall, M. Hosser, R.L. Machado, R. Galli, A.L. Machado |
31. Vegetation Indices Behavior in Soybean Management Zones Based on Apparent Soil Electrical ConductivityUnderstanding the spatial variability of crop development is essential for improving decision-making in precision agriculture. Vegetation indices derived from remote sensing have been widely used to monitor crop growth, biomass accumulation, and spatial heterogeneity within agricultural fields. When combined with management zones, these indices can provide valuable insights into the relationship between soil properties and crop performance. The objective of this study was to evaluate the beha... E.L. Bottega, Z.B. Oliveira, F.D. Mundstock, M.C. Sausen, G.B. Bernhard |
32. Delimitation of Management Zones in Agroforestry Coffee SystemsThe search for more sustainable agricultural production systems is a common goal for both agroforestry systems and precision agriculture. Integrating precision agriculture technologies into coffee cultivation within agroforestry systems has the potential to make these systems more efficient, productive, and sustainable. Thus, this study aims to test the effect of different variables on the delimitation of management zones (MZs) in agroforestry systems. The study was conducted on two coffee-pr... W. Silva dos santos, D. Queiroz, F.C. Pinto, A.L. Coelho, B.N. Vasconcelos |
33. Prediction of Soil Potassium Concentration Using Electrical Impedance SpectroscopyThe increasing global dependence on potassium fertilizers and instabilities in international trade, intensified by recent geopolitical conflicts, have raised concerns regarding potassium (K) availability and costs in 2026. Since variable-rate fertilizer application enables the optimization of agricultural inputs, the delineation of management zones based on the spatial variability of soil potassium concentration becomes essential. This study aimed to evaluate the use of electrical impedance s... I. Salvador, A.L. Coelho, S.V. Valarares, D. Queiroz |
34. Recalibration of Spectral Models Using Spiking Techniques for Predicting Primary Nutrient AttributesSoil spectral libraries are an important strategy for rapid prediction of soil fertility atributes in digital agriculture projects. However, their predictive performance may decline when models are applied outside the specific conditions for which they were calibrated. Even in regions with similar pedoclimatic characteristics, management practices can limit model accuracy. In this context, spiking-based recalibration has been proposed as a practical strategy to improve model performance, alth... A. Ten caten, V. Ormeño, J.A. Henriques, M.M. Reva, M.S. Silva |
35. Combining Orbital and Proximal Sensing to Map Management Zones in Precision Viticulture: A Spatiotemporal AnalysisPrecision agriculture stands out by mapping the spatiotemporal variability of vineyards to understand the interdependence between causes and effects throughout production cycles. Vegetative vigor, which can be estimated using vegetation indices calculated from proximal and orbital sensors, is a fundamental parameter for indicating this variability and assisting in the definition of potential, time-constant management zones. The objective of this study was to evaluate and compare the use of pr... E.A. Speranza, J.R. Da silva, L.R. Correa, L.H. Bassoi |
36. Carbon Stock Assessment in Macaúba (Acrocomia Aculeata) Crops Based on Aerial Digital ImagesIn the current context of climate change, a palm tree named Macaúba, native to the Brazilian Cerrado, has gained prominence as a more sustainable alternative to oil palm, standing out for its high capacity to fix atmospheric carbon throughout its cycle. However, there is a lack of methodologies capable of quantifying carbon stocks in large-scale plantations in a cost-effective way, and manual sampling is still common. In this context, the main objective was to evaluate the effectivenes... P.M. De sousa, B.C. De albuquerque, V.A. Galvan, L.D. Corrêdo, L.D. Pimentel, J. Souza |
37. Stability-driven Framework for Robust Plant Spectral Signature IdentificationAccurate identification of agricultural crops based on spectral signatures remains a critical challenge for large-scale phytosanitary monitoring. This study proposes a stability-based structure for the robust identification of plant spectral signatures, applied to the discrimination of soybean (Glycine max) from maize (Zea mays) and cotton (Gossypium hirsutum) under biotic stress caused by the pest Spodoptera frugiperda and stink bugs. The proposed method follows a flow of proposed steps that... J. Ferreira , A.O. françani, E. . Ferreira, L.A. Jorge, J.C. Felipe, L. Zhao |
