Proceedings
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| Filter results7 paper(s) found. |
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1. 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 |
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. Effect of Terrain Slope Obtained by LiDAR on Operational Performance in Semi-mechanized Coffee Transplanting with Autopilot.The application of precision agriculture techniques has become an important tool in surveying coffee plantations, allowing for the rapid assessment of slope profiles in these areas and indicating the possibility of mechanizing the plots. Therefore, there is a need to work with quality in semi-mechanized transplanting operations using autopilot to optimize future processes related to coffee cultivation. The objective of this study was to determine the efficiency of use and mechanical availability... R. , G. , R.D. Faria, L.S. Silva, M.D. Oliveira, E.H. Zavala |
4. 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 hyperspectral... A. Palma diniz baker, G. , M.D. Oliveira, E.H. Zavala, R. , M.M. Amaral, A. |
5. Development of a System for Intelligent Plant Monitoring and CultivationCultivation in protected environments and indoor systems requires continuous monitoring. Labor shortages and delays in management decisions compromise productivity, uniformity, and efficiency. Assessments of plant stand, vegetative vigor, nutritional status, and the incidence of pests and diseases still rely on visual inspections conducted over limited periods, reducing diagnostic accuracy and response time. Although automation technologies are advancing in horticultural production, available... R. Avelar, F.O. Dos reis silva, A.L. Carvalho, F.M. Alves soares, C.C. Santana |
6. Development and Validation of a Low-Cost IoT-Based Weather Station Using LoRa Communication for Precision AgricultureAccess to accurate local meteorological data remains a critical bottleneck for precision agriculture adoption among small and medium-scale Brazilian farmers. Commercial weather stations cost between R$ 15,000 and R$ 50,000, while public networks such as INMET operate with average inter-station spacing of 30–50 km, insufficient to capture the microclimate variability that drives field-scale irrigation and crop management decisions. This study presents the development, field validation, and... A.L. Carvalho, C.C. Santana, R. Avelar, F.D. Silva, F. Soares |
7. 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 monitor... F. Soares, C.C. Santana, R. Avelar, F.O. Silva, A.L. Carvalho |