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1. Predictive Analysis of Fertilizer Efficiency with Machine LearningFertilizers play a key role in agribusiness, both as an essential input for agricultural productivity and as a strategic component in the commercial chain. They provide nutrients that are indispensable for soil correction and crop growth, such as nitrogen, phosphorus, and potassium, allowing the soil to maintain its capacity to sustain crops even after several harvests. It is estimated that about 50% of global food production depends on the use of fertilizers, and in Brazil, these inputs repr... C.S. Santos, R.K. Weber |
2. A Model to Support Decision-making in the Generation of Management Zones for Fruit GrowingImplementation of precision fruit farming faces challenges in accurately defining these zones, mainly because, as the orchard reaches the productive phase, the relevance of soil fertility decreases compared to other phytotechnical and physiological parameters. Correct generation of management zones is crucial for the success of the operation, but the accurate interpretation of the collected data requires highly qualified professionals with years of experience, a gap that limits the adoption o... L. Gebler, J.M. Dias |
3. Data Governance Platform for Precision Agriculture: Enhancing Traceability and SustainabilityPrecision Agriculture (PA) is one of the enablers of data-driven agriculture. Digital Agriculture (DA) tools are increasingly vital in driving the adoption of PA techniques across small, medium, and large-scale farming operations. These technologies, including the Internet of Things (IoT), sensors, drones, satellite imagery, Artificial Intelligence (AI), and Big Data, work synergistically to capture detailed information on soil conditions, plant health, climate, and machinery performance. Thi... E.A. Speranza, R.Y. Inamasu, L.A. Romani, J. Naime, R. Sobjak, I. Vacari, C.L. Bazzi, S. Shibusawa |
4. Adapt Standard: Enabling Interoperability in Agricultural Field Operations DataModern agriculture increasingly relies on sophisticated technologies, including precision farming equipment, sensors, laboratory analyses, and farm management software, to generate critical operational data. Despite these advancements, the industry faces significant interoperability challenges, resulting in fragmented data ecosystems that impede optimized decision-making. While ISO 11783 (ISOBUS) successfully facilitates electronic communication at the machinery level, it does not adequately ... B. Craker, S.T. Nieman, J.W. Wilson, S. Rhea, K. Nelson, D. Danford, J.A. Wilson, B. Kemp |
5. Spatial Data Interpolation in the AgDataBox Platform Using Graphics Processing Unit ParallelismPrecision agriculture platforms increasingly operate as integrated ecosystems that collect, store, and process large volumes of heterogeneous spatial data originating from multiple sources, including soil sampling, onboard sensors embedded in agricultural machinery, yield monitors, and remote sensing technologies such as satellites and unmanned aerial vehicles (UAVs). These platforms play a fundamental role in transforming raw georeferenced data into actionable information that supports site-... R. Sobjak, V.H. Malacarne, C.L. Bazzi, E. Souza, K. Schenatto, M. Rodrigues |
6. Management Zone Delineation Replacing Yield Maps with Vegetation Indices: Effects of Spatial Resolution and Machine Learning-based SelectionThe delineation of Management Zones (MZs) is a precision agriculture strategy that exploits the spatial variability of crop fields to support more efficient and sustainable site-specific management practices. Traditionally, yield maps have been used as one of the main information layers in this process, as they integrate the effects of soil, climate, and management throughout the crop cycle. However, obtaining reliable yield maps still presents limitations, such as the need for onboard sensor... M. Gelain, J.P. Molin, L. De Goes Sterle |
7. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar BeetsThe global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-B... A. Tabbassi, S. Henkler, A. Zakhary, K. Rother |