38. Precision Agriculture in Cocoa Production: Evaluating RGB-Derived GNDVI as a Low-Cost Multispectral Proxy in the Brazilian AmazonCocoa production in the Xingu region of Pará, Brazil, represents the country’s main production hub, accounting for more than 76% of the state’s total output. Despite its economic importance, cocoa management remains largely traditional, with limited adoption of Precision Agriculture (PA) technologies due to the high cost of multispectral sensors and restricted technological access among growers.This study evaluated the seasonal compatibility between mean visible-spectrum re... R.D. Santos, C. Silva e silva, N. Cornejo noronha, C. tanajura caldeira, . soares cardoso, W. De pinho alvarez |
39. Analysis of Mixed Models in UAV-based Spectral Vegetation Indices for Prediction of Agronomic Variables in Soybean Subjected to FloodingThe identification of soybean genotypes with increased flooding tolerance is relevant for yield stability in lowlands producing areas. In this context, the use of relevant spectral vegetation indices based on multispectral sensors embedded in unmanned aerial vehicles (UAVs) for the selection of more flooding-tolerant soybean genotypes is a primary demand within plant phenomics. Nonetheless, the environmental effects can change the accuracy of spectral indices and the correct methodology for d... C.D. Lima, B. Nogueira, A. , D.U. Junior, I.R. Carvalho, C. Bredemeier |
40. Optimization of Electrochemical Device Development: Laser-Induced Graphene Electrode as an Alternative for Agricultural Monitoring.The agroindustrial sector has driven the technological development of electrochemical devices aimed at field applications. In this context, laser-induced graphene (LIG) electrodes stand out for enabling electrode miniaturization, favoring in situ analyses and equipment portability. These devices exhibit high sensitivity, selectivity, rapid response, and low cost, characteristics that expand their application potential in different scenarios. However, the growing demand for these devices ... C. Miler, L. Gonçalves, B. Vasconcellos lopes, B.B. Gallo, A. La rosa, D. Fruchtenicht, P. Silveira, F. , N. Carreño, L. Machado |
41. Proximal and suborbital vegetation indices in yield prediction of ‘Syrah’ grapevinesThe various vegetation indices (VIs) reported in the literature, derived from different wavelengths, necessitate identifying the most suitable spectral combinations to represent agronomic variables in precision viticulture. This study evaluated the performance of proximal and suborbital VIs to explain the spatial variability of yield of the ‘Syrah’ grapevine. The study was conducted in a trellised vineyard under double pruning management in Ribeirão Preto, state of Sã... L.H. Bassoi, L.A. Jorge, A. Pereira, I. Oliveira junior |
42. Consistency of Three Vegetation Indices from Suborbital and Proximal Sensing in Precision ViticultureThe integration of proximal and suborbital sensing platforms can expand the practice of precision viticulture. However, the consistency of vegetation indices (VIs) derived from different sensors remains a critical issue. This study quantified the agreement between VIs obtained by proximal and suborbital sensing using complementary metrics of association, error, and agreement. The research was conducted in a ‘Syrah’ vineyard in Ribeirão Preto, state of São Paulo, Braz... L.H. Bassoi, L.A. Jorge, A. Pereira, I. Oliveira junior |
43. Development and Field Validation of a Mid-Infrared Proximal Sensing System for In-Season, On-Vine Monitoring of Grape CompositionAccurate in-season monitoring of grape composition is important for precision agriculture, as it supports timely decisions on crop management and harvest scheduling. In practice, however, commonly used approaches for assessing internal quality—such as refractometry and near-infrared (NIR) spectroscopy—are often applied to harvested samples, which limits their use for truly non-destructive measurements on developing fruit in the field. We have been developing a novel mid-... H. Furukawa |
44. Importance of Irradiance Correction for UAV-based Vegetation Indices in the Prediction of Shoot Biomass in WheatThe extraction of vegetation indices from multispectral images obtained with UAV-bsed sensors for biomass estimation has proven to be a useful tool for designing site-specific interventions on wheat. The reflectance values from monochromatic bands collected with optical sensors for calculating vegetation indices should have high reliability and repeatability in the case of successive assessments under different illumination conditions. In this context, the objective of the study was to charac... A.C. Figueiró, B. Nogueira, E. Bender, R. Silva, V.M. Cassol, S.J. Silveira , C. Bredemeier, A.L. Vian |
