Proceedings
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| Filter results10 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. Estimation of Leaf Chlorophyll Index in Corn Using Smartphone Images and Machine LearningAccurate estimation of the Leaf Chlorophyll Index (LCI) in corn is fundamental for nitrogen management in precision agriculture, as nitrogen availability directly affects chlorophyll production and photosynthetic capacity. Conventional field assessment techniques are time-consuming and labor-intensive, while smartphone use provides a practical and low-cost alternative for obtaining high-resolution data in near-real-time. The objective of this study was to develop and validate a non-destructive... C.C. Santana, S.M. Mintesinot, D. Queiroz, F.S. Santos, A.L. Coelho |
3. 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 |
4. 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 |
5. 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 |
6. 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 applied... L.C. E. Ribeiro, M. Antônio, A. Batista Santos, E. Sousa Meneses, C.C. Santana |
7. 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 |
8. 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 evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana |
9. 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 crop... E. Sousa Meneses, M. Antônio, A. Batista Santos, L.C. E. Ribeiro, C.C. Santana |
10. 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 significantly... A. Batista Santos, E. Sousa Meneses, L.C. E. Ribeiro, M. Antônio, C.C. Santana |