8. PRAGMATIC - Innovative IT Platform for Yield and Cost Prediction of Agricultural ProductionThe aim of the R&D was to develop a prototype of an innovative IT platform containing algorithms for predicting yields and production costs of agricultural commodities for three reference crops, i.e.: blueberries, apples and potatoes in the supply chain from the field to the production line. The system are ... T. Wojciechowski, G. Niedbała, K. Bobran |
9. Automatic Creation of Thematic Maps and Management Zones Using Agdatabox-fast TrackPrecision agriculture encompasses the strategic application of inputs in requisite quantities at optimal times to enhance overall productivity. An essential aspect of this methodology is the formulation of thematic maps (TMs) and management zones (MZs). Despite their critical importance, delineating TMs and MZs requires substantial technical expertise in their construction, making their application challenging, particularly for smaller producers, due to the need for a specialized multidiscipl... J. Aikes Junior, E. Souza, C.L. Bazzi, R. Sobjak, E. Souza |
10. Spatio-temporal Yield Stability in Rice-soy Rotations at Farm ScaleIntegrated crop-livestock systems are facing the pressure to intensify worldwide, thus decoupling crops from pasture and reducing the amount of time under pasture, while increasing the frequency of annual grain crops. In Uruguay, rice production is commonly integrated into crop–livestock systems, generating productive and environmental advantages compared to many rice-growing regions worldwide. Recent intensification of these systems, particularly through the incorporation of soybean in... I. Macedo, &. Roel, J.J. Bonomo |
11. Standardisation Challenges in Precision Agriculture: Mapping the Landscape and Advancing Semantic InteroperabilityBackground: Precision agriculture increasingly depends on digital technologies and the exchange of data between equipment, sensors, platforms and decision support tools. A wide range of standards is available, including machine data formats such as ISOXML and semantic resources such as AGROVOC and rmAgro. Despite this variety, the overall standardisation landscape remains fragmented. Even within single countries, differences in code lists, vocabularies and data publishin... J. Tummers, F. Sijbrandij, T. Ten Den, A. Gupta, T. Bresilla, B. Veldhuisen |
12. Towards Trusted Satellite Data for Precision Farming: Mitigating Spoofing and Improving Data Integrity Using Galileo OSNMA and HAS and Copernicus Traceability ServiceBackground: Precision agriculture increasingly relies on GNSS positioning not only to execute field operations with high spatial accuracy, but also to provide trustworthy data for documentation, certification, and regulatory compliance. However, GNSS signals remain vulnerable to degradation, jamming, and especially spoofing—an intentional manipulation of satellite signals causing machinery to believe it is in a different position. Such incidents have already been observ... B. Veldhuisen, T. Bresilla, J. Tummers, F. Sijbrandij, T. Ten Den, A. Gupta, T. Van Der Wal |
13. Influence of Meteorological Variables on Bean Yield in the Semi-Arid Region: A Data-Driven Approach for Agricultural Decision SupportCommon bean is a strategic crop for the Brazilian semi-arid region, predominantly cultivated under rainfed systems that are highly dependent on climate variability. In regions characterized by irregular rainfall patterns, high temperatures, and extreme weather events, incorporating temporal analyses based on meteorological data becomes essential for evidence-based agricultural planning. Within the context of precision agriculture, the integration of historical climate series and productivity ... A. Fonseca, J.F. Dos Anjos, E.F. Da Silva, G.B. Moura, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, A.C. Bezerra |
14. Development of an IoT Platform for Soil and Climate Monitoring in Irrigated Fruit Production in the Semi-Arid Region of PernambucoIrrigated fruit production in the São Francisco hinterland, led by the Petrolina production hub, reached US$ 294 million in exports in 2023, consolidating mango and grape crops as strategic pillars of Pernambuco’s economy and of the Brazilian semi-arid region. This production system is dependent on irrigation due to irregular rainfall distribution, high evaporative demand, and recurrent drought conditions. Despite its international competitiveness and technological advances in ir... A. Fonseca, E.D. Figueirôa , G.P. Coelho, J.P. De Andrade, E. Lopes, A.D. Ribeiro |