45. Definition of Flight Height for Image Monitoring of Pupunha Palm CultivationThe use of Unmanned Aerial Vehicles (UAVs) associated with computer vision has expanded the applications of precision agriculture, especially in perennial crops that require detailed spatial monitoring. In the peach palm tree, the automated detection of clumps from aerial images is an efficient alternative to traditional methods, contributing to management and production estimates. Thus, the objective of this study was to evaluate the influence of flight height on the performance of ... D.A. Botta de siqueira, J.P. Molin, B.B. Barreto, M.L. Silva, P.R. Fiorio |
46. Evaluation of Vertical C/N Distribution in Maize Canopy Using Ensemble Learning with Hyperspectral DataMetabolic status of carbon (C) and nitrogen (N) as two essential elements in crop plants has essential influence on the ultimate formation of yield and quality in crop production. Ratio of carbon to nitrogen (C/N), defined as the ratio of LCC (leaf carbon concentration) to LNC (leaf nitrogen concentration), is useful for understanding and quantifying carbon and nitrogen metabolism in crops, and is one metrics for effectively evaluating the balance of carbon and nitrogen, nutrient status and g... X. Xu, X. Xu, Y. Meng, G. Yang, Y. Song, H. Xue |
47. Temporal Extrapolation of N-uptake Maps Retrieved from Satellite Imagery Aiming to Overcome Availability Issues Due to Cloudy DaysFor a series of satellite-based functionalities, including in-field crop nitrogen variable rate fertilization, an up-to-date satellite image is required. Due to cloudy conditions, actual images, depending on weather, may be highly compromised or unavailable. To overcome this issue, and aiming to obtain an image showing the actual field status, a simple crop growth model to extrapolate on time the crop growth of the last available image was created and implemented in a fully automatized proces... G. Portz, S. Reusch |
48. Nonlinear Modeling of Vegetation Response to Rainfall Variability in the Brazilian Semi-Arid Region Using Sentinel-2 and CHIRPS DataHigh climate variability in the Brazilian semi-arid region poses significant challenges to agriculture and the sustainable management of Caatinga ecosystems, requiring monitoring tools capable of anticipating vegetation responses to rainfall fluctuations. However, the spectral response of vegetation to precipitation does not always follow linear patterns and may reflect ecohydrological thresholds and water saturation effects. Sentinel-2 data were used to derive the Soil Adjusted Vegetation In... A. Fonseca, E.F. Da silva, A.C. Bezerra, G.B. Moura, J.F. Dos anjos, E.D. Figueirôa , G.P. coelho, J.P. De andrade, E. Lopes, J.I. Silva |
49. Manifold-Based Time-Lag Analysis of Soil Water Availability and Satellite Vegetation Indices for Irrigation Monitoring in Coffee PlantationsMonitoring soil water availability is essential for optimizing irrigation in perennial crops such as coffee, yet deploying dense in situ sensor networks remains impractical at scale. Although sensors like IGstat provide high-fidelity measurements of soil-water matric potential (SMP), their installation and maintenance costs limit broad adoption. A scalable alternative is to integrate sparse in situ observations with satellite-derived vegetation indices, including the Normalized Difference Moi... F. Johari, E. Ferreira, R. Prati |
50. Can Soil Profile Mapping and AI‑Assisted Yield Analysis Improve Explanation of Within‑Field Yield Variability? A Depth‑Resolved Case StudyWithin-field yield variability is commonly evaluated using surface soil test results and whole-field statistical correlations. However, these approaches often fail to explain why areas with acceptable surface pH and nutrient levels continue to underperform, while neighboring zones with similar or even lower soil test values yield substantially better. This study evaluates whether depth-resolved soil profile characterization can improve diagnosis of spatial yield variability. Rather ... E. Lund, T. Lund, C. Maxton |
51. Remote Sensing for Identification of Soil Texture Variability in Precision AgricultureUnderstanding spatial variability of soil texture is essential for site-specific management in Precision Agriculture. Traditional approaches rely on intensive soil sampling and apparent electrical conductivity (ECa) surveys, which provide high-quality information but may be costly and difficult to scale. Satellite remote sensing offers a promising alternative by enabling indirect estimation of soil properties through spectral responses. This study evaluated the potential of Sentinel-2 spectra... T. , L.R. Amaral |
