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| Filter results7 paper(s) found. |
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1. 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 agricultural... E.A. Speranza, C.R. Grego, T. Santos, G.C. Rodrigues, R.Y. Inamasu |
2. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut HarvestingThe integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut... |
3. Characterizing Cross-Crop Stink Bug Spectral Signatures from Hyperspectral DataEffective crop protection in agricultural production systems requires the ability to detect pest-induced stress in a timely and reliable manner. In large-scale farming systems, stink bugs attack multiple crop species, making cross-crop pest detection a critical capability for scalable monitoring solutions. Rather than developing crop-specific models that require retraining for each species, identifying crop-independent spectral signatures of stink bug infestation enables transferable detection... A.O. Françani, L. Zhao, J. Ferreira , J. Yan, E.J. Ferreira, L.A. Jorge |
4. Herbicide Savings and Weed Control Performance Using Green-on-Green Spot Spraying in SoybeanThe conventional approach to weed control in large-scale soybean production relies on full-area herbicide spraying, resulting in high chemical input and operational costs. In this context, artificial intelligence-based spot spraying has emerged as a promising alternative to increase efficiency and reduce environmental impact. This study evaluated the performance of a green-on-green spot spraying system based on deep learning algorithms, CORTEX AI (Soybean Model v08), for post-emergence weed control... D. Gabriel, A.S. Fiegenbaum, S.R. Griebeler, M. Franchi, N.S. Dos Santos, K.F. Rocha, A.L. Vian |
5. 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 agricultural... T. Costa Barboza, W. Batista Da Silva, S. Guimarães Moreira, S. Godinho Silva, L. Lacerda, A. Felipe Dos Santos |
6. UAV-Based Multispectral Modelling of Biomass and Crude Protein Yield for Green Biorefinery ApplicationsIn animal production systems, protein demand is steadily increasing due to global population growth. This rising demand has highlighted the need to identify alternative and sustainable protein sources. Green biorefinery systems can efficiently extract protein from plant biomass. Previous studies confirmed that perennial grass crops such as Perennial Ryegrass, Festulolium, and Tall Fescue can produce high-quality biomass suitable for protein extraction. An estimation model of biomass yield and... M. Canciani, E. Han, U. Jørgensen, Y. Ikeda, N.P. Hansen, S.K. Jensen, M.R. Weisbjerg, T. Didion |
7. 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 farming. Considering... H.F. Gebler, C.R. Grego, G.C. Rodrigues, A. Pereira, F. Fagundes |