15. Comparing Traditional Methods and Digital Platforms for Delineating Management Zones: A Study of Efficiency and AccuracyDigital platforms have emerged as user-friendly tools to support management zone delineation and field monitoring in precision agriculture. However, the algorithms and methods embedded in these platforms may overlook agronomic and operational constraints, limiting their effectiveness in decision-making. This study evaluated the performance of three commercial digital platforms for management zone delineation and compared them with a reference protocol and an... T. Costa Barboza, H. Oldoni, F.D. Inácio, L.R. Amaral, A. Felipe Dos Santos |
16. From Raw Yield Data to Cell-based Risk Management: a Next-generation Framework for Ultra-high Resolution Yield Stability and Yield Gap Analysis in Hungarian Arable EnvironmentA fundamental challenge in modern precision agriculture is ensuring access to raw yield data, its systematic processing, and the subsequent planning of foundational and Variable-Rate Application (VRA) maps required for decision support. This research aimed to develop a methodology suitable for the cell-level multitemporal processing of yield datasets to establish production-risk classes and quantify unrealized potential via yield gap analysis. This framework exceeds conventional precision pla... D. Szám |
17. Mobile Edge AI for Detection of Grape Clusters and Disease Symptoms in VineyardsPrecision viticulture demands accessible technological solutions that enable rapid disease diagnosis and production monitoring directly in the field. In real-world production contexts, dependence on cloud connectivity, external servers, or specialized hardware limits the adoption of computer vision tools by small and medium-sized farmers. In this context, this work presents a solution based on artificial intelligence embedded in a mobile application for the detection of grape bunches and leav... E.M. Da Silveira, F.I. Nogueira, S.D. Camargo, A. Freire Campos, J. Valiati, E.F. Leite |
18. Web Application Based on CNN for Classification of Biotic and Abiotic Stresses in Coffee LeavesThe use of digital systems can assist coffee growers and professionals in diagnosing stresses that affect coffee plantations, ensuring that crop management is carried out correctly and efficiently. Therefore, the aim of this study was to develop a web application based on a pre-trained Convolutional Neural Network to classify coffee leaf images exhibiting symptoms of biotic and abiotic stresses. Initially, a dataset consisting of coffee leaf images affected by biotic and abiotic stresses was ... D.H. Leite, D.S. Valente, P.M. Arruda, F.D. Tancredi, D. Queiroz, G. Dumbá Monteiro De Castro |
19. RAVI: A QGIS plugin for satellite remote sensing applications of Vegetation Indices and SAR data in Precision AgricultureRemote Sensing (RS) plays a fundamental role in Precision Agriculture (PA), particularly through the use of satellite imagery to identify spatial variability within the fields. Compared to traditional methods for detecting field variability, such as soil sampling, yield mapping, and proximal sensors, RS offers advantages in reduced operational costs, lower labor demands, and greater spatial coverage. Analyzing vegetation indices (VIs) over time allows to track crop phenological development, i... |
20. SPARC-AI: Synthetic Procedural Agricultural Rendering and Annotation Framework for Crop Phenotyping and AI ApplicationsBetween 20% and 40% of global agricultural production is lost annually to pests and diseases, generating economic damages estimated at over US$220 billion each year. This persistent challenge underscores the urgent need for scalable, precise, and cost-effective monitoring solutions. In this scenario, Artificial Intelligence (AI) based pathogen detection systems emerge as transformative tools, enabling high-resolution spatial and temporal monitoring of crop health. However, the perfo... R. Freitas, V.S. Mello, G.D. Dallegrave, E. Farinati Leite, J.F. Valiati |
21. An Online Decision Support Tool for Homogeneous Zone Delineation in Precision AgricultureManagement zone delineation is a key component of site-specific management in precision agriculture, enabling the spatial optimization of inputs and an improved understanding of within-field variability. Traditionally, homogeneous zones have been derived from historical yield maps or soil-related variables obtained through proximal sensing. More recently, the increasing availability of multispectral satellite imagery and derived vegetation indices has expanded the range of data sources availa... |