52. Soil Structural Gradients as Drivers of Multiyear Spectral Variability: A Robust Framework for Management Zone Delineation in Heterogeneous Sugarcane SystemsPersistent soil structural gradients are widely recognized as key drivers of spatial heterogeneity in perennial cropping systems. In the heterogeneous sugarcane production environments of northwestern Argentina (Salta and Jujuy provinces), contrasting sandy and clay-rich sectors generate long-term differences in crop growth potential and resource-use efficiency. Distinguishing stable edaphic influences from transient seasonal variability is essential for reliable precision management. This st... H.J. Fernandez |
53. Sensor Fusion and Fitness-for-purpose Screening for SOC Prediction in Vis–NIR Spectral Libraries, Insights from Vis–NIR Plus XRF, Local Calibration, and MRV ThresholdsSoil organic carbon (SOC) monitoring requires analytical tools that are accurate and scalable for measurement, reporting and verification process (MRV). In practice, MRV programs also require transparent error interpretation so that sensor-based inference can be linked to decision risk under different field conditions. We assessed whether fusing X-ray fluorescence (XRF) information with vis–NIR spectra improves SOC prediction when models are trained on large spectral libraries and trans... T. Tavares, L.A. Da silva, E. , M.R. Cherubin |
54. Assessing the Potential of Google Satellite Embeddings for Mapping Sugarcane in Brazilian Production AreasThe increasing availability of remote sensing (RS) data with higher spatial resolution, combined with advances in artificial intelligence (AI), has been an essential tool in driving the development of the agricultural sector, such as precision agriculture (PA). Among the most relevant information derived from these approaches, crop mapping plays an essential role in crop monitoring, management strategies and yield forecasting. However, the large amount of data required for training classifica... G. Rodigheri, J. Da silva, L. Alvim santos romani, J. Garcia arnal barbedo |
55. Estimation of Sugarcane Yield Based on Phenological Feature Extraction from Time-Series Sentinel-1 Images and Machine LearningDue to frequently rainy and cloudy weather in the main sugarcane production areas, optical remote sensing data are often missing, and the conventional yield estimation models based on radar remote sensing data lack the support of crop growth mechanisms. This study aims to explore a new yield estimation method for capturing the key dynamic growth features of sugarcane under all-weather conditions. This study takes the sugarcane yield in the dominant area of sugarcane production, Guangxi Zhuang... H. Xue, X. Xu, G. Yang, Z. Xu, S. Xiaoyu, L. Chen |
56. Assessment of Machine Learning Models for Leaf Chlorophyll Estimation Using Visible-Range ReflectanceChlorophyll content plays a central role in the photosynthetic process directly influencing plant growth, development and yield. However, plant pigment dynamics arise from complex metabolic interactions that are not adequately captured by conventional statistical approaches or traditional laboratory analyses, which are time-consuming and impractical for large-scale field applications. In this context, remote sensing offers a non-destructive alternative for assessing foliar pigments in agricul... T. Costa barboza, W. Batista da silva, S. Guimarães moreira, S. Godinho silva, L. Lacerda, A. Felipe dos santos |
57. Spectral Behavior of Coffee Fruit Ripeness Using a Hyperspectral CameraSelective harvesting is essential to ensure high beverage quality in coffee production; however, the coexistence of fruits at multiple ripeness stages on the same plant makes manual selection subjective, labor‑intensive, and time‑consuming. This preliminary study aimed to develop a non‑destructive method based on spectral information for the classification of Coffea arabica L. cv. Arara fruits at green (unripe) and yellow (ripe) stages, using images acquired on a laboratory bench with h... A. Palma diniz baker, G. , M.D. Oliveira, E.H. Zavala, R. , M.M. Amaral, A. |
