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| Filter results16 paper(s) found. |
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1. Precision Irrigation and Beyond: A Multi-Step Zoning Approach for Vineyard Water Management and Wine Quality EnhancementSpatial variability in soil moisture, terrain, and vine physiology presents a major challenge for efficient water management in vineyards. Traditional uniform irrigation often fails to address this heterogeneity, limiting both water use efficiency and wine quality. This study investigated spatial and temporal variations in vine water stress over five consecutive growing seasons, and evaluated zone-specific precision agriculture strategies to support improved irrigation and complementary vineyard... Y. Cohen, I. Bahat, J.M. Grünzweig, V. Alchanatis , O. Keisar, G. Lidor, E. Goldshtein, Y. Netzer |
2. High-Sensitivity Flexible LIG/GO Humidity Sensors for Continuous Environmental Monitoring in Agricultural ApplicationsThe efficiency of agricultural production depends substantially on the continuous monitoring of environmental variables, particularly humidity, which directly influences plant physiological processes, the physicochemical properties of soil, and the preservation of plant materials in the post-harvest stage. This study presents the development of a capacitive humidity sensor based on laser-induced graphene (LIG) and graphene oxide (GO), characterized by its flexibility and adaptability to different... A. La Rosa, P. Silveira, B.B. Gallo, B.V. Lopes, L.M. Goncalves, N. Carreño |
3. 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 effectiveness... P.M. De Sousa, B.C. De Albuquerque, V.A. Galvan, L.D. Corrêdo, L.D. Pimentel, J. Souza |
4. Deep Learning Models Applied to Drone Imagery for Counting, Biometry, and Carbon Stock Estimation in Large-scale Macaw Palm (Acrocomia Aculeata) PlantationsMacaw palm is a native Brazilian species with significant productive potential, emerging as a premier candidate for the sustainable replacement of oil palm and as a strategic feedstock for sustainable aviation fuel (SAF) and carbon credit markets. However, as the crop is still in the process of domestication and commercial expansion, there is an urgent need for efficient monitoring technologies that enable the identification of superior individuals and the rigorous auditing of carbon stocks across... P.M. De Sousa, V.A. Galvan, J. Souza, R. . De Oliveira , L.D. Corrêdo, L.D. Pimentel, B.C. Albuquerque |
5. Towards Precision Agriculture with Electrochemical Sensors for Detecting Dopamine in Plants and FruitsCathecolamines are essential neurotransmitters that regulate the central nervous system of animals, while also acting as direct modulators in plants, coordinating antioxidant responses and ionic balance regulation. Dopamine (DA) is an essential catecholamine not only for animals but also plays a critical regulatory role in plants, acting as a potent antioxidant and growth modulator under abiotic stresses such as drought and salinity or pathogen attacks and helps neutralize free radicals and... L.M. Gonçalves, B. Vasconcellos Lopes, B.B. Gallo, A. La Rosa, D.A. Fruchtenicht, C. Miler, P. Silveira, N.L. Carreno |
6. 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 highlights... C. Miler, L. Gonçalves, B. Vasconcellos Lopes, B.B. Gallo, A. La Rosa, D. Fruchtenicht, P. Silveira, F. , N. Carreño, L. Machado |
7. 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 publishing... J. Tummers, F. Sijbrandij, T. Ten Den, A. Gupta, T. Bresilla, B. Veldhuisen |
8. 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 observed... B. Veldhuisen, T. Bresilla, J. Tummers, F. Sijbrandij, T. Ten Den, A. Gupta, T. Van Der Wal |
9. A Methodological Framework for Modeling Plant Virus Occurrence Using Biometeorological Data: Insights from Multi-crop Case Studies in ArgentinaViral diseases represent a major threat to the productive stability of agricultural systems. Their spatial and temporal occurrence is influenced by environmental conditions that regulate interactions among viruses, vectors, and hosts, making disease anticipation difficult using statistical traditional approaches. This situation highlights the need to understand the dynamics of the different biological components capable of affecting agricultural systems, and design and apply tools that facilitate... F. Suarez, B. Gómez Montenegro, C. Dottori, V. Alemandri, S. De Breuil, C. Bruno, F. García Seleme |
10. Satellite-IoT Integration for Digital Agriculture: Advances and ChallengesAgriculture is an essential pillar of global food security, demanding continuous digital integration from seed selection and planting to post-harvest logistics and crop distribution. In this scenario, seamless communication is the primary enabler for data-driven decision-making, ensuring that critical information regarding environmental variables, soil moisture, and direct plant health indicators reaches processing platforms fast enough for agile operational responses. However, the expansion of... M. Feldman, O.R. Da Cruz, N.K. Wagner, C. Glier, I. Müller |
11. Smart Resource Use in Precision Agriculture: A Conceptual Framework for Digital Sustainability DevelopmentDigital and precision agriculture are expected to play a central role in shaping future sustainable food production systems by enabling more intelligent, adaptive, and resource-efficient practices. This study adopted a forward-looking literature review, composed of bibliometric and systematic aspects, to propose Smart Resource Use as an emerging paradigm for sustainable development in digitally enabled precision agriculture, extending the concept of smart consumption beyond its traditional origins... B.B. Gallo, L. Visintainer Lerman, R.F. Da Silva, E. Bolfe, M.M. Da Silva, T. Alves, C.E. Pereira, T.M. Porcino |
12. 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 conditions.... 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 |
13. 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-destructive... 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 |
14. Evaluation of Lettuce Image Classification with CNNs under Different NPK Nutritional ConditionsThe growing global demand for food has driven the development of technologies aimed at increasing productive efficiency in sustainable agricultural systems, such as hydroponics. In this context, proper monitoring of nutrient solutions is essential, particularly for the early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, which directly affect lettuce growth, yield, and quality. Traditional nutritional diagnostic methods often rely on destructive laboratory analyses,... E.L. Silva, E. Freitas, V.C. Secundino, D.G. Gomes |
15. Detection of Banana Bunches and Peduncles in the Prata Catarina Cultivar Using Faster R-CNN With Transfer LearningBanana is one of the most produced and consumed fruits worldwide, being strategic for precision agriculture, especially in applications aimed at intelligent management and automated harvesting. Its economic and social relevance in tropical countries reinforces the need for technological solutions that increase productive efficiency and reduce losses in the field. In this context, the automatic detection of bunches and stalks in a natural environment represents a relevant challenge due to occlusion,... Y. Costa G. Da Silva, E. Freitas, P.S. Costa, D.V. Beserra, D.G. Gomes |
16. Development and Field Validation of SMART-C: A Geostatistics and PCA-Based Decision Framework for Site-Specific Cocoa Management in the Brazilian AmazonCocoa production plays a major socioeconomic role in Pará State, Brazil’s largest producing region, with annual output exceeding 140 thousand tons. Although Brazil ranks among the world’s leading cocoa producers, most production systems are still managed using field-average approaches that disregard within-field spatial variability of soil attributes and crop performance. This limitation restricts input efficiency and long-term system sustainability in perennial tropical systems.This... |