Proceedings
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| Filter results23 paper(s) found. |
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1. Response Of Rhodes Grass (Chloris Gayana Kunth) To Variable Rate Application Of Irrigation Water And Fertilizer NitrogenRhodes grass is cultivated extensively in Saudi Arabia under center pivot sprinkler irrigation system. The research work was carried out to optimize irrigation water and fertilizer nitrogen levels for the crop. The objectives of the study were: 1. To delineate the field in to management zones, 2. To study the effects of variable rate application (VRA) of irrigation water and fertilizer nitrogen on the yield of Rhodes grass. A field experiment was carried out from... V. Patil, R. Madugundu, E. Tola, S. Marey, D.J. Mulla, S.K. Upadhyaya, K.A. Al-gaadi |
2. Site Specific Costs Concerning Machine Path OrientationComputer algorithms have been created to simulate in advance the orientation/pattern of a machine operation on a field. Undesired impacts were obtained and quantified for these simulations, like: maneuvering and overlap of inputs in headlands; servicing of secondary units; and soil loss by water erosion. While the efforts could minimize the overall costs, they disregard the fact that these costs aren’t uniformly distributed over irregular fields. The cost of a non-productive machine process... M. Spekken, J.P. Molin, T.L. Romanelli, M.N. Ferraz |
3. NIR Spectroscopy to Map Quality Parameters of SugarcanePrecision Agriculture aims to explore the potential of each crop considering the differences within the field. One information that is considered the most important is the yield or the obtained income in the field. However, in the case of sugarcane, quality will also directly influence farmer’s income. Several studies suggest harvester automation aiming to monitor yield, but few consider the quality analysis in the process. Among the existing methods for measuring sugar content the one that... M.N. Ferraz, J.P. Molin |
4. Positioning Strategy of Maize Hybrids Adjusting Plant Population by Management ZonesChoice of hybrid and accurate amount of plants per area determines grain yield and consequently net incomes. Local field adjustment in plant population is a strategy to manage spatial variability and optimize environmental resources that are not under farmer control (like soil type and water availability). This study aims to evaluate the response of hybrids by levels of plant population across management zones (MZ). Six different hybrids and five rates of plant populations were analyzed starting... A.A. Anselmi, J.P. Molin, M.T. Eitelwein, R. Trevisan, A. Colaço |
5. Processing Yield Data from Two or More CombinesErroneous data affect the quality of yield map. Data from combines working close to each other may differ widely if one of the monitors is not properly calibrated and this difference has to be adjusted before generating the map. The objective of this work was to develop a method to correct the yield data when running two or more combines in which at least one has the monitor not properly calibrated. The passes of each combine were initially identified and three methods to correct yield data were... L. Maldaner, J.P. Molin, T.F. Canata |
6. Spatial Variability of Canopy Volume in a Commercial Citrus GroveLiDAR (light detection and ranging) sensors have shown good potential to estimate canopy volume and guide variable rate applications in different fruit crops. Oranges are a major crop in Brazil; however the spatial variability of geometrical parameters remains still unknown in large commercial groves, as well as the potential benefit of sensor guided variable rate applications. Thus, the objective of this work was to characterize the spatial variability of the canopy volume in a commercial orange... A.F. Colaço, J.P. Molin, R.G. Trevisan, J.R. Rosell-polo, A. Escolà |
7. Use of Crop Canopy Reflectance Sensor in Management of Nitrogen Fertilization in Sugarcane in BrazilGiven the difficulty to determine N status in soil testing and lack of crop parameters to recommend N for sugarcane in Brazil raise the necessity of identify new methods to find crop requirement to improve the N use efficiency. Crop canopy sensor, such as those used to measure indirectly chlorophyll content as N status indicator, can be used to monitor crop nutritional demand. The objective of this experiment was to assess the nutritional status of the sugarcane fertilized with different nitrogen... S.G. Castro, G.M. Sanches, G.M. Cardoso, A.E. Silva, H.C. Franco, P.S. Magalhães |