58. Simplifying Lab Analysis for Mapping Texture and Om Content Via Sensor-based Inference: a Case Study Showing Maps of Eca and Traditional MethodsPrecision agriculture hinges on soil information at spatial resolutions that capture within-field variability, yet conventional laboratory workflows often constrain sampling density due to cost and turnaround time. In this study, we evaluated a laboratory-based sensor inference service for mapping soil organic matter (OM) and texture (clay and sand) using visible and near-infrared spectroscopy (vis-NIR) and X-ray fluorescence (XRF) spectroscopy. We further evaluated whether resulting maps are... -. -, E. Casciello, J. Pozzuto, T. Tavares, L. Da silva, H.W. De carvalho |
59. Evaluation of the Similarity between Management Zones Based on Spectral Indices and Soil Electrical ConductivityThe delineation of management zones is one of the main strategies in precision agriculture, as it enables site-specific interventions and improves input-use efficiency. Soil electrical conductivity (EC) is widely used to represent the spatial variability of soil physical and chemical attributes and is often considered a reliable indicator. However, acquiring EC data requires specific equipment and field operations, which may increase operational costs. In contrast, spectral indices derived fr... |
60. A Statistical Approach to Defining Coffee Management Zones: Integrating Apparent Soil Electrical Conductivity, Altimetry and Satelitte Indices for Moisture MonitoringCharacterizing the spatial and temporal behavior of soil and plant attributes represents the elementary step toward adoption precision agriculture. The expanding availability of multi-temporal remote sensing imagery with enhanced spatial resolution has rendered the delineation of management zones (MZ) an increasingly feasible strategy, especially when the intention is to carry out spatially differentiated interventions considering the vegetative vigor throughout the crop cycle or the plant yi... E.A. Speranza, E.J. Ferreira, L.H. Bassoi, L.M. Rabello, C.M. Vaz, A. Torre-neto |
61. Producing Ordinary Kriging Interpolated Maps for Biomass Observation Through Values Captured with NDVI and NDRE Imagery.Geostatistics is a well-established method in the scientific community for aiding decision-making in situations with spatial dependence. Generally, the methodology adopted for interpolating fertility maps is the use of data from soil sampling on the property, generating representative thematic maps. However, the number of samples required for this methodology can be problematic when the analysis site is a small farm or one divided into multiple plots, common scenarios in Brazilian coffee farm... H.F. Gebler, C.R. Grego, G.C. Rodrigues, A. Pereira, F. Fagundes |
62. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated CornSensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal ... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal |
63. Impact of Mechanical Decompaction on Soybean Yield through Remote Sensing and Agronomic VariablesSoil compaction is one of the main limiting factors for agricultural productivity, requiring efficient diagnostic and management methods. Increased machinery traffic in agricultural areas alters soil physical properties, reducing macroporosity and increasing soil penetration resistance, which restricts root growth and limits water and nutrient uptake. Traditional field methods for diagnosing soil compaction are often punctual and labor-intensive, and they may not adequately represent the spat... |
64. Leaf Nutrient Estimation in Soybean from Multispectral and Multitemporal Information Using UAV and Machine LearningPrecision agriculture, through remote sensing with Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence, offers solutions for monitoring crop growth and development, supporting decision-making aimed at resource optimization and agricultural sustainability. This study evaluated the feasibility of using multispectral information captured by UAVs at different phenological stages (V6, V8, and R2) to estimate leaf nutrients (N, P, K, Ca, Mg, Cu, Zn, and Mn) in soybean crops as an alternativ... E. Cely bonilla, C.L. Bazzi, R. Sobjak, K. Schenatto, E. Torres avila, M. Rodrigues, S. Spricigo |
65. Quality of Interpolated Maps of Soil Mechanical Resistance to Penetration as Support for Variable Rate Compaction ManagementReliable continuous maps of soil mechanical penetration resistance (SMPR) are essential for site‑specific compaction management, but their usefulness depends on how interpolation methods respond to different spatial dependence patterns along the soil profile. Rather than seeking a single “best” interpolator, this study explicitly addresses how the suitability of interpolation methods varies as a function of the spatial structure of SMPR, an aspect seldom explored in depth in pre... R.P. Silva, J.D. Riquiel, A. Andrade da silva, E. Sales, T.M. Oliveira, R. De souza silva |