22. Mapping Digital Technologies, Cloud Platforms, and Artificial Intelligence in Precision Agriculture: The Software Baseline for a Citrus and Sugarcane Living Lab.The digital transformation of Precision Agriculture (PA) has been driven by the growing availability of Farm Management Information Systems (FMIS), cloud platforms, and Artificial Intelligence (AI) solutions. This study, linked to the Smart B100 Science for Development Center (CCD-SB100), funded by FAPESP and led by the Agronomic Institute of Campinas (IAC), Faac/Unesp (Bauru), in partnership with FATEC Pompeia, aimed to build a multicriteria matrix (technological inventory) of digital PA sol... M. Mazega, H. Fortinis, H. Fischer, C.K. Luvizotto, C.E. Otoboni, M.C. De Almeida |
23. AgGeoSampler: A Geospatial Open-Source Data Acquisition and Sampling Design Dashboard for Agricultural ApplicationsModern agricultural and environmental research increasingly depends on high-resolution geospatial data to support precise, site-specific decision-making. Advances in satellite remote sensing, unmanned aerial systems, and digital soil mapping have generated vast spatial datasets that capture fine-scale variability in vegetation health, soil properties, and terrain attributes. However, translating this wealth of information into effective field-sampling... A. Bhattarai, A. Jakhar, K. Poudel, A. Dhaliwal, L.M. Bastos |
24. Challenges in Integrating Digital Agriculture SolutionsAdvances in digital agriculture have increased the supply of solutions to improve the management of agricultural activity. However, the increasing number of solutions in quantity and variety also imposes barriers to their adoption by small and medium-sized family farmers reasoned by higher exposition to technical and financial limitations. High cost, low digital literacy, and little perception of the usefulness are some of the obstacles. These can be further exacerbated if producers need ... J. Da Silva, S.R. Evangelista, J.G. Barbedo, L.A. Romani |
25. Transforming Agronomic Tables into Continuous Sufficiency and Fertilizer-rate Functions for Digital Recommendation SystemsSoil-test interpretation tables and fertilizer recommendation tables are widely used in agronomic practice, but they typically classify results into discrete categories (e.g., very low, low, medium, and high). While this format is suitable for manual consultation, it introduces artificial “jumps” between classes and limits automation when implementing diagnostic and recommendation rules in computerized systems. In this study, we developed a two-step methodology to convert these ta... D. Fernandes Paiva, G.M. Chaer |
26. Spatial Delineation of Site-Specific Management Units Using Vegetation Indices in Precision AgriculturePrecision Agriculture has incorporated Remote Sensing as an essential tool for characterizing the spatial variability of agricultural crops. Among the available spectral indices, vegetation indices stand out for their ability to represent vegetative vigor and spatial patterns associated with crop performance. This study aimed to evaluate the spatial stability of spectral indices obtained from a median composite for management zone delineation and to analyze their agreement with a yield map in... L.G. Kern, L. Silveira Pavão, . Müllich, I. Maldaner, L. , J. Sgarbossa, G. Kaefer Seganfredo, E. Rolim Farias Da Silva, M. Silveira Farias |
27. Spatial Distribution of Coffee Leaf Miner Infestation and Its Impact on Coffee Fruit Maturation, Yield, and Beverage QualityDifferences in the maturation rate of coffee fruits can be associated with plant stress. The incidence of pests, such as the coffee leaf miner (Leucoptera coffeella), compromises the photosynthetically active area, which can reduce yield and beverage quality. Computer vision can assist in damage reduction by identifying the pest's spatial and temporal behavior. This study aimed to verify, spatially and temporally, the impact of damage caused by the coffee leaf miner on ... L.V. Lazzarini, A. , G.P. Cândido, V.M. Nunes, S.M. Hurtado, F.H. Leandro, I.D. Gonçalves |