8. On-the-go Measurements of pH in Tropical SoilThe objective of this study was to assess the performance of a mobile sensor platform with ion-selective antimony electrodes (ISE) to determine pH on-the-go in a Brazilian tropical soil. The field experiments were carried out in a Cambisol in Piracicaba-SP, Brazil. To create pH variability, increasing doses (0, 1, 3, 5, 7 and 9 Mg ha-1) of lime were added on the experimental plots (25 x 10 m) one year before the data acquisitions. To estimate soil pH levels we used a Mobile Sensor Platform... M.T. Eitelwein, R.G. Trevisan, A.F. Colaço, M.R. Vargas, J.P. Molin |
9. Akkerweb: A Platform for Precision Farming Data, Science, and PracticeThe concept of precision farming (PF) was formulated about 40 years ago and the scientific knowledge for some applications of PF in The Netherlands has been available for almost 20 years. Also, in many cases equipment is available to implement PF in practice. In spite of all this PF uptake is still limited. An important reason for the limited uptake of PF is in the challenges that must be overcome to let data flow from sensors to data storage, to combine data sources and process them into recommendations,... F.K. Van Evert, T. Been, J.A. Booij, C. Kempenaar, G.J. Kessel, L.P. Molendijk |
10. Canopy Parameters in Coffee Orchards Obtained by a Mobile Terrestrial Laser ScannerThe application of mobile terrestrial laser scanner (MTLS) has been studied for different tree crops such as citrus, apple, olive, pears and others. Such sensing system is capable of accurately estimating relevant canopy parameters such as volume and can be used for site-specific applications and for high throughput plant phenotyping. Coffee is an important tree crop for Brazil and could benefit from MTLS applications. Therefore, the purpose of this research was to define a field protocol for... F. Hoffmann Silva Karp, A. Feritas Colaço, R. Gonçalves Trevisan, J.P. Molin |
11. UAV Images As a Source for Retrieval of Machine Tracks and Vegetation Gaps Along Crop RowsThe trend of acquiring equipment and obtaining high resolution remote sensed images by Unmanned Aerial Vehicles (UAV) have been followed by sugarcane producers in Brazil, given its low cost. The images taken from fields have been used for retrieval of information like Digital Terrain Models (DTMs) from stereoscopy of overlapping images and spatial variance of biomass. In sugarcane production, driving deviations occur during planting because of manual steering inaccuracy, sliding of machines sideways... M. Spekken, J.P. Molin |
12. Predicting Secondary Soil Fertility Attributes Using XRF Sensor with Reduced Scanning Time in Samples with Different Moisture ContentTo support future in situ/on-the-go applications using X-ray fluorescence (XRF) sensors for soil mapping, this study aimed at evaluating the XRF performance for predicting organic matter (OM), base saturation (V), and exchangeable (ex-) Mg, using a reduced analysis time (e.g., 4 s) in soil samples with different moisture contents. These attributes are considered secondary for XRF prediction because they do not present emission lines in the XRF spectrum. Ninety-nine soil samples... T.R. Tavares, J.P. Molin, T.R. Da Silva , H.W. De Carvalho |
13. 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 |
14. Stability-driven Framework for Robust Plant Spectral Signature IdentificationAccurate identification of agricultural crops based on spectral signatures remains a critical challenge for large-scale phytosanitary monitoring. This study proposes a stability-based structure for the robust identification of plant spectral signatures, applied to the discrimination of soybean (Glycine max) from maize (Zea mays) and cotton (Gossypium hirsutum) under biotic stress caused by the pest Spodoptera frugiperda and stink bugs. The proposed method follows a flow of proposed steps that... J. Ferreira , A.O. Françani, E. . Ferreira, L.A. Jorge, J.C. Felipe, L. Zhao |
15. A Hybrid Non-Destructive Approach Combining Image Processing and Spectral Feature Selection for Grapevine Leaf Water Content EstimationReliable and continuous estimation of leaf water content (LWC) is essential for viticulture, as it enables assessment of spatiotemporal variability in vine water demand and supports improved irrigation management efficiency within Precision Agriculture (PA) practices. Although the gravimetric method based on fresh weight (FW) and dry weight (DW) measurements provides accurate LWC estimates, it is time-consuming, destructive, and exhibits limited scalability for large sample sizes. In contrast,... L.H. Bassoi, B.S. Costa, E.J. Ferreira, H. Oldoni, L.A. Jorge |