66. Adding Plant Water Uptake Monitoring to a Low-Cost Wireless Multi-Sensor Data Aggregation Platform for Precision AgricultureMost low-cost precision agriculture monitoring systems only measure soil conditions such as moisture and pH, leaving plant response to water availability unmonitored. Soil moisture alone does not indicate whether a crop is taking up water or experiencing stress. Commercial plant-sensing systems that could fill this gap are expensive and operate as standalone instruments that are difficult to incorporate into existing multi-sensor field networks. ... K. Wing, D. Onyeoguzoro, M. Everett, J.C. Shovic |
67. Growth-Stage and Hourly Modeling of Non-Stressed Soybean Canopy Temperature Using High-Frequency Proximal Thermal SensingCanopy temperature (Tc) sensing provides a proximal, non-destructive approach for monitoring crop water status. It supports irrigation scheduling through thermal indices such as the Crop Water Stress Index (CWSI) and Degrees Above Non-Stressed (DANS), both of which require accurate estimation of non-stressed canopy temperature (Tcns) (Nakabuye et al., 2022). Maintaining a continuously non-stressed reference treatment to determine Tcns is operationally difficult, motivating development of weat... |
68. Estimation of Soybean Yield Using Remote Sensing and Soil Physical Attributes in Subsoiled AreasPrecision agriculture has incorporated these sensing and computational modeling technologies as strategic tools for monitoring crop development and estimating yield. In this context, the present study aimed to estimate soybean yield through vegetation indices obtained from satellite images, integrated with soil and plant variables, using artificial intelligence techniques. The experiment was conducted in a commercial field in the municipality of Brejo, Maranhão, in a region with a subh... W. Garreto, S. De almeida, W. Da silva sousa, J. Costa souza |
69. Selection of UAV-based Vegetation Indices for the Prediction of Leaf Chlorophyll Content in Maize Using a Normalized Partial Least Squares Regression (PLSR) Reduction ApproachThe accurate monitoring of the nutritional status is essential for optimizing nitrogen (N) fertilization and maximizing maize grain yield. Variations in N availability directly affect agronomic parameters such as leaf chlorophyll content, which can be estimated using optical sensors. This study assessed the effects of urease inhibitors and nitrogen application rates on leaf chlorophyll content and predicted total leaf chlorophyll content in maize using relevant vegetation indices under field ... B. Nogueira, E. Bender, D. De carvalho arruda, M. Da costa salem, L. Espindola muller, S.R. Dos santos gonçalves junior, G. Eissmann souza, J.V. Muller klassmann, B.B. Gallo, C. Bredemeier |
70. Automated Detection of European Canker (Neonectria ditissima) in Apple Trees via Multispectral Sensors and Computer VisionEuropean canker, caused by the fungus Neonectria ditissima, represents one of the major economic challenges for Brazilian pomiculture, severely affecting ‘Gala’ and ‘Fuji’ cultivars. The disease manifests primarily in woody tissues, such as trunks and branches, although it can also cause fruit rot during the pre-harvest stage. Infection occurs obligatorily through wounds, whether natural (leaf scars) or resulting from management practices (pruning and harvesti... S. Alves, E.C. Da silva, L.D. Marchioretto, L. Gebler |
71. Hyperspectral Imagery for Prediction of Leaf Chlorophyll Content in Maize Under the Application of Different Urease Inhibitors Using Machine LearningUrea is the most common and widely used nitrogen (N) source. However, it is highly susceptible to ammonia volatilization losses, especially under favorable climatic conditions. The use of urease inhibitors becomes an important strategy because these compounds slow down the hydrolysis of urea, increasing efficiency in terms of N assimilation, enhancing leaf chlorophyll content, promoting plant growth, and maximizing maize grain yield. In parallel, hyperspectral sensors have emerged as a non-de... S.R. Gonçalves junior, M. Da costa salem, G. Eissmann souza, D. Carvalho de arruda, E. Bender, B. Nogueira, L. Espindola muller, B.B. Gallo, C. Bredemeier |