16. Proximal and suborbital vegetation indices in yield prediction of ‘Syrah’ grapevinesThe various vegetation indices (VIs) reported in the literature, derived from different wavelengths, necessitate identifying the most suitable spectral combinations to represent agronomic variables in precision viticulture. This study evaluated the performance of proximal and suborbital VIs to explain the spatial variability of yield of the ‘Syrah’ grapevine. The study was conducted in a trellised vineyard under double pruning management in Ribeirão Preto, state of São... L.H. Bassoi, L.A. Jorge, A. Pereira, I. Oliveira Junior |
17. Consistency of Three Vegetation Indices from Suborbital and Proximal Sensing in Precision ViticultureThe integration of proximal and suborbital sensing platforms can expand the practice of precision viticulture. However, the consistency of vegetation indices (VIs) derived from different sensors remains a critical issue. This study quantified the agreement between VIs obtained by proximal and suborbital sensing using complementary metrics of association, error, and agreement. The research was conducted in a ‘Syrah’ vineyard in Ribeirão Preto, state of São Paulo, Brazil,... L.H. Bassoi, L.A. Jorge, A. Pereira, I. Oliveira Junior |
18. Early Detection of Soybean Pest Infestations Using Leaf-Level Reflectance Spectroradiometry and Machine LearningThe agricultural sector plays a central role in sustaining global food production, energy supply, and economic development. However, population growth, climate change, resource scarcity, and increasing sustainability demands have intensified production challenges. Pest and disease outbreaks are major contributors to crop losses worldwide, underscoring the urgent need for reliable methods capable of enabling early detection and timely intervention. In this context, leaf-level spectroradiometry... M. Lima, J.C. Felipe, E.J. Ferreira, L.A. Jorge, L. Zhao |
19. Enhancing Pest Detection Through Spectral Signature Extraction in Hyperspectral DataDetecting insect infestation is essential for effective crop protection, particularly in large-scale systems. Caterpillars and stink bugs induce physiological and structural alterations in plant tissues that can be captured through hyperspectral reflectance sensing, which is a non-destructive technique that measures plant responses across hundreds of wavelengths. However, raw spectral signatures are characterized by high dimensionality, strong inter-band correlation, and they often exhibit baseline... A.O. Françani, J. Ferreira , L. Zhao, L.A. Jorge, K.M. De Oliveira, J.C. Felipe |
20. Weed mapping: advantages of RGB CNN-based approaches vs multispectral pixel-based methodsWeed detection remains a major challenge in modern agriculture, and accurate weed mapping is crucial to support rapid and efficient management interventions, ensuring crop productivity and economic viability. In this context, geotechnologies such as remote sensing and computer vision, together with the widespread adoption of drones, enable the acquisition of ultra–high spatial resolution imagery, allowing more detailed analyses in complex agricultural environments. Although multispectral... |
21. Integration of LiDAR-Derived TWI and UAV Multispectral Data for Waterlogging Susceptibility Mapping in Precision AgricultureTopography directly controls water redistribution across the landscape, shaping the spatial variability of soil moisture in agricultural areas. The Topographic Wetness Index (TWI), derived from digital elevation models, is widely used to estimate the potential for water accumulation; however, its field-scale validation supported by high-resolution multispectral drone imagery remains limited. In agricultural systems, recurrent waterlogging can reduce productivity by impairing germination, promoting... |
22. Comparative Evaluation of Ground Point Classifiers in LiDAR Point Clouds for DEM Generation in Pasture AreasThe classification of ground points in LiDAR point clouds is an essential step for generating reliable Digital Terrain Models (DTMs), particularly in livestock production systems based on pastures. Despite methodological advances in forested and urban environments, studies specifically addressing ground classification in pasture areas remain limited, where the proximity between the forage canopy and the ground surface makes altimetric distinction between classes challenging. The heterogeneous... |
23. YOLOv10x-based deep learning for automated detection and counting of seeds per soybean podAccurate quantification of the number of seeds per soybean pod is a fundamental step for reliable yield estimation. However, this measurement still relies on manual procedures, which are subject to observational variability and limited scalability. In the context of digital agriculture, deep learning–based techniques have shown promise for automating the detection and counting of reproductive structures. Nevertheless, there is still limited application of models specifically aimed... |