72. Development of Predictive Models for Determining Organic Carbon and Clay Content in Soils under Irrigated Fruit Production in the Brazilian Semi-Arid RegionThe agricultural sector plays a pivotal role in both greenhouse gas emissions and climate change mitigation through soil carbon sequestration. Total organic carbon (TOC) and soil texture - particularly clay content - are key indicators of this dynamic, as they influence organic matter stabilization, water retention, and soil structural quality. In semi-arid regions, where edaphoclimatic conditions and water scarcity constrain agricultural production, the integrated assessment of these at... A.L. Santos, A.A. Linhares, P.C. Barbosa , W.C. Lopes, M.D. Reis, K.D. Rocha, D.G. De oliveira, M.S. Rodrigues, D.D. Costa |
73. Maturity Monitoring in Chickpea Using RGB Images Obtained by UAVsChickpea is a legume of great importance for global food security, and precise maturity monitoring is fundamental to optimize harvest timing and reduce grain losses. Remote sensing using unmanned aerial vehicles (UAVs) equipped with RGB cameras offers a non-destructive and high-throughput alternative for crop phenotyping, enabling rapid and reliable assessments of maturation progression. In this context, this study aimed to identify the best vegetation index based on RGB aerial images to moni... F. Soares, C.C. Santana, R. Avelar, F.O. Silva, A.L. Carvalho |
74. NDVI Index and Its Correlation with Biennial Coffee YieldPrecision agriculture (PA) has consolidated itself as an important tool in agricultural management, allowing greater productive efficiency, cost reduction, and environmental sustainability. Among PA tools, the use of vegetation indices stands out, as it allows inferences about crop biomass both spatially and temporally. In this context, this study aimed to evaluate the relationship between the normalized difference vegetation index (NDVI) and the yield of Arabica coffee (Coffea ... L.V. Lazzarini, S.M. Hurtado, I.D. Gonçalves, M.F. Carneiro filho, A. , G.P. Cândido |
75. Temporal Dynamics of Chlorophyll Indices in Corn Leaves in Response to Top-dressing with Organo-mineral FertilizerNitrogen is one of the main limiting factors for maize productivity, and managing it efficiently is fundamental for sustainable agriculture. Understanding how different nitrogen fertilization strategies affect plant physiological parameters throughout the crop cycle is essential for optimizing nutrient use efficiency. This study aimed to evaluate how chlorophyll indices (total, a, and b) in maize leaves vary throughout the cycle in response to different rates of organo-mineral fertilizer appl... L.C. e. ribeiro, M. Antônio, A. Batista santos, E. Sousa meneses, C.C. Santana |
76. Detection of Plants with Xylella fastidiosa in Olive Orchards Using Aerial Multispectral, Thermal Imagery and Machine LearningEarly detection of Xylella fastidiosa in olive orchards remains a significant phytosanitary challenge due to the difficulty of identifying infected plants during the initial symptom development phase. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable approach for disease monitoring at field scale. This study evaluated the potential of spectral indices derived from multispectral and thermal imagery for classifying the occurrence of... F. Silva, M. Antônio, G. Koch, P.A. Moura, C.C. Santana |
77. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal ImageryAccurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluat... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana |
78. Prediction of Leaf Nitrogen in Corn Cultivated Under Organomineral Fertilization Using Machine Learning and Multispectral Aerial ImageryOne of the main determining factors of corn productivity is nitrogen (N) fertilization. However, significant knowledge gaps still persist regarding plant responses to top-dressing nitrogen application with organomineral fertilizers. Conventional methods for assessing nutritional status, such as leaf tissue analysis and portable chlorophyll meters, although efficient, are limited by their point-in-time nature, failing to adequately represent the spatial and temporal variability throughout the ... E. Sousa meneses, M. Antônio, A. Batista santos, L.C. e. ribeiro, C.C. Santana |
79. Textural Indices from Multispectral Images Improve Leaf Nitrogen Prediction in Corn Using Machine Learning Models?The pursuit of more accurate models for predicting nitrogen (N) in corn has driven the exploration of variables beyond traditional spectral indices. While effective, vegetation indices often fail to capture the full complexity of the canopy structure, and conventional methods like leaf analysis are constrained by their point-in-time nature. Recent studies have demonstrated that Gray Level Co-occurrence Matrix (GLCM) texture features derived from low-altitude remote sensing platforms can signi... A. Batista santos, E. Sousa meneses, L.C. e. ribeiro, M. Antônio, C.C. Santana |